fix: Add proper null checks for logger calls and improve type safety in LLM class

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

.gitignore

@@ -21,4 +21,3 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log

docs/concepts/agents.mdx

@@ -101,8 +101,6 @@ from crewai_tools import SerperDevTool
 class LatestAiDevelopmentCrew():
   """LatestAiDevelopment crew"""
 
-  agents_config = "config/agents.yaml"
-
   @agent
   def researcher(self) -> Agent:
     return Agent(

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.
 
@@ -190,7 +161,6 @@ The CLI will initially prompt for API keys for the following services:
 * Groq
 * Anthropic
 * Google Gemini
-* SambaNova
 
 When you select a provider, the CLI will prompt you to enter your API key.
 

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.
 
@@ -152,7 +138,7 @@ print("---- Final Output ----")
 print(final_output)
 ````
 
-```text Output
+``` text Output
 ---- Final Output ----
 Second method received: Output from first_method
 ````
@@ -221,17 +207,14 @@ 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.message = "Hello from structured flow"
         self.state.counter = 0
 
@@ -248,12 +231,10 @@ class UnstructuredExampleFlow(Flow):
         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

@@ -4,6 +4,8 @@ description: What is knowledge in CrewAI and how to use it.
 icon: book
 ---
 
+# Using Knowledge in CrewAI
+
 ## What is Knowledge?
 
 Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
@@ -34,20 +36,7 @@ CrewAI supports various types of knowledge sources out of the box:
   </Card>
 </CardGroup>
 
-## Supported Knowledge Parameters
-
-| Parameter                    | Type                                | Required | Description                                                                                                                                           |
-| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
-| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
-| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
-
-## Quickstart Example
-
-<Tip>
-For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project. 
-Also, use relative paths from the `knowledge` directory when creating the source.
-</Tip>
+## Quick Start
 
 Here's an example using string-based knowledge:
 
@@ -91,14 +80,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 ```
 
 
-Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more. 
-
-<Note>
-  You need to install `docling` for the following example to work: `uv add docling`
-</Note>
-
-
-
+Here's another example with the `CrewDoclingSource`
 ```python Code
 from crewai import LLM, Agent, Crew, Process, Task
 from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
@@ -146,218 +128,39 @@ result = crew.kickoff(
 )
 ```
 
-## More Examples
-
-Here are examples of how to use different types of knowledge sources:
-
-### Text File Knowledge Source
-```python
-from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
-
-# Create a text file knowledge source
-text_source = CrewDoclingSource(
-    file_paths=["document.txt", "another.txt"]
-)
-
-# Create crew with text file source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[text_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[text_source]
-)
-```
-
-### PDF Knowledge Source
-```python
-from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
-
-# Create a PDF knowledge source
-pdf_source = PDFKnowledgeSource(
-    file_paths=["document.pdf", "another.pdf"]
-)
-
-# Create crew with PDF knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[pdf_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[pdf_source]
-)
-```
-
-### CSV Knowledge Source
-```python
-from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
-
-# Create a CSV knowledge source
-csv_source = CSVKnowledgeSource(
-    file_paths=["data.csv"]
-)
-
-# Create crew with CSV knowledge source or on agent level
-agent = Agent(
-    ...
-    knowledge_sources=[csv_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[csv_source]
-)
-```
-
-### Excel Knowledge Source
-```python
-from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
-
-# Create an Excel knowledge source
-excel_source = ExcelKnowledgeSource(
-    file_paths=["spreadsheet.xlsx"]
-)
-
-# Create crew with Excel knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[excel_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[excel_source]
-)
-```
-
-### JSON Knowledge Source
-```python
-from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
-
-# Create a JSON knowledge source
-json_source = JSONKnowledgeSource(
-    file_paths=["data.json"]
-)
-
-# Create crew with JSON knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[json_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[json_source]
-)
-```
-
 ## Knowledge Configuration
 
 ### Chunking Configuration
 
-Knowledge sources automatically chunk content for better processing. 
-You can configure chunking behavior in your knowledge sources:
+Control how content is split for processing by setting the chunk size and overlap.
 
-```python
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-
-source = StringKnowledgeSource(
-    content="Your content here",
-    chunk_size=4000,      # Maximum size of each chunk (default: 4000)
-    chunk_overlap=200     # Overlap between chunks (default: 200)
+```python Code
+knowledge_source = StringKnowledgeSource(
+    content="Long content...",
+    chunk_size=4000,     # Characters per chunk (default)
+    chunk_overlap=200    # Overlap between chunks (default)
 )
 ```
 
-The chunking configuration helps in:
-- Breaking down large documents into manageable pieces
-- Maintaining context through chunk overlap
-- Optimizing retrieval accuracy
+## Embedder Configuration
 
-### Embeddings Configuration
+You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
 
-You can also configure the embedder for the knowledge store. 
-This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
-The `embedder` parameter supports various embedding model providers that include:
-- `openai`: OpenAI's embedding models
-- `google`: Google's text embedding models
-- `azure`: Azure OpenAI embeddings
-- `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
-
-Here's an example of how to configure the embedder for the knowledge store using Google's `text-embedding-004` model:
-<CodeGroup>
-```python Example
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-import os
-
-# Get the GEMINI API key
-GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
-
-# Create a knowledge source
-content = "Users name is John. He is 30 years old and lives in San Francisco."
+```python Code
+...
 string_source = StringKnowledgeSource(
-    content=content,
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
 )
-
-# Create an LLM with a temperature of 0 to ensure deterministic outputs
-gemini_llm = LLM(
-    model="gemini/gemini-1.5-pro-002",
-    api_key=GEMINI_API_KEY,
-    temperature=0,
-)
-
-# Create an agent with the knowledge store
-agent = Agent(
-    role="About User",
-    goal="You know everything about the user.",
-    backstory="""You are a master at understanding people and their preferences.""",
-    verbose=True,
-    allow_delegation=False,
-    llm=gemini_llm,
-)
-
-task = Task(
-    description="Answer the following questions about the user: {question}",
-    expected_output="An answer to the question.",
-    agent=agent,
-)
-
 crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    verbose=True,
-    process=Process.sequential,
+    ...
     knowledge_sources=[string_source],
     embedder={
-        "provider": "google",
-        "config": {
-            "model": "models/text-embedding-004",
-            "api_key": GEMINI_API_KEY,
-        }
-    }
+        "provider": "openai",
+        "config": {"model": "text-embedding-3-small"},
+    },
 )
-
-result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
 ```
-```text Output
-# Agent: About User
-## Task: Answer the following questions about the user: What city does John live in and how old is he?
 
-# Agent: About User
-## Final Answer: 
-John is 30 years old and lives in San Francisco.
-```
-</CodeGroup>
 ## Clearing Knowledge
 
 If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.

docs/concepts/llms.mdx

@@ -146,19 +146,6 @@ Here's a detailed breakdown of supported models and their capabilities, you can
       Groq is known for its fast inference speeds, making it suitable for real-time applications.
     </Tip>
   </Tab>
-  <Tab title="SambaNova">
-    | Model | Context Window | Best For |
-    |-------|---------------|-----------|
-    | Llama 3.1 70B/8B | Up to 131,072 tokens | High-performance, large context tasks |
-    | Llama 3.1 405B | 8,192 tokens | High-performance and output quality |
-    | Llama 3.2 Series | 8,192 tokens | General-purpose tasks, multimodal |
-    | Llama 3.3 70B | Up to 131,072 tokens | High-performance and output quality|
-    | Qwen2 familly | 8,192 tokens | High-performance and output quality |
-
-    <Tip>
-      [SambaNova](https://cloud.sambanova.ai/) has several models with fast inference speed at full precision.
-    </Tip>
-  </Tab>
   <Tab title="Others">
     | Provider | Context Window | Key Features |
     |----------|---------------|--------------|
@@ -243,9 +230,6 @@ 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
@@ -509,21 +493,6 @@ 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

docs/concepts/memory.mdx

@@ -134,23 +134,6 @@ crew = Crew(
 )
 ```
 
-## Memory Configuration Options
-If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.
-
-```python Code
-from crewai import Crew
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    verbose=True,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
-    },
-)
-```
 
 ## Additional Embedding Providers
 
@@ -293,26 +276,6 @@ my_crew = Crew(
     }
 )
 ```
-### Using VoyageAI embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "voyageai",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "model_name": "<model_name>"
-        }
-    }
-)
-```
 ### Using HuggingFace embeddings
 
 ```python Code

docs/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

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/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/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
@@ -33,7 +32,6 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - Cloudflare Workers AI
 - DeepInfra
 - Groq
-- SambaNova
 - [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1)
 - And many more!
 

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.
 
@@ -108,7 +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/how-to/portkey-observability.mdx

@@ -1,202 +0,0 @@
----
-title: Portkey Observability and Guardrails
-description: How to use Portkey with CrewAI
-icon: key
----
-
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
-
-
-[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
-
-Portkey adds 4 core production capabilities to any CrewAI agent:
-1. Routing to **200+ LLMs**
-2. Making each LLM call more robust
-3. Full-stack tracing & cost, performance analytics
-4. Real-time guardrails to enforce behavior
-
-## Getting Started
-
-<Steps>
-    <Step title="Install CrewAI and Portkey">
-    ```bash
-    pip install -qU crewai portkey-ai
-    ```
-    </Step>
-    <Step title="Configure the LLM Client">
-    To build CrewAI Agents with Portkey, you'll need two keys:
-    - **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
-    - **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
-
-    ```python
-    from crewai import LLM
-    from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-    gpt_llm = LLM(
-        model="gpt-4",
-        base_url=PORTKEY_GATEWAY_URL,
-        api_key="dummy", # We are using Virtual key
-        extra_headers=createHeaders(
-            api_key="YOUR_PORTKEY_API_KEY",
-            virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
-        )
-    )
-    ```
-    </Step>
-    <Step title="Create and Run Your First Agent">
-    ```python
-    from crewai import Agent, Task, Crew
-
-    # Define your agents with roles and goals
-    coder = Agent(
-        role='Software developer',
-        goal='Write clear, concise code on demand',
-        backstory='An expert coder with a keen eye for software trends.',
-        llm=gpt_llm
-    )
-
-    # Create tasks for your agents
-    task1 = Task(
-        description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
-        expected_output="A clear and concise HTML code",
-        agent=coder
-    )
-
-    # Instantiate your crew
-    crew = Crew(
-        agents=[coder],
-        tasks=[task1],
-    )
-
-    result = crew.kickoff()
-    print(result)
-    ```
-    </Step>
-</Steps>
-
-## Key Features
-
-| Feature | Description |
-|:--------|:------------|
-| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
-| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
-| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
-| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
-| 🚧 Security Controls | Set budget limits and implement role-based access control |
-| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
-| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
-
-
-## Production Features with Portkey Configs
-
-All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
-
-<Frame>
-    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
-</Frame>
-
-
-### 1. Use 250+ LLMs
-Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
-
-
-Easily switch between different LLM providers:
-
-```python
-# Anthropic Configuration
-anthropic_llm = LLM(
-    model="claude-3-5-sonnet-latest",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="anthropic_agent"
-    )
-)
-
-# Azure OpenAI Configuration
-azure_llm = LLM(
-    model="gpt-4",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="azure_agent"
-    )
-)
-```
-
-
-### 2. Caching
-Improve response times and reduce costs with two powerful caching modes:
-- **Simple Cache**: Perfect for exact matches
-- **Semantic Cache**: Matches responses for requests that are semantically similar
-[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
-
-```py
-config = {
-    "cache": {
-        "mode": "semantic",  # or "simple" for exact matching
-    }
-}
-```
-
-### 3. Production Reliability
-Portkey provides comprehensive reliability features:
-- **Automatic Retries**: Handle temporary failures gracefully
-- **Request Timeouts**: Prevent hanging operations
-- **Conditional Routing**: Route requests based on specific conditions
-- **Fallbacks**: Set up automatic provider failovers
-- **Load Balancing**: Distribute requests efficiently
-
-[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
-
-
-
-### 4. Metrics
-
-Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
-
-
-- Cost per agent interaction
-- Response times and latency
-- Token usage and efficiency
-- Success/failure rates
-- Cache hit rates
-
-<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
-
-### 5. Detailed Logging
-Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
-
-
-Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
-
-<details>
-  <summary><b>Traces</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
-</details>
-
-<details>
-  <summary><b>Logs</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
-</details>
-
-### 6. Enterprise Security Features
-- Set budget limit and rate limts per Virtual Key (disposable API keys)
-- Implement role-based access control
-- Track system changes with audit logs
-- Configure data retention policies
-
-
-
-For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
-
-## Resources
-
-- [📘 Portkey Documentation](https://docs.portkey.ai)
-- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
-- [🐦 Twitter](https://twitter.com/portkeyai)
-- [💬 Discord Community](https://discord.gg/DD7vgKK299)

docs/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/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",
@@ -101,8 +100,7 @@
         "how-to/conditional-tasks",
         "how-to/agentops-observability",
         "how-to/langtrace-observability",
-        "how-to/openlit-observability",
-        "how-to/portkey-observability"
+        "how-to/openlit-observability"
       ]
     },
     {

docs/quickstart.mdx

@@ -278,7 +278,7 @@ email_summarizer:
       Summarize emails into a concise and clear summary
     backstory: >
       You will create a 5 bullet point summary of the report
-    llm: 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)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.98.0"
+version = "0.86.0"
 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==1.57.4",
+    "litellm>=1.56.4",
     "instructor>=1.3.3",
+    
     # Text Processing
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
+    
     # Telemetry and Monitoring
     "opentelemetry-api>=1.22.0",
     "opentelemetry-sdk>=1.22.0",
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
+    
     # Data Handling
     "chromadb>=0.5.23",
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
+    
     # Authentication and Security
     "auth0-python>=4.7.1",
     "python-dotenv>=1.0.0",
+    
     # Configuration and Utils
     "click>=8.1.7",
     "appdirs>=1.4.4",
@@ -36,7 +41,6 @@ dependencies = [
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
     "blinker>=1.9.0",
-    "json5>=0.10.0",
 ]
 
 [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>=0.17.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.98.0"
+__version__ = "0.86.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,3 +1,6 @@
+from __future__ import annotations
+
+import os
 import shutil
 import subprocess
 from typing import Any, Dict, List, Literal, Optional, Union
@@ -7,6 +10,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
@@ -19,7 +23,9 @@ 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.logger import Logger
+from crewai.utilities.rpm_controller import RPMController
+from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -44,24 +50,113 @@ class Agent(BaseAgent):
     Each agent has a role, a goal, a backstory, and an optional language model (llm).
     The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
 
-    Attributes:
-            agent_executor: An instance of the CrewAgentExecutor class.
-            role: The role of the agent.
-            goal: The objective of the agent.
-            backstory: The backstory of the agent.
-            knowledge: The knowledge base of the agent.
-            config: Dict representation of agent configuration.
-            llm: The language model that will run the agent.
-            function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
-            max_iter: Maximum number of iterations for an agent to execute a task.
-            memory: Whether the agent should have memory or not.
-            max_rpm: Maximum number of requests per minute for the agent execution to be respected.
-            verbose: Whether the agent execution should be in verbose mode.
-            allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
-            tools: Tools at agents disposal
-            step_callback: Callback to be executed after each step of the agent execution.
-            knowledge_sources: Knowledge sources for the agent.
+    Args:
+        role (Optional[str]): The role of the agent
+        goal (Optional[str]): The objective of the agent
+        backstory (Optional[str]): The backstory of the agent
+        allow_delegation (bool): Whether the agent can delegate tasks
+        config (Optional[Dict[str, Any]]): Configuration for the agent
+        verbose (bool): Whether to enable verbose output
+        max_rpm (Optional[int]): Maximum requests per minute
+        tools (Optional[List[Any]]): Tools available to the agent
+        llm (Optional[Union[str, Any]]): Language model to use
+        function_calling_llm (Optional[Any]): Language model for tool calling
+        max_iter (Optional[int]): Maximum iterations for task execution
+        memory (bool): Whether the agent should have memory
+        step_callback (Optional[Any]): Callback after each execution step
+        knowledge_sources (Optional[List[BaseKnowledgeSource]]): Knowledge sources
     """
+    
+    model_config = {
+        "arbitrary_types_allowed": True,
+        "extra": "allow",
+    }
+
+    def __init__(
+        self,
+        role: Optional[str] = None,
+        goal: Optional[str] = None,
+        backstory: Optional[str] = None,
+        allow_delegation: bool = False,
+        config: Optional[Dict[str, Any]] = None,
+        verbose: bool = False,
+        max_rpm: Optional[int] = None,
+        tools: Optional[List[Any]] = None,
+        llm: Optional[Union[str, LLM, Any]] = None,
+        function_calling_llm: Optional[Any] = None,
+        max_iter: Optional[int] = None,
+        memory: bool = True,
+        step_callback: Optional[Any] = None,
+        knowledge_sources: Optional[List[BaseKnowledgeSource]] = None,
+        **kwargs
+    ) -> None:
+        """Initialize an Agent with the given parameters."""
+        # Process tools before passing to parent
+        processed_tools = []
+        if tools:
+            from crewai.tools import BaseTool
+            for tool in tools:
+                if isinstance(tool, BaseTool):
+                    processed_tools.append(tool)
+                elif callable(tool):
+                    # Convert function to BaseTool
+                    processed_tools.append(Tool.from_function(tool))
+                else:
+                    raise ValueError(f"Tool {tool} must be either a BaseTool instance or a callable")
+
+        # Process LLM before passing to parent
+        processed_llm = None
+        if isinstance(llm, str):
+            processed_llm = LLM(model=llm)
+        elif isinstance(llm, LLM):
+            processed_llm = llm
+        elif llm is not None and hasattr(llm, 'model') and hasattr(llm, 'temperature'):
+            # Handle ChatOpenAI and similar objects
+            model_name = getattr(llm, 'model', None)
+            if model_name is not None:
+                if not isinstance(model_name, str):
+                    model_name = str(model_name)
+                processed_llm = LLM(
+                    model=model_name,
+                    temperature=getattr(llm, 'temperature', None),
+                    api_key=getattr(llm, 'api_key', None),
+                    base_url=getattr(llm, 'base_url', None)
+                )
+        # If no valid LLM configuration found, leave as None for post_init_setup
+
+        # Initialize all fields in a dict
+        init_dict = {
+            "role": role,
+            "goal": goal,
+            "backstory": backstory,
+            "allow_delegation": allow_delegation,
+            "config": config,
+            "verbose": verbose,
+            "max_rpm": max_rpm,
+            "tools": processed_tools,
+            "max_iter": max_iter if max_iter is not None else 25,
+            "function_calling_llm": function_calling_llm,
+            "step_callback": step_callback,
+            "knowledge_sources": knowledge_sources,
+            **kwargs
+        }
+
+        # Initialize base model with all fields
+        super().__init__(**init_dict)
+        
+        # Store original values for interpolation
+        self._original_role = role
+        self._original_goal = goal
+        self._original_backstory = backstory
+        
+        # Set LLM after base initialization to ensure proper model handling
+        self.llm = processed_llm
+        
+        # Initialize private attributes
+        self._logger = Logger(verbose=self.verbose)
+        if self.max_rpm:
+            self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
+        self._token_process = TokenProcess()
 
     _times_executed: int = PrivateAttr(default=0)
     max_execution_time: Optional[int] = Field(
@@ -84,7 +179,7 @@ class Agent(BaseAgent):
     llm: Union[str, InstanceOf[LLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+    function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: Optional[str] = Field(
@@ -137,11 +232,146 @@ class Agent(BaseAgent):
     @model_validator(mode="after")
     def post_init_setup(self):
         self._set_knowledge()
-        self.agent_ops_agent_name = self.role
+        self.agent_ops_agent_name = self.role or "agent"
+        unaccepted_attributes = [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+        ]
 
-        self.llm = create_llm(self.llm)
-        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
-            self.function_calling_llm = create_llm(self.function_calling_llm)
+        # Handle LLM initialization if not already done
+        if 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:
+                                        try:
+                                            # Create a new dictionary for properly typed parameters
+                                            typed_params = {}
+                                            
+                                            # Convert and validate values based on parameter type
+                                            if key in ['temperature', 'top_p', 'presence_penalty', 'frequency_penalty']:
+                                                if value is not None:
+                                                    try:
+                                                        typed_params[key] = float(value)
+                                                    except (ValueError, TypeError):
+                                                        pass
+                                            elif key in ['n', 'max_tokens', 'max_completion_tokens', 'seed']:
+                                                if value is not None:
+                                                    try:
+                                                        typed_params[key] = int(value)
+                                                    except (ValueError, TypeError):
+                                                        pass
+                                            elif key == 'logit_bias' and isinstance(value, str):
+                                                try:
+                                                    bias_dict = {}
+                                                    for pair in value.split(','):
+                                                        token_id, bias = pair.split(':')
+                                                        bias_dict[int(token_id.strip())] = float(bias.strip())
+                                                    typed_params[key] = bias_dict
+                                                except (ValueError, AttributeError):
+                                                    pass
+                                            elif key == 'response_format' and isinstance(value, str):
+                                                try:
+                                                    import json
+                                                    typed_params[key] = json.loads(value)
+                                                except json.JSONDecodeError:
+                                                    pass
+                                            elif key == 'logprobs':
+                                                if value is not None:
+                                                    typed_params[key] = bool(value.lower() == 'true') if isinstance(value, str) else bool(value)
+                                            elif key == 'callbacks':
+                                                typed_params[key] = [] if value is None else [value] if isinstance(value, str) else value
+                                            elif key == 'stop':
+                                                typed_params[key] = [value] if isinstance(value, str) else value
+                                            elif key in ['model', 'base_url', 'api_version', 'api_key']:
+                                                typed_params[key] = value
+                                            
+                                            # Update llm_params with properly typed values
+                                            if typed_params:
+                                                llm_params.update(typed_params)
+                                        except (ValueError, AttributeError, json.JSONDecodeError):
+                                            continue
+
+            # Create LLM instance with properly typed parameters
+            valid_params = {
+                'model', 'timeout', 'temperature', 'top_p', 'n', 'stop',
+                'max_completion_tokens', 'max_tokens', 'presence_penalty',
+                'frequency_penalty', 'logit_bias', 'response_format',
+                'seed', 'logprobs', 'top_logprobs', 'base_url',
+                'api_version', 'api_key', 'callbacks'
+            }
+            
+            # Filter out None values and invalid parameters
+            filtered_params = {}
+            for k, v in llm_params.items():
+                if k in valid_params and v is not None:
+                    filtered_params[k] = v
+            
+            # Create LLM instance with properly typed parameters
+            self.llm = LLM(**filtered_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)
+                )
 
         if not self.agent_executor:
             self._setup_agent_executor()
@@ -159,7 +389,7 @@ class Agent(BaseAgent):
     def _set_knowledge(self):
         try:
             if self.knowledge_sources:
-                knowledge_agent_name = f"{self.role.replace(' ', '_')}"
+                knowledge_agent_name = f"{(self.role or 'agent').replace(' ', '_')}"
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
@@ -259,9 +489,6 @@ class Agent(BaseAgent):
                 }
             )["output"]
         except Exception as e:
-            if e.__class__.__module__.startswith("litellm"):
-                # Do not retry on litellm errors
-                raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 raise e
@@ -307,6 +534,32 @@ class Agent(BaseAgent):
                 self.response_template.split("{{ .Response }}")[1].strip()
             )
 
+        # Ensure LLM is initialized with proper error handling
+        try:
+            if not self.llm:
+                self.llm = LLM(model="gpt-4")
+                if hasattr(self, '_logger'):
+                    self._logger.debug("Initialized default LLM with gpt-4 model")
+        except Exception as e:
+            if hasattr(self, '_logger'):
+                self._logger.error(f"Failed to initialize LLM: {str(e)}")
+            raise
+
+        # Create token callback with proper error handling
+        try:
+            token_callback = None
+            if hasattr(self, '_token_process'):
+                token_callback = TokenCalcHandler(self._token_process)
+        except Exception as e:
+            if hasattr(self, '_logger'):
+                self._logger.warning(f"Failed to create token callback: {str(e)}")
+            token_callback = None
+
+        # Initialize callbacks list
+        executor_callbacks = []
+        if token_callback:
+            executor_callbacks.append(token_callback)
+
         self.agent_executor = CrewAgentExecutor(
             llm=self.llm,
             task=task,
@@ -324,9 +577,9 @@ class Agent(BaseAgent):
             function_calling_llm=self.function_calling_llm,
             respect_context_window=self.respect_context_window,
             request_within_rpm_limit=(
-                self._rpm_controller.check_or_wait if self._rpm_controller else None
+                self._rpm_controller.check_or_wait if (hasattr(self, '_rpm_controller') and self._rpm_controller is not None) else None
             ),
-            callbacks=[TokenCalcHandler(self._token_process)],
+            callbacks=executor_callbacks,
         )
 
     def get_delegation_tools(self, agents: List[BaseAgent]):
@@ -336,7 +589,6 @@ 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

@@ -18,6 +18,7 @@ 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.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
@@ -87,9 +88,9 @@ class BaseAgent(ABC, BaseModel):
     formatting_errors: int = Field(
         default=0, description="Number of formatting errors."
     )
-    role: str = Field(description="Role of the agent")
-    goal: str = Field(description="Objective of the agent")
-    backstory: str = Field(description="Backstory of the agent")
+    role: Optional[str] = Field(default=None, description="Role of the agent")
+    goal: Optional[str] = Field(default=None, description="Objective of the agent")
+    backstory: Optional[str] = Field(default=None, description="Backstory of the agent")
     config: Optional[Dict[str, Any]] = Field(
         description="Configuration for the agent", default=None, exclude=True
     )
@@ -130,26 +131,47 @@ class BaseAgent(ABC, BaseModel):
     max_tokens: Optional[int] = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
     )
+    function_calling_llm: Optional[Any] = Field(
+        default=None, description="Language model for function calling."
+    )
+    step_callback: Optional[Any] = Field(
+        default=None, description="Callback for execution steps."
+    )
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+        default=None, description="Knowledge sources for the agent."
+    )
+
+    model_config = {
+        "arbitrary_types_allowed": True,
+        "extra": "allow",  # Allow extra fields in constructor
+    }
 
     @model_validator(mode="before")
     @classmethod
     def process_model_config(cls, values):
+        """Process configuration values before model initialization."""
         return process_config(values, cls)
 
     @field_validator("tools")
     @classmethod
-    def validate_tools(cls, tools: List[Any]) -> List[BaseTool]:
+    def validate_tools(cls, tools: Optional[List[Any]]) -> List[BaseTool]:
         """Validate and process the tools provided to the agent.
 
-        This method ensures that each tool is either an instance of BaseTool
-        or an object with 'name', 'func', and 'description' attributes. If the
-        tool meets these criteria, it is processed and added to the list of
-        tools. Otherwise, a ValueError is raised.
+        This method ensures that each tool is either an instance of BaseTool,
+        a function decorated with @tool, or an object with 'name', 'func', 
+        and 'description' attributes. If the tool meets these criteria, it is 
+        processed and added to the list of tools. Otherwise, a ValueError is raised.
         """
+        if not tools:
+            return []
+
         processed_tools = []
         for tool in tools:
             if isinstance(tool, BaseTool):
                 processed_tools.append(tool)
+            elif callable(tool) and hasattr(tool, "_is_tool") and tool._is_tool:
+                # Handle @tool decorated functions
+                processed_tools.append(Tool.from_function(tool))
             elif (
                 hasattr(tool, "name")
                 and hasattr(tool, "func")
@@ -157,31 +179,54 @@ class BaseAgent(ABC, BaseModel):
             ):
                 # Tool has the required attributes, create a Tool instance
                 processed_tools.append(Tool.from_langchain(tool))
-            else:
+            else: 
                 raise ValueError(
                     f"Invalid tool type: {type(tool)}. "
-                    "Tool must be an instance of BaseTool or "
-                    "an object with 'name', 'func', and 'description' attributes."
+                    "Tool must be an instance of BaseTool, a @tool decorated function, "
+                    "or an object with 'name', 'func', and 'description' attributes."
                 )
         return processed_tools
 
     @model_validator(mode="after")
     def validate_and_set_attributes(self):
-        # Validate required fields
-        for field in ["role", "goal", "backstory"]:
-            if getattr(self, field) is None:
-                raise ValueError(
-                    f"{field} must be provided either directly or through config"
-                )
+        """Validate and set attributes for the agent.
+        
+        This method ensures that attributes are properly set and initialized,
+        either from direct parameters or configuration.
+        """
+        # Store original values for interpolation
+        self._original_role = self.role
+        self._original_goal = self.goal
+        self._original_backstory = self.backstory
 
-        # Set private attributes
+        # Process config if provided
+        if self.config:
+            config_data = self.config
+            if isinstance(config_data, str):
+                import json
+                try:
+                    config_data = json.loads(config_data)
+                except json.JSONDecodeError:
+                    raise ValueError("Invalid JSON in config")
+
+            # Update fields from config if they're None
+            for field in ["role", "goal", "backstory"]:
+                if field in config_data and getattr(self, field) is None:
+                    setattr(self, field, config_data[field])
+
+        # Set default values for required fields if they're still None
+        self.role = self.role or "Assistant"
+        self.goal = self.goal or "Help the user accomplish their tasks"
+        self.backstory = self.backstory or "I am an AI assistant ready to help"
+
+        # Initialize tools handler if not set
+        if not hasattr(self, 'tools_handler') or self.tools_handler is None:
+            self.tools_handler = ToolsHandler()
+
+        # Initialize logger and rpm controller
         self._logger = Logger(verbose=self.verbose)
-        if self.max_rpm and not self._rpm_controller:
-            self._rpm_controller = RPMController(
-                max_rpm=self.max_rpm, logger=self._logger
-            )
-        if not self._token_process:
-            self._token_process = TokenProcess()
+        if self.max_rpm:
+            self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
 
         return self
 
@@ -208,9 +253,9 @@ class BaseAgent(ABC, BaseModel):
     @property
     def key(self):
         source = [
-            self._original_role or self.role,
-            self._original_goal or self.goal,
-            self._original_backstory or self.backstory,
+            str(self._original_role or self.role or ""),
+            str(self._original_goal or self.goal or ""),
+            str(self._original_backstory or self.backstory or ""),
         ]
         return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
 
@@ -256,29 +301,45 @@ class BaseAgent(ABC, BaseModel):
             "tools_handler",
             "cache_handler",
             "llm",
+            "function_calling_llm",
         }
 
-        # Copy llm and clear callbacks
-        existing_llm = shallow_copy(self.llm)
+        # Copy LLMs and clear callbacks
+        existing_llm = shallow_copy(self.llm) if self.llm else None
+        existing_function_calling_llm = shallow_copy(self.function_calling_llm) if self.function_calling_llm else None
+
+        # Create base data
         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)
+
+        # Create new instance with copied data
+        copied_agent = type(self)(
+            **copied_data,
+            llm=existing_llm,
+            function_calling_llm=existing_function_calling_llm,
+            tools=self.tools
+        )
+
+        # Copy private attributes
+        copied_agent._original_role = self._original_role
+        copied_agent._original_goal = self._original_goal
+        copied_agent._original_backstory = self._original_backstory
 
         return copied_agent
 
     def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
         """Interpolate inputs into the agent description and backstory."""
         if self._original_role is None:
-            self._original_role = self.role
+            self._original_role = self.role or ""
         if self._original_goal is None:
-            self._original_goal = self.goal
+            self._original_goal = self.goal or ""
         if self._original_backstory is None:
-            self._original_backstory = self.backstory
+            self._original_backstory = self.backstory or ""
 
         if inputs:
-            self.role = self._original_role.format(**inputs)
-            self.goal = self._original_goal.format(**inputs)
-            self.backstory = self._original_backstory.format(**inputs)
+            self.role = self._original_role.format(**inputs) if self._original_role else None
+            self.goal = self._original_goal.format(**inputs) if self._original_goal else None
+            self.backstory = self._original_backstory.format(**inputs) if self._original_backstory else None
 
     def set_cache_handler(self, cache_handler: CacheHandler) -> None:
         """Set the cache handler for the agent.

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -19,10 +19,15 @@ class CrewAgentExecutorMixin:
     agent: Optional["BaseAgent"]
     task: Optional["Task"]
     iterations: int
+    have_forced_answer: bool
     max_iter: int
     _i18n: I18N
     _printer: Printer = Printer()
 
+    def _should_force_answer(self) -> bool:
+        """Determine if a forced answer is required based on iteration count."""
+        return (self.iterations >= self.max_iter) and not self.have_forced_answer
+
     def _create_short_term_memory(self, output) -> None:
         """Create and save a short-term memory item if conditions are met."""
         if (
@@ -77,16 +82,17 @@ class CrewAgentExecutorMixin:
                 )
                 self.crew._long_term_memory.save(long_term_memory)
 
-                for entity in evaluation.entities:
-                    entity_memory = EntityMemoryItem(
-                        name=entity.name,
-                        type=entity.type,
-                        description=entity.description,
-                        relationships="\n".join(
-                            [f"- {r}" for r in entity.relationships]
-                        ),
-                    )
-                    self.crew._entity_memory.save(entity_memory)
+                if hasattr(evaluation, 'entities') and evaluation.entities:
+                    for entity in evaluation.entities:
+                        entity_memory = EntityMemoryItem(
+                            name=entity.name,
+                            type=entity.type,
+                            description=entity.description,
+                            relationships="\n".join(
+                                [f"- {r}" for r in entity.relationships]
+                            ),
+                        )
+                        self.crew._entity_memory.save(entity_memory)
             except AttributeError as e:
                 print(f"Missing attributes for long term memory: {e}")
                 pass

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

@@ -1,7 +1,7 @@
 import json
 import re
 from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Dict, List, Union
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
@@ -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
@@ -51,11 +50,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         original_tools: List[Any] = [],
         function_calling_llm: Any = None,
         respect_context_window: bool = False,
-        request_within_rpm_limit: Optional[Callable[[], bool]] = None,
+        request_within_rpm_limit: Any = None,
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm: LLM = llm
+        self.llm = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -69,7 +68,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tools_handler = tools_handler
         self.original_tools = original_tools
         self.step_callback = step_callback
-        self.use_stop_words = self.llm.supports_stop_words()
+        self.use_stop_words = self.llm.supports_stop_words() if self.llm else False
         self.tools_description = tools_description
         self.function_calling_llm = function_calling_llm
         self.respect_context_window = respect_context_window
@@ -78,11 +77,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: List[Dict[str, str]] = []
         self.iterations = 0
         self.log_error_after = 3
+        self.have_forced_answer = False
         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:
@@ -97,16 +99,7 @@ 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 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)
@@ -115,186 +108,121 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
-    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.
-        """
-        formatted_answer = None
-        while not isinstance(formatted_answer, AgentFinish):
-            try:
-                if self._has_reached_max_iterations():
-                    formatted_answer = self._handle_max_iterations_exceeded(
-                        formatted_answer
-                    )
-                    break
-
-                self._enforce_rpm_limit()
-
-                answer = self._get_llm_response()
-                formatted_answer = self._process_llm_response(answer)
-
-                if isinstance(formatted_answer, AgentAction):
-                    tool_result = self._execute_tool_and_check_finality(
-                        formatted_answer
-                    )
-                    formatted_answer = self._handle_agent_action(
-                        formatted_answer, tool_result
-                    )
-
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(formatted_answer.text, role="assistant")
-
-            except OutputParserException as e:
-                formatted_answer = self._handle_output_parser_exception(e)
-
-            except Exception as e:
-                if e.__class__.__module__.startswith("litellm"):
-                    # Do not retry on litellm errors
-                    raise e
-                if self._is_context_length_exceeded(e):
-                    self._handle_context_length()
-                    continue
-                else:
-                    self._handle_unknown_error(e)
-                    raise e
-            finally:
-                self.iterations += 1
-
-        self._show_logs(formatted_answer)
-        return formatted_answer
-
-    def _handle_unknown_error(self, exception: Exception) -> None:
-        """Handle unknown errors by informing the user."""
-        self._printer.print(
-            content="An unknown error occurred. Please check the details below.",
-            color="red",
-        )
-        self._printer.print(
-            content=f"Error details: {exception}",
-            color="red",
-        )
-
-    def _has_reached_max_iterations(self) -> bool:
-        """Check if the maximum number of iterations has been reached."""
-        return self.iterations >= self.max_iter
-
-    def _enforce_rpm_limit(self) -> None:
-        """Enforce the requests per minute (RPM) limit if applicable."""
-        if self.request_within_rpm_limit:
-            self.request_within_rpm_limit()
-
-    def _get_llm_response(self) -> str:
-        """Call the LLM and return the response, handling any invalid responses."""
+    def _invoke_loop(self, formatted_answer=None):
         try:
-            answer = self.llm.call(
-                self.messages,
-                callbacks=self.callbacks,
-            )
+            while not isinstance(formatted_answer, AgentFinish):
+                if not self.request_within_rpm_limit or self.request_within_rpm_limit():
+                    answer = self.llm.call(
+                        self.messages,
+                        callbacks=self.callbacks,
+                    )
+
+                    if answer is None or answer == "":
+                        self._printer.print(
+                            content="Received None or empty response from LLM call.",
+                            color="red",
+                        )
+                        raise ValueError(
+                            "Invalid response from LLM call - None or empty."
+                        )
+
+                    if not self.use_stop_words:
+                        try:
+                            self._format_answer(answer)
+                        except OutputParserException as e:
+                            if (
+                                FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE
+                                in e.error
+                            ):
+                                answer = answer.split("Observation:")[0].strip()
+
+                    self.iterations += 1
+                    formatted_answer = self._format_answer(answer)
+
+                    if isinstance(formatted_answer, AgentAction):
+                        tool_result = self._execute_tool_and_check_finality(
+                            formatted_answer
+                        )
+
+                        # Directly append the result to the messages if the
+                        # tool is "Add image to content" in case of multimodal
+                        # agents
+                        add_image_tool_name = self._i18n.tools("add_image")
+                        if add_image_tool_name and formatted_answer.tool == add_image_tool_name:
+                            self.messages.append(tool_result.result)
+                            continue
+
+                        else:
+                            if self.step_callback:
+                                self.step_callback(tool_result)
+
+                            formatted_answer.text += f"\nObservation: {tool_result.result}"
+
+                        formatted_answer.result = tool_result.result
+                        if tool_result.result_as_answer:
+                            return AgentFinish(
+                                thought="",
+                                output=tool_result.result,
+                                text=formatted_answer.text,
+                            )
+                        self._show_logs(formatted_answer)
+
+                    if self.step_callback:
+                        self.step_callback(formatted_answer)
+
+                    if self._should_force_answer():
+                        if self.have_forced_answer:
+                            return AgentFinish(
+                                thought="",
+                                output=self._i18n.errors(
+                                    "force_final_answer_error"
+                                ).format(formatted_answer.text),
+                                text=formatted_answer.text,
+                            )
+                        else:
+                            formatted_answer.text += (
+                                f'\n{self._i18n.errors("force_final_answer")}'
+                            )
+                            self.have_forced_answer = True
+                    self.messages.append(
+                        self._format_msg(formatted_answer.text, role="assistant")
+                    )
+
+        except OutputParserException as e:
+            self.messages.append({"role": "user", "content": e.error})
+            if self.iterations > self.log_error_after:
+                self._printer.print(
+                    content=f"Error parsing LLM output, agent will retry: {e.error}",
+                    color="red",
+                )
+            return self._invoke_loop(formatted_answer)
+
         except Exception as e:
-            self._printer.print(
-                content=f"Error during LLM call: {e}",
-                color="red",
-            )
-            raise e
-
-        if not answer:
-            self._printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-            raise ValueError("Invalid response from LLM call - None or empty.")
-
-        return answer
-
-    def _process_llm_response(self, answer: str) -> Union[AgentAction, AgentFinish]:
-        """Process the LLM response and format it into an AgentAction or AgentFinish."""
-        if not self.use_stop_words:
-            try:
-                # Preliminary parsing to check for errors.
-                self._format_answer(answer)
-            except OutputParserException as e:
-                if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
-                    answer = answer.split("Observation:")[0].strip()
-
-        return self._format_answer(answer)
-
-    def _handle_agent_action(
-        self, formatted_answer: AgentAction, tool_result: ToolResult
-    ) -> Union[AgentAction, AgentFinish]:
-        """Handle the AgentAction, execute tools, and process the results."""
-        add_image_tool = self._i18n.tools("add_image")
-        if (
-            isinstance(add_image_tool, dict)
-            and formatted_answer.tool.casefold().strip()
-            == add_image_tool.get("name", "").casefold().strip()
-        ):
-            self.messages.append(tool_result.result)
-            return formatted_answer  # Continue the loop
-
-        if self.step_callback:
-            self.step_callback(tool_result)
-
-        formatted_answer.text += f"\nObservation: {tool_result.result}"
-        formatted_answer.result = tool_result.result
-
-        if tool_result.result_as_answer:
-            return AgentFinish(
-                thought="",
-                output=tool_result.result,
-                text=formatted_answer.text,
-            )
+            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
+                str(e)
+            ):
+                self._handle_context_length()
+                return self._invoke_loop(formatted_answer)
+            else:
+                raise e
 
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    def _invoke_step_callback(self, formatted_answer) -> None:
-        """Invoke the step callback if it exists."""
-        if self.step_callback:
-            self.step_callback(formatted_answer)
-
-    def _append_message(self, text: str, role: str = "assistant") -> None:
-        """Append a message to the message list with the given role."""
-        self.messages.append(self._format_msg(text, role=role))
-
-    def _handle_output_parser_exception(self, e: OutputParserException) -> AgentAction:
-        """Handle OutputParserException by updating messages and formatted_answer."""
-        self.messages.append({"role": "user", "content": e.error})
-
-        formatted_answer = AgentAction(
-            text=e.error,
-            tool="",
-            tool_input="",
-            thought="",
-        )
-
-        if self.iterations > self.log_error_after:
-            self._printer.print(
-                content=f"Error parsing LLM output, agent will retry: {e.error}",
-                color="red",
-            )
-
-        return formatted_answer
-
-    def _is_context_length_exceeded(self, exception: Exception) -> bool:
-        """Check if the exception is due to context length exceeding."""
-        return LLMContextLengthExceededException(
-            str(exception)
-        )._is_context_limit_error(str(exception))
-
     def _show_start_logs(self):
         if self.agent is None:
             raise ValueError("Agent cannot be None")
         if self.agent.verbose or (
             hasattr(self, "crew") and getattr(self.crew, "verbose", False)
         ):
-            agent_role = self.agent.role.split("\n")[0]
+            agent_role = self.agent.role.split("\n")[0] if self.agent and self.agent.role else ""
             self._printer.print(
                 content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
             )
-            self._printer.print(
-                content=f"\033[95m## Task:\033[00m \033[92m{self.task.description}\033[00m"
-            )
+            if self.task and self.task.description:
+                self._printer.print(
+                    content=f"\033[95m## Task:\033[00m \033[92m{self.task.description}\033[00m"
+                )
 
     def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
         if self.agent is None:
@@ -302,7 +230,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         if self.agent.verbose or (
             hasattr(self, "crew") and getattr(self.crew, "verbose", False)
         ):
-            agent_role = self.agent.role.split("\n")[0]
+            agent_role = self.agent.role.split("\n")[0] if self.agent and self.agent.role else ""
             if isinstance(formatted_answer, AgentAction):
                 thought = re.sub(r"\n+", "\n", formatted_answer.thought)
                 formatted_json = json.dumps(
@@ -346,7 +274,7 @@ 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
@@ -561,45 +489,3 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self.ask_for_human_input = False
 
         return formatted_answer
-
-    def _handle_max_iterations_exceeded(self, formatted_answer):
-        """
-        Handles the case when the maximum number of iterations is exceeded.
-        Performs one more LLM call to get the final answer.
-
-        Parameters:
-            formatted_answer: The last formatted answer from the agent.
-
-        Returns:
-            The final formatted answer after exceeding max iterations.
-        """
-        self._printer.print(
-            content="Maximum iterations reached. Requesting final answer.",
-            color="yellow",
-        )
-
-        if formatted_answer and hasattr(formatted_answer, "text"):
-            assistant_message = (
-                formatted_answer.text + f'\n{self._i18n.errors("force_final_answer")}'
-            )
-        else:
-            assistant_message = self._i18n.errors("force_final_answer")
-
-        self.messages.append(self._format_msg(assistant_message, role="assistant"))
-
-        # Perform one more LLM call to get the final answer
-        answer = self.llm.call(
-            self.messages,
-            callbacks=self.callbacks,
-        )
-
-        if answer is None or answer == "":
-            self._printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-            raise ValueError("Invalid response from LLM call - None or empty.")
-
-        formatted_answer = self._format_answer(answer)
-        # Return the formatted answer, regardless of its type
-        return formatted_answer

src/crewai/cli/cli.py

@@ -1,13 +1,11 @@
-import os
 from importlib.metadata import version as get_version
-from typing import Optional, Tuple
+from typing import Optional
 
 import click
 
 from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
-from crewai.cli.crew_chat import run_chat
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
@@ -344,18 +342,5 @@ def flow_add_crew(crew_name):
     add_crew_to_flow(crew_name)
 
 
-@crewai.command()
-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",
-    )
-
-    run_chat()
-
-
 if __name__ == "__main__":
     crewai()

src/crewai/cli/constants.py

@@ -17,12 +17,6 @@ ENV_VARS = {
             "key_name": "GEMINI_API_KEY",
         }
     ],
-    "nvidia_nim": [
-        {
-            "prompt": "Enter your NVIDIA API key (press Enter to skip)",
-            "key_name": "NVIDIA_NIM_API_KEY",
-        }
-    ],
     "groq": [
         {
             "prompt": "Enter your GROQ API key (press Enter to skip)",
@@ -91,12 +85,6 @@ ENV_VARS = {
             "key_name": "CEREBRAS_API_KEY",
         },
     ],
-    "sambanova": [
-        {
-            "prompt": "Enter your SambaNovaCloud API key (press Enter to skip)",
-            "key_name": "SAMBANOVA_API_KEY",
-        }
-    ],
 }
 
 
@@ -104,14 +92,12 @@ PROVIDERS = [
     "openai",
     "anthropic",
     "gemini",
-    "nvidia_nim",
     "groq",
     "ollama",
     "watson",
     "bedrock",
     "azure",
     "cerebras",
-    "sambanova",
 ]
 
 MODELS = {
@@ -128,75 +114,6 @@ MODELS = {
         "gemini/gemini-gemma-2-9b-it",
         "gemini/gemini-gemma-2-27b-it",
     ],
-    "nvidia_nim": [
-        "nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",
-        "nvidia_nim/nvidia/nemotron-4-mini-hindi-4b-instruct",
-        "nvidia_nim/nvidia/llama-3.1-nemotron-70b-instruct",
-        "nvidia_nim/nvidia/llama3-chatqa-1.5-8b",
-        "nvidia_nim/nvidia/llama3-chatqa-1.5-70b",
-        "nvidia_nim/nvidia/vila",
-        "nvidia_nim/nvidia/neva-22",
-        "nvidia_nim/nvidia/nemotron-mini-4b-instruct",
-        "nvidia_nim/nvidia/usdcode-llama3-70b-instruct",
-        "nvidia_nim/nvidia/nemotron-4-340b-instruct",
-        "nvidia_nim/meta/codellama-70b",
-        "nvidia_nim/meta/llama2-70b",
-        "nvidia_nim/meta/llama3-8b-instruct",
-        "nvidia_nim/meta/llama3-70b-instruct",
-        "nvidia_nim/meta/llama-3.1-8b-instruct",
-        "nvidia_nim/meta/llama-3.1-70b-instruct",
-        "nvidia_nim/meta/llama-3.1-405b-instruct",
-        "nvidia_nim/meta/llama-3.2-1b-instruct",
-        "nvidia_nim/meta/llama-3.2-3b-instruct",
-        "nvidia_nim/meta/llama-3.2-11b-vision-instruct",
-        "nvidia_nim/meta/llama-3.2-90b-vision-instruct",
-        "nvidia_nim/meta/llama-3.1-70b-instruct",
-        "nvidia_nim/google/gemma-7b",
-        "nvidia_nim/google/gemma-2b",
-        "nvidia_nim/google/codegemma-7b",
-        "nvidia_nim/google/codegemma-1.1-7b",
-        "nvidia_nim/google/recurrentgemma-2b",
-        "nvidia_nim/google/gemma-2-9b-it",
-        "nvidia_nim/google/gemma-2-27b-it",
-        "nvidia_nim/google/gemma-2-2b-it",
-        "nvidia_nim/google/deplot",
-        "nvidia_nim/google/paligemma",
-        "nvidia_nim/mistralai/mistral-7b-instruct-v0.2",
-        "nvidia_nim/mistralai/mixtral-8x7b-instruct-v0.1",
-        "nvidia_nim/mistralai/mistral-large",
-        "nvidia_nim/mistralai/mixtral-8x22b-instruct-v0.1",
-        "nvidia_nim/mistralai/mistral-7b-instruct-v0.3",
-        "nvidia_nim/nv-mistralai/mistral-nemo-12b-instruct",
-        "nvidia_nim/mistralai/mamba-codestral-7b-v0.1",
-        "nvidia_nim/microsoft/phi-3-mini-128k-instruct",
-        "nvidia_nim/microsoft/phi-3-mini-4k-instruct",
-        "nvidia_nim/microsoft/phi-3-small-8k-instruct",
-        "nvidia_nim/microsoft/phi-3-small-128k-instruct",
-        "nvidia_nim/microsoft/phi-3-medium-4k-instruct",
-        "nvidia_nim/microsoft/phi-3-medium-128k-instruct",
-        "nvidia_nim/microsoft/phi-3.5-mini-instruct",
-        "nvidia_nim/microsoft/phi-3.5-moe-instruct",
-        "nvidia_nim/microsoft/kosmos-2",
-        "nvidia_nim/microsoft/phi-3-vision-128k-instruct",
-        "nvidia_nim/microsoft/phi-3.5-vision-instruct",
-        "nvidia_nim/databricks/dbrx-instruct",
-        "nvidia_nim/snowflake/arctic",
-        "nvidia_nim/aisingapore/sea-lion-7b-instruct",
-        "nvidia_nim/ibm/granite-8b-code-instruct",
-        "nvidia_nim/ibm/granite-34b-code-instruct",
-        "nvidia_nim/ibm/granite-3.0-8b-instruct",
-        "nvidia_nim/ibm/granite-3.0-3b-a800m-instruct",
-        "nvidia_nim/mediatek/breeze-7b-instruct",
-        "nvidia_nim/upstage/solar-10.7b-instruct",
-        "nvidia_nim/writer/palmyra-med-70b-32k",
-        "nvidia_nim/writer/palmyra-med-70b",
-        "nvidia_nim/writer/palmyra-fin-70b-32k",
-        "nvidia_nim/01-ai/yi-large",
-        "nvidia_nim/deepseek-ai/deepseek-coder-6.7b-instruct",
-        "nvidia_nim/rakuten/rakutenai-7b-instruct",
-        "nvidia_nim/rakuten/rakutenai-7b-chat",
-        "nvidia_nim/baichuan-inc/baichuan2-13b-chat",
-    ],
     "groq": [
         "groq/llama-3.1-8b-instant",
         "groq/llama-3.1-70b-versatile",
@@ -239,23 +156,8 @@ MODELS = {
         "bedrock/mistral.mistral-7b-instruct-v0:2",
         "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
     ],
-    "sambanova": [
-        "sambanova/Meta-Llama-3.3-70B-Instruct",
-        "sambanova/QwQ-32B-Preview",
-        "sambanova/Qwen2.5-72B-Instruct",
-        "sambanova/Qwen2.5-Coder-32B-Instruct",
-        "sambanova/Meta-Llama-3.1-405B-Instruct",
-        "sambanova/Meta-Llama-3.1-70B-Instruct",
-        "sambanova/Meta-Llama-3.1-8B-Instruct",
-        "sambanova/Llama-3.2-90B-Vision-Instruct",
-        "sambanova/Llama-3.2-11B-Vision-Instruct",
-        "sambanova/Meta-Llama-3.2-3B-Instruct",
-        "sambanova/Meta-Llama-3.2-1B-Instruct",
-    ],
 }
 
-DEFAULT_LLM_MODEL = "gpt-4o-mini"
-
 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
 
 

src/crewai/cli/crew_chat.py

@@ -1,536 +0,0 @@
-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():
-    """
-    Runs an interactive chat loop using the Crew's chat LLM with function calling.
-    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",
-    )
-
-    # Start loading indicator
-    loading_complete = threading.Event()
-    loading_thread = threading.Thread(target=show_loading, args=(loading_complete,))
-    loading_thread.start()
-
-    try:
-        crew_chat_inputs = generate_crew_chat_inputs(crew, crew_name, chat_llm)
-        crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
-        system_message = build_system_message(crew_chat_inputs)
-
-        # Call the LLM to generate the introductory message
-        introductory_message = chat_llm.call(
-            messages=[{"role": "system", "content": system_message}]
-        )
-    finally:
-        # Stop loading indicator
-        loading_complete.set()
-        loading_thread.join()
-
-    # Indicate that the analysis is complete
-    click.secho("\nFinished analyzing crew.\n", fg="white")
-
-    click.secho(f"Assistant: {introductory_message}\n", fg="green")
-
-    messages = [
-        {"role": "system", "content": system_message},
-        {"role": "assistant", "content": introductory_message},
-    ]
-
-    available_functions = {
-        crew_chat_inputs.crew_name: create_tool_function(crew, messages),
-    }
-
-    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:
-        return create_llm(crew.chat_llm)
-    except Exception as e:
-        click.secho(
-            f"Unable to find a Chat LLM. Please make sure you set chat_llm on the crew: {e}",
-            fg="red",
-        )
-        return None
-
-
-def build_system_message(crew_chat_inputs: ChatInputs) -> str:
-    """Builds the initial system message for the chat."""
-    required_fields_str = (
-        ", ".join(
-            f"{field.name} (desc: {field.description or 'n/a'})"
-            for field in crew_chat_inputs.inputs
-        )
-        or "(No required fields detected)"
-    )
-
-    return (
-        "You are a helpful AI assistant for the CrewAI platform. "
-        "Your primary purpose is to assist users with the crew's specific tasks. "
-        "You can answer general questions, but should guide users back to the crew's purpose afterward. "
-        "For example, after answering a general question, remind the user of your main purpose, such as generating a research report, and prompt them to specify a topic or task related to the crew's purpose. "
-        "You have a function (tool) you can call by name if you have all required inputs. "
-        f"Those required inputs are: {required_fields_str}. "
-        "Once you have them, call the function. "
-        "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. "
-        "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}"
-        f"\nCrew Description: {crew_chat_inputs.crew_description}"
-    )
-
-
-def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
-    """Creates a wrapper function for running the crew tool with messages."""
-
-    def run_crew_tool_with_messages(**kwargs):
-        return run_crew_tool(crew, messages, **kwargs)
-
-    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 = get_user_input()
-            handle_user_input(
-                user_input, chat_llm, messages, crew_tool_schema, available_functions
-            )
-
-        except KeyboardInterrupt:
-            click.echo("\nExiting chat. Goodbye!")
-            break
-        except Exception as e:
-            click.secho(f"An error occurred: {e}", fg="red")
-            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.
-
-    crew_name: The name of the crew (used for the function 'name').
-    crew_inputs: A ChatInputs object containing crew_description
-                 and a list of input fields (each with a name & description).
-    """
-    properties = {}
-    for field in crew_inputs.inputs:
-        properties[field.name] = {
-            "type": "string",
-            "description": field.description or "No description provided",
-        }
-
-    required_fields = [field.name for field in crew_inputs.inputs]
-
-    return {
-        "type": "function",
-        "function": {
-            "name": crew_inputs.crew_name,
-            "description": crew_inputs.crew_description or "No crew description",
-            "parameters": {
-                "type": "object",
-                "properties": properties,
-                "required": required_fields,
-            },
-        },
-    }
-
-
-def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
-    """
-    Runs the crew using crew.kickoff(inputs=kwargs) and returns the output.
-
-    Args:
-        crew (Crew): The crew instance to run.
-        messages (List[Dict[str, str]]): The chat messages up to this point.
-        **kwargs: The inputs collected from the user.
-
-    Returns:
-        str: The output from the crew's execution.
-
-    Raises:
-        SystemExit: Exits the chat if an error occurs during crew execution.
-    """
-    try:
-        # Serialize 'messages' to JSON string before adding to kwargs
-        kwargs["crew_chat_messages"] = json.dumps(messages)
-
-        # Run the crew with the provided inputs
-        crew_output = crew.kickoff(inputs=kwargs)
-
-        # Convert CrewOutput to a string to send back to the user
-        result = str(crew_output)
-
-        return result
-    except Exception as e:
-        # Exit the chat and show the error message
-        click.secho("An error occurred while running the crew:", fg="red")
-        click.secho(str(e), fg="red")
-        sys.exit(1)
-
-
-def load_crew_and_name() -> Tuple[Crew, str]:
-    """
-    Loads the crew by importing the crew class from the user's project.
-
-    Returns:
-        Tuple[Crew, str]: A tuple containing the Crew instance and the name of the crew.
-    """
-    # Get the current working directory
-    cwd = Path.cwd()
-
-    # Path to the pyproject.toml file
-    pyproject_path = cwd / "pyproject.toml"
-    if not pyproject_path.exists():
-        raise FileNotFoundError("pyproject.toml not found in the current directory.")
-
-    # Load the pyproject.toml file using 'tomli'
-    with pyproject_path.open("rb") as f:
-        pyproject_data = tomli.load(f)
-
-    # Get the project name from the 'project' section
-    project_name = pyproject_data["project"]["name"]
-    folder_name = project_name
-
-    # Derive the crew class name from the project name
-    # E.g., if project_name is 'my_project', crew_class_name is 'MyProject'
-    crew_class_name = project_name.replace("_", " ").title().replace(" ", "")
-
-    # Add the 'src' directory to sys.path
-    src_path = cwd / "src"
-    if str(src_path) not in sys.path:
-        sys.path.insert(0, str(src_path))
-
-    # Import the crew module
-    crew_module_name = f"{folder_name}.crew"
-    try:
-        crew_module = __import__(crew_module_name, fromlist=[crew_class_name])
-    except ImportError as e:
-        raise ImportError(f"Failed to import crew module {crew_module_name}: {e}")
-
-    # Get the crew class from the module
-    try:
-        crew_class = getattr(crew_module, crew_class_name)
-    except AttributeError:
-        raise AttributeError(
-            f"Crew class {crew_class_name} not found in module {crew_module_name}"
-        )
-
-    # Instantiate the crew
-    crew_instance = crew_class().crew()
-    return crew_instance, crew_class_name
-
-
-def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInputs:
-    """
-    Generates the ChatInputs required for the crew by analyzing the tasks and agents.
-
-    Args:
-        crew (Crew): The crew object containing tasks and agents.
-        crew_name (str): The name of the crew.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        ChatInputs: An object containing the crew's name, description, and input fields.
-    """
-    # Extract placeholders from tasks and agents
-    required_inputs = fetch_required_inputs(crew)
-
-    # Generate descriptions for each input using AI
-    input_fields = []
-    for input_name in required_inputs:
-        description = generate_input_description_with_ai(input_name, crew, chat_llm)
-        input_fields.append(ChatInputField(name=input_name, description=description))
-
-    # Generate crew description using AI
-    crew_description = generate_crew_description_with_ai(crew, chat_llm)
-
-    return ChatInputs(
-        crew_name=crew_name, crew_description=crew_description, inputs=input_fields
-    )
-
-
-def fetch_required_inputs(crew: Crew) -> Set[str]:
-    """
-    Extracts placeholders from the crew's tasks and agents.
-
-    Args:
-        crew (Crew): The crew object.
-
-    Returns:
-        Set[str]: A set of placeholder names.
-    """
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-    required_inputs: Set[str] = set()
-
-    # Scan tasks
-    for task in crew.tasks:
-        text = f"{task.description or ''} {task.expected_output or ''}"
-        required_inputs.update(placeholder_pattern.findall(text))
-
-    # Scan agents
-    for agent in crew.agents:
-        text = f"{agent.role or ''} {agent.goal or ''} {agent.backstory or ''}"
-        required_inputs.update(placeholder_pattern.findall(text))
-
-    return required_inputs
-
-
-def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) -> str:
-    """
-    Generates an input description using AI based on the context of the crew.
-
-    Args:
-        input_name (str): The name of the input placeholder.
-        crew (Crew): The crew object.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        str: A concise description of the input.
-    """
-    # Gather context from tasks and agents where the input is used
-    context_texts = []
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-
-    for task in crew.tasks:
-        if (
-            f"{{{input_name}}}" in task.description
-            or f"{{{input_name}}}" in task.expected_output
-        ):
-            # Replace placeholders with input names
-            task_description = placeholder_pattern.sub(
-                lambda m: m.group(1), task.description or ""
-            )
-            expected_output = placeholder_pattern.sub(
-                lambda m: m.group(1), task.expected_output or ""
-            )
-            context_texts.append(f"Task Description: {task_description}")
-            context_texts.append(f"Expected Output: {expected_output}")
-    for agent in crew.agents:
-        if (
-            f"{{{input_name}}}" in agent.role
-            or f"{{{input_name}}}" in agent.goal
-            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_backstory = placeholder_pattern.sub(
-                lambda m: m.group(1), agent.backstory or ""
-            )
-            context_texts.append(f"Agent Role: {agent_role}")
-            context_texts.append(f"Agent Goal: {agent_goal}")
-            context_texts.append(f"Agent Backstory: {agent_backstory}")
-
-    context = "\n".join(context_texts)
-    if not context:
-        # If no context is found for the input, raise an exception as per instruction
-        raise ValueError(f"No context found for input '{input_name}'.")
-
-    prompt = (
-        f"Based on the following context, write a concise description (15 words or less) of the input '{input_name}'.\n"
-        "Provide only the description, without any extra text or labels. Do not include placeholders like '{topic}' in the description.\n"
-        "Context:\n"
-        f"{context}"
-    )
-    response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    description = response.strip()
-
-    return description
-
-
-def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
-    """
-    Generates a brief description of the crew using AI.
-
-    Args:
-        crew (Crew): The crew object.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        str: A concise description of the crew's purpose (15 words or less).
-    """
-    # Gather context from tasks and agents
-    context_texts = []
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-
-    for task in crew.tasks:
-        # Replace placeholders with input names
-        task_description = placeholder_pattern.sub(
-            lambda m: m.group(1), task.description or ""
-        )
-        expected_output = placeholder_pattern.sub(
-            lambda m: m.group(1), task.expected_output or ""
-        )
-        context_texts.append(f"Task Description: {task_description}")
-        context_texts.append(f"Expected Output: {expected_output}")
-    for agent in crew.agents:
-        # Replace placeholders with input names
-        agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role or "")
-        agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal or "")
-        agent_backstory = placeholder_pattern.sub(
-            lambda m: m.group(1), agent.backstory or ""
-        )
-        context_texts.append(f"Agent Role: {agent_role}")
-        context_texts.append(f"Agent Goal: {agent_goal}")
-        context_texts.append(f"Agent Backstory: {agent_backstory}")
-
-    context = "\n".join(context_texts)
-    if not context:
-        raise ValueError("No context found for generating crew description.")
-
-    prompt = (
-        "Based on the following context, write a concise, action-oriented description (15 words or less) of the crew's purpose.\n"
-        "Provide only the description, without any extra text or labels. Do not include placeholders like '{topic}' in the description.\n"
-        "Context:\n"
-        f"{context}"
-    )
-    response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    crew_description = response.strip()
-
-    return crew_description

src/crewai/cli/provider.py

@@ -5,6 +5,7 @@ from pathlib import Path
 
 import click
 import requests
+from typing import Any
 
 from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS
 
@@ -192,7 +193,7 @@ def download_data(response):
     data_chunks = []
     with click.progressbar(
         length=total_size, label="Downloading", show_pos=True
-    ) as progress_bar:
+    ) as progress_bar:  # type: Any
         for chunk in response.iter_content(block_size):
             if chunk:
                 data_chunks.append(chunk)

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

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

src/crewai/cli/templates/crew/config/tasks.yaml

@@ -2,7 +2,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is {current_year}.
+    the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher

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

@@ -2,8 +2,6 @@
 import sys
 import warnings
 
-from datetime import datetime
-
 from {{folder_name}}.crew import {{crew_name}}
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
@@ -18,14 +16,9 @@ def run():
     Run the crew.
     """
     inputs = {
-        'topic': 'AI LLMs',
-        'current_year': str(datetime.now().year)
+        'topic': 'AI LLMs'
     }
-    
-    try:
-        {{crew_name}}().crew().kickoff(inputs=inputs)
-    except Exception as e:
-        raise Exception(f"An error occurred while running the crew: {e}")
+    {{crew_name}}().crew().kickoff(inputs=inputs)
 
 
 def train():
@@ -62,4 +55,4 @@ def test():
         {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
 
     except Exception as e:
-        raise Exception(f"An error occurred while testing the crew: {e}")
+        raise Exception(f"An error occurred while replaying the crew: {e}")

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]>=0.98.0,<1.0.0"
+    "crewai[tools]>=0.86.0,<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]>=0.98.0,<1.0.0",
+    "crewai[tools]>=0.86.0,<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]>=0.98.0"
+    "crewai[tools]>=0.86.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -1,11 +1,12 @@
 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, Set, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from crewai.tools.base_tool import BaseTool
 
 from pydantic import (
     UUID4,
@@ -46,7 +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
@@ -83,7 +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
@@ -150,7 +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)
@@ -206,10 +205,6 @@ class Crew(BaseModel):
         default=None,
         description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.",
     )
-    chat_llm: Optional[Any] = Field(
-        default=None,
-        description="LLM used to handle chatting with the crew.",
-    )
     _knowledge: Optional[Knowledge] = PrivateAttr(
         default=None,
     )
@@ -246,9 +241,15 @@ 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
@@ -513,8 +514,6 @@ class Crew(BaseModel):
         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."""
@@ -676,7 +675,6 @@ class Crew(BaseModel):
         else:
             self.manager_llm = (
                 getattr(self.manager_llm, "model_name", None)
-                or getattr(self.manager_llm, "model", None)
                 or getattr(self.manager_llm, "deployment_name", None)
                 or self.manager_llm
             )
@@ -732,7 +730,7 @@ class Crew(BaseModel):
             tools_for_task = task.tools or agent_to_use.tools or []
             tools_for_task = self._prepare_tools(agent_to_use, task, tools_for_task)
 
-            self._log_task_start(task, agent_to_use.role)
+            self._log_task_start(task, agent_to_use.role if agent_to_use and agent_to_use.role else "")
 
             if isinstance(task, ConditionalTask):
                 skipped_task_output = self._handle_conditional_task(
@@ -798,8 +796,8 @@ class Crew(BaseModel):
         return None
 
     def _prepare_tools(
-        self, agent: BaseAgent, task: Task, tools: List[Tool]
-    ) -> List[Tool]:
+        self, agent: BaseAgent, task: Task, tools: List[Union[Tool, BaseTool]]
+    ) -> List[Union[Tool, BaseTool]]:
         # Add delegation tools if agent allows delegation
         if agent.allow_delegation:
             if self.process == Process.hierarchical:
@@ -828,8 +826,8 @@ class Crew(BaseModel):
         return task.agent
 
     def _merge_tools(
-        self, existing_tools: List[Tool], new_tools: List[Tool]
-    ) -> List[Tool]:
+        self, existing_tools: List[Union[Tool, BaseTool]], new_tools: List[Union[Tool, BaseTool]]
+    ) -> List[Union[Tool, BaseTool]]:
         """Merge new tools into existing tools list, avoiding duplicates by tool name."""
         if not new_tools:
             return existing_tools
@@ -995,31 +993,6 @@ class Crew(BaseModel):
             return self._knowledge.query(query)
         return None
 
-    def fetch_inputs(self) -> Set[str]:
-        """
-        Gathers placeholders (e.g., {something}) referenced in tasks or agents.
-        Scans each task's 'description' + 'expected_output', and each agent's
-        'role', 'goal', and 'backstory'.
-
-        Returns a set of all discovered placeholder names.
-        """
-        placeholder_pattern = re.compile(r"\{(.+?)\}")
-        required_inputs: Set[str] = set()
-
-        # Scan tasks for inputs
-        for task in self.tasks:
-            # description and expected_output might contain e.g. {topic}, {user_name}, etc.
-            text = f"{task.description or ''} {task.expected_output or ''}"
-            required_inputs.update(placeholder_pattern.findall(text))
-
-        # Scan agents for inputs
-        for agent in self.agents:
-            # role, goal, backstory might have placeholders like {role_detail}, etc.
-            text = f"{agent.role or ''} {agent.goal or ''} {agent.backstory or ''}"
-            required_inputs.update(placeholder_pattern.findall(text))
-
-        return required_inputs
-
     def copy(self):
         """Create a deep copy of the Crew."""
 
@@ -1075,7 +1048,7 @@ class Crew(BaseModel):
     def _interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
         """Interpolates the inputs in the tasks and agents."""
         [
-            task.interpolate_inputs_and_add_conversation_history(
+            task.interpolate_inputs(
                 # type: ignore # "interpolate_inputs" of "Task" does not return a value (it only ever returns None)
                 inputs
             )

src/crewai/crews/crew_output.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, Optional
+from typing import Any, Callable, Dict, Optional, Union
 
 from pydantic import BaseModel, Field
 
@@ -23,14 +23,25 @@ class CrewOutput(BaseModel):
     )
     token_usage: UsageMetrics = Field(description="Processed token summary", default={})
 
-    @property
-    def json(self) -> Optional[str]:
+    def json(
+        self,
+        *,
+        include: Union[set[str], None] = None,
+        exclude: Union[set[str], None] = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        encoder: Optional[Callable[[Any], Any]] = None,
+        models_as_dict: bool = True,
+        **dumps_kwargs: Any,
+    ) -> 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)
+        return json.dumps(self.json_dict, default=encoder, **dumps_kwargs)
 
     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:
@@ -578,158 +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(
@@ -737,11 +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:
@@ -984,30 +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/flow_visualizer.py

@@ -106,7 +106,12 @@ class FlowPlot:
 
             # Add nodes to the network
             try:
-                add_nodes_to_network(net, self.flow, node_positions, self.node_styles)
+                add_nodes_to_network(
+                    net,
+                    flow=self.flow,
+                    pos=node_positions,
+                    node_styles=self.node_styles
+                )
             except Exception as e:
                 raise RuntimeError(f"Failed to add nodes to network: {str(e)}")
 

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/source/crew_docling_source.py

@@ -2,17 +2,11 @@ from pathlib import Path
 from typing import Iterator, List, Optional, Union
 from urllib.parse import urlparse
 
-try:
-    from docling.datamodel.base_models import InputFormat
-    from docling.document_converter import DocumentConverter
-    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
-
+from docling.datamodel.base_models import InputFormat
+from docling.document_converter import DocumentConverter
+from docling.exceptions import ConversionError
+from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker
+from docling_core.types.doc.document import DoclingDocument
 from pydantic import Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
@@ -25,22 +19,14 @@ class CrewDoclingSource(BaseKnowledgeSource):
     This will auto support PDF, DOCX, and TXT, XLSX, Images, and HTML files without any additional dependencies and follows the docling package as the source of truth.
     """
 
-    def __init__(self, *args, **kwargs):
-        if not DOCLING_AVAILABLE:
-            raise ImportError(
-                "The docling package is required to use CrewDoclingSource. "
-                "Please install it using: uv add docling"
-            )
-        super().__init__(*args, **kwargs)
-
     _logger: Logger = Logger(verbose=True)
 
     file_path: Optional[List[Union[Path, str]]] = Field(default=None)
     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 +52,7 @@ 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 +74,11 @@ class CrewDoclingSource(BaseKnowledgeSource):
             self.chunks.extend(list(new_chunks_iterable))
         self._save_documents()
 
-    def _convert_source_to_docling_documents(self) -> List["DoclingDocument"]:
+    def _convert_source_to_docling_documents(self) -> List[DoclingDocument]:
         conv_results_iter = self.document_converter.convert_all(self.safe_file_paths)
         return [result.document for result in conv_results_iter]
 
-    def _chunk_doc(self, doc: "DoclingDocument") -> Iterator[str]:
+    def _chunk_doc(self, doc: DoclingDocument) -> Iterator[str]:
         chunker = HierarchicalChunker()
         for chunk in chunker.chunk(doc):
             yield chunk.text

src/crewai/llm.py

@@ -1,27 +1,22 @@
-import json
 import logging
 import os
 import sys
 import threading
 import warnings
 from contextlib import contextmanager
-from typing import Any, Dict, List, Optional, Union, cast
+from typing import Any, Dict, List, Optional, Union
 
-from dotenv import load_dotenv
+from pydantic import BaseModel, Field
 
 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 import get_supported_openai_params
 
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
 
-load_dotenv()
-
 
 class FilteredStream:
     def __init__(self, original_stream):
@@ -30,7 +25,6 @@ class FilteredStream:
 
     def write(self, s) -> int:
         with self._lock:
-            # Filter out extraneous messages from LiteLLM
             if (
                 "Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new"
                 in s
@@ -76,18 +70,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "mixtral-8x7b-32768": 32768,
     "llama-3.3-70b-versatile": 128000,
     "llama-3.3-70b-instruct": 128000,
-    # sambanova
-    "Meta-Llama-3.3-70B-Instruct": 131072,
-    "QwQ-32B-Preview": 8192,
-    "Qwen2.5-72B-Instruct": 8192,
-    "Qwen2.5-Coder-32B-Instruct": 8192,
-    "Meta-Llama-3.1-405B-Instruct": 8192,
-    "Meta-Llama-3.1-70B-Instruct": 131072,
-    "Meta-Llama-3.1-8B-Instruct": 131072,
-    "Llama-3.2-90B-Vision-Instruct": 16384,
-    "Llama-3.2-11B-Vision-Instruct": 16384,
-    "Meta-Llama-3.2-3B-Instruct": 4096,
-    "Meta-Llama-3.2-1B-Instruct": 16384,
 }
 
 DEFAULT_CONTEXT_WINDOW_SIZE = 8192
@@ -98,26 +80,48 @@ CONTEXT_WINDOW_USAGE_RATIO = 0.75
 def suppress_warnings():
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore")
-        warnings.filterwarnings(
-            "ignore", message="open_text is deprecated*", category=DeprecationWarning
-        )
 
         # Redirect stdout and stderr
         old_stdout = sys.stdout
         old_stderr = sys.stderr
         sys.stdout = FilteredStream(old_stdout)
         sys.stderr = FilteredStream(old_stderr)
+
         try:
             yield
         finally:
+            # Restore stdout and stderr
             sys.stdout = old_stdout
             sys.stderr = old_stderr
 
 
-class LLM:
+class LLM(BaseModel):
+    model: str = "gpt-4"  # Set default model
+    timeout: Optional[Union[float, int]] = None
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    n: Optional[int] = None
+    stop: Optional[Union[str, List[str]]] = None
+    max_completion_tokens: Optional[int] = None
+    max_tokens: Optional[int] = None
+    presence_penalty: Optional[float] = None
+    frequency_penalty: Optional[float] = None
+    logit_bias: Optional[Dict[int, float]] = None
+    response_format: Optional[Dict[str, Any]] = None
+    seed: Optional[int] = None
+    logprobs: Optional[bool] = None
+    top_logprobs: Optional[int] = None
+    base_url: Optional[str] = None
+    api_version: Optional[str] = None
+    api_key: Optional[str] = None
+    callbacks: Optional[List[Any]] = None
+    context_window_size: Optional[int] = None
+    kwargs: Dict[str, Any] = Field(default_factory=dict)
+    logger: Optional[logging.Logger] = Field(default_factory=lambda: logging.getLogger(__name__))
+
     def __init__(
         self,
-        model: str,
+        model: Optional[Union[str, 'LLM']] = "gpt-4",
         timeout: Optional[Union[float, int]] = None,
         temperature: Optional[float] = None,
         top_p: Optional[float] = None,
@@ -130,217 +134,422 @@ class LLM:
         logit_bias: Optional[Dict[int, float]] = None,
         response_format: Optional[Dict[str, Any]] = None,
         seed: Optional[int] = None,
-        logprobs: Optional[int] = None,
+        logprobs: Optional[bool] = None,
         top_logprobs: Optional[int] = None,
         base_url: Optional[str] = None,
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
-        callbacks: List[Any] = [],
-    ):
-        self.model = model
-        self.timeout = timeout
-        self.temperature = temperature
-        self.top_p = top_p
-        self.n = n
-        self.max_completion_tokens = max_completion_tokens
-        self.max_tokens = max_tokens
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.logit_bias = logit_bias
-        self.response_format = response_format
-        self.seed = seed
-        self.logprobs = logprobs
-        self.top_logprobs = top_logprobs
-        self.base_url = base_url
-        self.api_version = api_version
-        self.api_key = api_key
-        self.callbacks = callbacks
-        self.context_window_size = 0
+        callbacks: Optional[List[Any]] = None,
+        context_window_size: Optional[int] = None,
+        **kwargs: Any,
+    ) -> None:
+        # Initialize with default values
+        init_dict = {
+            "model": model if isinstance(model, str) else "gpt-4",
+            "timeout": timeout,
+            "temperature": temperature,
+            "top_p": top_p,
+            "n": n,
+            "stop": stop,
+            "max_completion_tokens": max_completion_tokens,
+            "max_tokens": max_tokens,
+            "presence_penalty": presence_penalty,
+            "frequency_penalty": frequency_penalty,
+            "logit_bias": logit_bias,
+            "response_format": response_format,
+            "seed": seed,
+            "logprobs": logprobs,
+            "top_logprobs": top_logprobs,
+            "base_url": base_url,
+            "api_version": api_version,
+            "api_key": api_key,
+            "callbacks": callbacks,
+            "context_window_size": context_window_size,
+            "kwargs": kwargs,
+        }
+        super().__init__(**init_dict)
+        
+        # Initialize model with default value
+        self.model = "gpt-4"  # Default fallback
+        
+        # Extract and validate model name
+        if isinstance(model, LLM):
+            # Extract and validate model name from LLM instance
+            if hasattr(model, 'model'):
+                if isinstance(model.model, str):
+                    self.model = model.model
+                else:
+                    # Try to extract string model name from nested LLM
+                    if isinstance(model.model, LLM):
+                        self.model = str(model.model.model) if hasattr(model.model, 'model') else "gpt-4"
+                    else:
+                        self.model = "gpt-4"
+                        if self.logger:
+                            self.logger.warning("Nested LLM model is not a string, using default: gpt-4")
+            else:
+                self.model = "gpt-4"
+                if self.logger:
+                    self.logger.warning("LLM instance has no model attribute, using default: gpt-4")
+        else:
+            # Extract and validate model name for non-LLM instances
+            if not isinstance(model, str):
+                if self.logger:
+                    self.logger.debug(f"Model is not a string, attempting to extract name. Type: {type(model)}")
+                if model is not None:
+                    if hasattr(model, 'model_name'):
+                        model_name = getattr(model, 'model_name', None)
+                        self.model = str(model_name) if model_name is not None else "gpt-4"
+                    elif hasattr(model, 'model'):
+                        model_attr = getattr(model, 'model', None)
+                        self.model = str(model_attr) if model_attr is not None else "gpt-4"
+                    elif hasattr(model, '_model_name'):
+                        model_name = getattr(model, '_model_name', None)
+                        self.model = str(model_name) if model_name is not None else "gpt-4"
+                    else:
+                        self.model = "gpt-4"  # Default fallback
+                        if self.logger:
+                            self.logger.warning(f"Could not extract model name from {type(model)}, using default: {self.model}")
+                else:
+                    self.model = "gpt-4"  # Default fallback for None
+                    if self.logger:
+                        self.logger.warning("Model is None, using default: gpt-4")
+            else:
+                self.model = str(model)  # Ensure it's a string
+
+        # If model is an LLM instance, copy its configuration
+        if isinstance(model, LLM):
+            # Extract and validate model name first
+            if hasattr(model, 'model'):
+                if isinstance(model.model, str):
+                    self.model = model.model
+                else:
+                    # Try to extract string model name from nested LLM
+                    if isinstance(model.model, LLM):
+                        self.model = str(model.model.model) if hasattr(model.model, 'model') else "gpt-4"
+                    else:
+                        self.model = "gpt-4"
+                        if self.logger:
+                            self.logger.warning("Nested LLM model is not a string, using default: gpt-4")
+            else:
+                self.model = "gpt-4"
+                if self.logger:
+                    self.logger.warning("LLM instance has no model attribute, using default: gpt-4")
+
+            # Copy other configuration
+            self.timeout = model.timeout
+            self.temperature = model.temperature
+            self.top_p = model.top_p
+            self.n = model.n
+            self.stop = model.stop
+            self.max_completion_tokens = model.max_completion_tokens
+            self.max_tokens = model.max_tokens
+            self.presence_penalty = model.presence_penalty
+            self.frequency_penalty = model.frequency_penalty
+            self.logit_bias = model.logit_bias
+            self.response_format = model.response_format
+            self.seed = model.seed
+            self.logprobs = model.logprobs
+            self.top_logprobs = model.top_logprobs
+            self.base_url = model.base_url
+            self.api_version = model.api_version
+            self.api_key = model.api_key
+            self.callbacks = model.callbacks
+            self.context_window_size = model.context_window_size
+            self.kwargs = model.kwargs
+
+            # Final validation of model name
+            if not isinstance(self.model, str):
+                self.model = "gpt-4"
+                if self.logger:
+                    self.logger.warning("Model name is still not a string after LLM copy, using default: gpt-4")
+        else:
+            # Extract and validate model name for non-LLM instances
+            if not isinstance(model, str):
+                if self.logger:
+                    self.logger.debug(f"Model is not a string, attempting to extract name. Type: {type(model)}")
+                if model is not None:
+                    if hasattr(model, 'model_name'):
+                        model_name = getattr(model, 'model_name', None)
+                        self.model = str(model_name) if model_name is not None else "gpt-4"
+                    elif hasattr(model, 'model'):
+                        model_attr = getattr(model, 'model', None)
+                        self.model = str(model_attr) if model_attr is not None else "gpt-4"
+                    elif hasattr(model, '_model_name'):
+                        model_name = getattr(model, '_model_name', None)
+                        self.model = str(model_name) if model_name is not None else "gpt-4"
+                    else:
+                        self.model = "gpt-4"  # Default fallback
+                        if self.logger:
+                            self.logger.warning(f"Could not extract model name from {type(model)}, using default: {self.model}")
+                else:
+                    self.model = "gpt-4"  # Default fallback for None
+                    if self.logger:
+                        self.logger.warning("Model is None, using default: gpt-4")
+            else:
+                self.model = str(model)  # Ensure it's a string
+
+            # Final validation
+            if not isinstance(self.model, str):
+                self.model = "gpt-4"
+                if self.logger:
+                    self.logger.warning("Model name is still not a string after extraction, using default: gpt-4")
+
+            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
+            self.frequency_penalty = frequency_penalty
+            self.logit_bias = logit_bias
+            self.response_format = response_format
+            self.seed = seed
+            self.logprobs = logprobs
+            self.top_logprobs = top_logprobs
+            self.base_url = base_url
+            self.api_version = api_version
+            self.api_key = api_key
+            self.callbacks = callbacks
+            self.context_window_size = 0
+            self.kwargs = kwargs
+
+            # Ensure model is a string after initialization
+            if not isinstance(self.model, str):
+                self.model = "gpt-4"
+                self.logger.warning(f"Model is still not a string after initialization, using default: {self.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 call(
         self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
+        messages: List[Dict[str, str]],
+        callbacks: Optional[List[Any]] = None
     ) -> str:
-        """
-        High-level llm call method that:
-          1) Accepts either a string or a list of messages
-          2) Converts string input to the required message format
-          3) Calls litellm.completion
-          4) Handles function/tool calls if any
-          5) Returns the final text response or tool result
-
-        Parameters:
-        - messages (Union[str, List[Dict[str, str]]]): The input messages for the LLM.
-          - If a string is provided, it will be converted into a message list with a single entry.
-          - If a list of dictionaries is provided, each dictionary should have 'role' and 'content' keys.
-        - tools (Optional[List[dict]]): A list of tool schemas for function calling.
-        - callbacks (Optional[List[Any]]): A list of callback functions to be executed.
-        - available_functions (Optional[Dict[str, Any]]): A dictionary mapping function names to actual Python functions.
-
-        Returns:
-        - str: The final text response from the LLM or the result of a tool function call.
-
-        Examples:
-        ---------
-        # Example 1: Using a string input
-        response = llm.call("Return the name of a random city in the world.")
-        print(response)
-
-        # Example 2: Using a list of messages
-        messages = [{"role": "user", "content": "What is the capital of France?"}]
-        response = llm.call(messages)
-        print(response)
-        """
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
+            # Store original model to restore later
+            original_model = self.model
+
             try:
-                # --- 1) Prepare the parameters for the completion call
+                # Ensure model is a string before making the call
+                if not isinstance(self.model, str):
+                    if self.logger:
+                        self.logger.warning(f"Model is not a string in call method: {type(self.model)}. Attempting to convert...")
+                    if isinstance(self.model, LLM):
+                        self.model = self.model.model if isinstance(self.model.model, str) else "gpt-4"
+                    elif hasattr(self.model, 'model_name'):
+                        self.model = str(self.model.model_name)
+                    elif hasattr(self.model, 'model'):
+                        if isinstance(self.model.model, str):
+                            self.model = str(self.model.model)
+                        elif hasattr(self.model.model, 'model_name'):
+                            self.model = str(self.model.model.model_name)
+                        else:
+                            self.model = "gpt-4"
+                            if self.logger:
+                                self.logger.warning("Could not extract model name from nested model object, using default: gpt-4")
+                    else:
+                        self.model = "gpt-4"
+                        if self.logger:
+                            self.logger.warning("Could not extract model name, using default: gpt-4")
+                
+                if self.logger:
+                    self.logger.debug(f"Using model: {self.model} (type: {type(self.model)}) for LiteLLM call")
+
+                # Create base params with validated model name
+                # Extract model name string
+                model_name = None
+                if isinstance(self.model, str):
+                    model_name = self.model
+                elif hasattr(self.model, 'model_name'):
+                    model_name = str(self.model.model_name)
+                elif hasattr(self.model, 'model'):
+                    if isinstance(self.model.model, str):
+                        model_name = str(self.model.model)
+                    elif hasattr(self.model.model, 'model_name'):
+                        model_name = str(self.model.model.model_name)
+                
+                if not model_name:
+                    model_name = "gpt-4"
+                    if self.logger:
+                        self.logger.warning("Could not extract model name, using default: gpt-4")
+
                 params = {
-                    "model": self.model,
+                    "model": model_name,
                     "messages": messages,
-                    "timeout": self.timeout,
-                    "temperature": self.temperature,
-                    "top_p": self.top_p,
-                    "n": self.n,
-                    "stop": self.stop,
-                    "max_tokens": self.max_tokens or self.max_completion_tokens,
-                    "presence_penalty": self.presence_penalty,
-                    "frequency_penalty": self.frequency_penalty,
-                    "logit_bias": self.logit_bias,
-                    "response_format": self.response_format,
-                    "seed": self.seed,
-                    "logprobs": self.logprobs,
-                    "top_logprobs": self.top_logprobs,
+                    "stream": False,
+                    "api_key": self.api_key or os.getenv("OPENAI_API_KEY"),
                     "api_base": self.base_url,
                     "api_version": self.api_version,
-                    "api_key": self.api_key,
-                    "stream": False,
-                    "tools": tools,
                 }
+                
+                if self.logger:
+                    self.logger.debug(f"Using model parameters: {params}")
 
-                # Remove None values from params
+                # Add API configuration if available
+                api_key = self.api_key or os.getenv("OPENAI_API_KEY")
+                if api_key:
+                    params["api_key"] = api_key
+
+                # Try to get supported parameters for the model
+                try:
+                    supported_params = get_supported_openai_params(self.model)
+                    optional_params = {}
+                    
+                    if supported_params:
+                        param_mapping = {
+                            "timeout": self.timeout,
+                            "temperature": self.temperature,
+                            "top_p": self.top_p,
+                            "n": self.n,
+                            "stop": self.stop,
+                            "max_tokens": self.max_tokens or self.max_completion_tokens,
+                            "presence_penalty": self.presence_penalty,
+                            "frequency_penalty": self.frequency_penalty,
+                            "logit_bias": self.logit_bias,
+                            "response_format": self.response_format,
+                            "seed": self.seed,
+                            "logprobs": self.logprobs,
+                            "top_logprobs": self.top_logprobs
+                        }
+                        
+                        # Only add parameters that are supported and not None
+                        optional_params = {
+                            k: v for k, v in param_mapping.items()
+                            if k in supported_params and v is not None
+                        }
+                        if "logprobs" in supported_params and self.logprobs is not None:
+                            optional_params["logprobs"] = self.logprobs
+                        if "top_logprobs" in supported_params and self.top_logprobs is not None:
+                            optional_params["top_logprobs"] = self.top_logprobs
+                except Exception as e:
+                    if self.logger:
+                        self.logger.error(f"Failed to get supported params for model {self.model}: {str(e)}")
+                    # If we can't get supported params, just add non-None parameters
+                    param_mapping = {
+                        "timeout": self.timeout,
+                        "temperature": self.temperature,
+                        "top_p": self.top_p,
+                        "n": self.n,
+                        "stop": self.stop,
+                        "max_tokens": self.max_tokens or self.max_completion_tokens,
+                        "presence_penalty": self.presence_penalty,
+                        "frequency_penalty": self.frequency_penalty,
+                        "logit_bias": self.logit_bias,
+                        "response_format": self.response_format,
+                        "seed": self.seed,
+                        "logprobs": self.logprobs,
+                        "top_logprobs": self.top_logprobs
+                    }
+                    optional_params = {k: v for k, v in param_mapping.items() if v is not None}
+
+                # Update params with optional parameters
+                params.update(optional_params)
+                
+                # Add API endpoint configuration if available
+                if self.base_url:
+                    params["api_base"] = self.base_url
+                if self.api_version:
+                    params["api_version"] = self.api_version
+
+                # Final validation of model parameter
+                if not isinstance(params["model"], str):
+                    if self.logger:
+                        self.logger.error(f"Model is still not a string after all conversions: {type(params['model'])}")
+                    params["model"] = "gpt-4"
+                
+                # Update params with non-None optional parameters
+                params.update({k: v for k, v in optional_params.items() if v is not None})
+                
+                # Add any additional kwargs
+                if self.kwargs:
+                    params.update(self.kwargs)
+
+                # Remove None values to avoid passing unnecessary parameters
                 params = {k: v for k, v in params.items() if v is not None}
 
-                # --- 2) Make the completion call
                 response = litellm.completion(**params)
-                response_message = cast(Choices, cast(ModelResponse, response).choices)[
-                    0
-                ].message
-                text_response = response_message.content or ""
-                tool_calls = getattr(response_message, "tool_calls", [])
-
-                # --- 3) Handle callbacks with usage info
-                if callbacks and len(callbacks) > 0:
+                content = response["choices"][0]["message"]["content"]
+                
+                # Extract usage metrics
+                usage = response.get("usage", {})
+                if callbacks:
                     for callback in callbacks:
-                        if hasattr(callback, "log_success_event"):
-                            usage_info = getattr(response, "usage", None)
-                            if usage_info:
-                                callback.log_success_event(
-                                    kwargs=params,
-                                    response_obj={"usage": usage_info},
-                                    start_time=0,
-                                    end_time=0,
-                                )
-
-                # --- 4) If no tool calls, return the text response
-                if not tool_calls or not available_functions:
-                    return text_response
-
-                # --- 5) Handle the tool call
-                tool_call = tool_calls[0]
-                function_name = tool_call.function.name
-
-                if function_name in available_functions:
-                    try:
-                        function_args = json.loads(tool_call.function.arguments)
-                    except json.JSONDecodeError as e:
-                        logging.warning(f"Failed to parse function arguments: {e}")
-                        return text_response
-
-                    fn = available_functions[function_name]
-                    try:
-                        # Call the actual tool function
-                        result = fn(**function_args)
-                        return result
-
-                    except Exception as e:
-                        logging.error(
-                            f"Error executing function '{function_name}': {e}"
-                        )
-                        return text_response
-
-                else:
-                    logging.warning(
-                        f"Tool call requested unknown function '{function_name}'"
-                    )
-                    return text_response
-
+                        if hasattr(callback, "update_token_usage"):
+                            callback.update_token_usage(usage)
+                
+                return content
             except Exception as e:
                 if not LLMContextLengthExceededException(
                     str(e)
                 )._is_context_limit_error(str(e)):
                     logging.error(f"LiteLLM call failed: {str(e)}")
-                raise
+                raise  # Re-raise the exception after logging
+            finally:
+                # Always restore the original model object
+                self.model = original_model
 
     def supports_function_calling(self) -> bool:
+        """Check if the LLM supports function calling.
+        
+        Returns:
+            bool: True if the model supports function calling, False otherwise
+        """
         try:
             params = get_supported_openai_params(model=self.model)
             return "response_format" in params
         except Exception as e:
-            logging.error(f"Failed to get supported params: {str(e)}")
+            if self.logger:
+                self.logger.error(f"Failed to get supported params: {str(e)}")
             return False
 
     def supports_stop_words(self) -> bool:
+        """Check if the LLM supports stop words.
+        Returns False if the LLM is not properly initialized."""
+        if not hasattr(self, 'model') or self.model is None:
+            return False
         try:
             params = get_supported_openai_params(model=self.model)
             return "stop" in params
         except Exception as e:
-            logging.error(f"Failed to get supported params: {str(e)}")
+            if self.logger:
+                self.logger.error(f"Failed to get supported params: {str(e)}")
             return False
 
     def get_context_window_size(self) -> int:
+        """Get the context window size for the current model.
+        
+        Returns:
+            int: The context window size in tokens
         """
-        Returns the context window size, using 75% of the maximum to avoid
-        cutting off messages mid-thread.
-        """
-        if self.context_window_size != 0:
-            return self.context_window_size
+        # Only using 75% of the context window size to avoid cutting the message in the middle
+        if self.context_window_size is not None and self.context_window_size != 0:
+            return int(self.context_window_size)
 
-        self.context_window_size = int(
-            DEFAULT_CONTEXT_WINDOW_SIZE * CONTEXT_WINDOW_USAGE_RATIO
-        )
-        for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if self.model.startswith(key):
-                self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
+        window_size = DEFAULT_CONTEXT_WINDOW_SIZE
+        if isinstance(self.model, str):
+            for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
+                if self.model.startswith(key):
+                    window_size = value
+                    break
+
+        self.context_window_size = int(window_size * CONTEXT_WINDOW_USAGE_RATIO)
         return self.context_window_size
 
-    def set_callbacks(self, callbacks: List[Any]):
+    def set_callbacks(self, callbacks: Optional[List[Any]] = None) -> None:
+        """Set callbacks for the LLM.
+        
+        Args:
+            callbacks: Optional list of callback functions. If None, no callbacks will be set.
         """
-        Attempt to keep a single set of callbacks in litellm by removing old
-        duplicates and adding new ones.
-        """
-        with suppress_warnings():
+        if callbacks is not None:
             callback_types = [type(callback) for callback in callbacks]
             for callback in litellm.success_callback[:]:
                 if type(callback) in callback_types:
@@ -371,20 +580,19 @@ class LLM:
         This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
         `litellm.failure_callback` to ["langfuse"].
         """
-        with suppress_warnings():
-            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-            success_callbacks = []
-            if success_callbacks_str:
-                success_callbacks = [
-                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
-                ]
+        success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
+        success_callbacks = []
+        if success_callbacks_str:
+            success_callbacks = [
+                callback.strip() for callback in success_callbacks_str.split(",")
+            ]
 
-            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
-            failure_callbacks = []
-            if failure_callbacks_str:
-                failure_callbacks = [
-                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
-                ]
+        failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
+        failure_callbacks = []
+        if failure_callbacks_str:
+            failure_callbacks = [
+                callback.strip() for callback in failure_callbacks_str.split(",")
+            ]
 
-                litellm.success_callback = success_callbacks
-                litellm.failure_callback = failure_callbacks
+        litellm.success_callback = success_callbacks
+        litellm.failure_callback = failure_callbacks

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/mem0_storage.py

@@ -27,18 +27,10 @@ class Mem0Storage(Storage):
             raise ValueError("User ID is required for user memory type")
 
         # API key in memory config overrides the environment variable
-        config = self.memory_config.get("config", {})
-        mem0_api_key = config.get("api_key") or os.getenv("MEM0_API_KEY")
-        mem0_org_id = config.get("org_id")
-        mem0_project_id = config.get("project_id")
-
-        # Initialize MemoryClient with available parameters
-        if mem0_org_id and mem0_project_id:
-            self.memory = MemoryClient(
-                api_key=mem0_api_key, org_id=mem0_org_id, project_id=mem0_project_id
-            )
-        else:
-            self.memory = MemoryClient(api_key=mem0_api_key)
+        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
+            "MEM0_API_KEY"
+        )
+        self.memory = MemoryClient(api_key=mem0_api_key)
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -65,7 +57,7 @@ class Mem0Storage(Storage):
                 metadata={"type": "long_term", **metadata},
             )
         elif self.memory_type == "entities":
-            entity_name = self._get_agent_name()
+            entity_name = None
             self.memory.add(
                 value, user_id=entity_name, metadata={"type": "entity", **metadata}
             )

src/crewai/project/annotations.py

@@ -4,23 +4,18 @@ from typing import Callable
 from crewai import Crew
 from crewai.project.utils import memoize
 
-"""Decorators for defining crew components and their behaviors."""
-
 
 def before_kickoff(func):
-    """Marks a method to execute before crew kickoff."""
     func.is_before_kickoff = True
     return func
 
 
 def after_kickoff(func):
-    """Marks a method to execute after crew kickoff."""
     func.is_after_kickoff = True
     return func
 
 
 def task(func):
-    """Marks a method as a crew task."""
     func.is_task = True
 
     @wraps(func)
@@ -34,51 +29,43 @@ def task(func):
 
 
 def agent(func):
-    """Marks a method as a crew agent."""
     func.is_agent = True
     func = memoize(func)
     return func
 
 
 def llm(func):
-    """Marks a method as an LLM provider."""
     func.is_llm = True
     func = memoize(func)
     return func
 
 
 def output_json(cls):
-    """Marks a class as JSON output format."""
     cls.is_output_json = True
     return cls
 
 
 def output_pydantic(cls):
-    """Marks a class as Pydantic output format."""
     cls.is_output_pydantic = True
     return cls
 
 
 def tool(func):
-    """Marks a method as a crew tool."""
     func.is_tool = True
     return memoize(func)
 
 
 def callback(func):
-    """Marks a method as a crew callback."""
     func.is_callback = True
     return memoize(func)
 
 
 def cache_handler(func):
-    """Marks a method as a cache handler."""
     func.is_cache_handler = True
     return memoize(func)
 
 
 def crew(func) -> Callable[..., Crew]:
-    """Marks a method as the main crew execution point."""
 
     @wraps(func)
     def wrapper(self, *args, **kwargs) -> Crew:

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 +7,10 @@ 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 +24,16 @@ 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 +49,7 @@ def CrewBase(cls: T) -> T:
                     ]
                 )
             }
+
             # Store specific function types
             self._original_tasks = self._filter_functions(
                 self._original_functions, "is_task"
@@ -66,44 +67,6 @@ 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:
@@ -253,5 +216,5 @@ def CrewBase(cls: T) -> T:
     # Include base class (qual)name in the wrapper class (qual)name.
     WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")"
     WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")"
-
+  
     return cast(T, WrappedClass)

src/crewai/task.py

@@ -41,7 +41,6 @@ from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.i18n import I18N
-from crewai.utilities.printer import Printer
 
 
 class Task(BaseModel):
@@ -128,40 +127,38 @@ class Task(BaseModel):
     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",
+        description="Function to validate task output before proceeding to next task"
     )
     max_retries: int = Field(
-        default=3, description="Maximum number of retries when guardrail fails"
+        default=3,
+        description="Maximum number of retries when guardrail fails"
     )
-    retry_count: int = Field(default=0, description="Current number of retries")
-    start_time: Optional[datetime.datetime] = Field(
-        default=None, description="Start time of the task execution"
-    )
-    end_time: Optional[datetime.datetime] = Field(
-        default=None, description="End time of the task execution"
+    retry_count: int = Field(
+        default=0,
+        description="Current number of retries"
     )
 
     @field_validator("guardrail")
     @classmethod
     def validate_guardrail_function(cls, v: Optional[Callable]) -> Optional[Callable]:
         """Validate that the guardrail function has the correct signature and behavior.
-
+        
         While type hints provide static checking, this validator ensures runtime safety by:
         1. Verifying the function accepts exactly one parameter (the TaskOutput)
         2. Checking return type annotations match Tuple[bool, Any] if present
         3. Providing clear, immediate error messages for debugging
-
+        
         This runtime validation is crucial because:
         - Type hints are optional and can be ignored at runtime
         - Function signatures need immediate validation before task execution
         - Clear error messages help users debug guardrail implementation issues
-
+        
         Args:
             v: The guardrail function to validate
-
+            
         Returns:
             The validated guardrail function
-
+            
         Raises:
             ValueError: If the function signature is invalid or return annotation
                        doesn't match Tuple[bool, Any]
@@ -174,13 +171,8 @@ class Task(BaseModel):
             # Check return annotation if present, but don't require it
             return_annotation = sig.return_annotation
             if return_annotation != inspect.Signature.empty:
-                if not (
-                    return_annotation == Tuple[bool, Any]
-                    or str(return_annotation) == "Tuple[bool, Any]"
-                ):
-                    raise ValueError(
-                        "If return type is annotated, it must be Tuple[bool, Any]"
-                    )
+                if not (return_annotation == Tuple[bool, Any] or str(return_annotation) == 'Tuple[bool, Any]'):
+                    raise ValueError("If return type is annotated, it must be Tuple[bool, Any]")
         return v
 
     _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
@@ -189,6 +181,7 @@ class Task(BaseModel):
     _original_expected_output: Optional[str] = PrivateAttr(default=None)
     _original_output_file: Optional[str] = PrivateAttr(default=None)
     _thread: Optional[threading.Thread] = PrivateAttr(default=None)
+    _execution_time: Optional[float] = PrivateAttr(default=None)
 
     @model_validator(mode="before")
     @classmethod
@@ -213,19 +206,25 @@ class Task(BaseModel):
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
 
+    def _set_start_execution_time(self) -> float:
+        return datetime.datetime.now().timestamp()
+
+    def _set_end_execution_time(self, start_time: float) -> None:
+        self._execution_time = datetime.datetime.now().timestamp() - start_time
+
     @field_validator("output_file")
     @classmethod
     def output_file_validation(cls, value: Optional[str]) -> Optional[str]:
         """Validate the output file path.
-
+        
         Args:
             value: The output file path to validate. Can be None or a string.
                   If the path contains template variables (e.g. {var}), leading slashes are preserved.
                   For regular paths, leading slashes are stripped.
-
+        
         Returns:
             The validated and potentially modified path, or None if no path was provided.
-
+            
         Raises:
             ValueError: If the path contains invalid characters, path traversal attempts,
                       or other security concerns.
@@ -235,24 +234,18 @@ class Task(BaseModel):
 
         # Basic security checks
         if ".." in value:
-            raise ValueError(
-                "Path traversal attempts are not allowed in output_file paths"
-            )
-
+            raise ValueError("Path traversal attempts are not allowed in output_file paths")
+        
         # Check for shell expansion first
-        if value.startswith("~") or value.startswith("$"):
-            raise ValueError(
-                "Shell expansion characters are not allowed in output_file paths"
-            )
-
+        if value.startswith('~') or value.startswith('$'):
+            raise ValueError("Shell expansion characters are not allowed in output_file paths")
+            
         # Then check other shell special characters
-        if any(char in value for char in ["|", ">", "<", "&", ";"]):
-            raise ValueError(
-                "Shell special characters are not allowed in output_file paths"
-            )
+        if any(char in value for char in ['|', '>', '<', '&', ';']):
+            raise ValueError("Shell special characters are not allowed in output_file paths")
 
         # Don't strip leading slash if it's a template path with variables
-        if "{" in value or "}" in value:
+        if "{" in value or "}" in value: 
             # Validate template variable format
             template_vars = [part.split("}")[0] for part in value.split("{")[1:]]
             for var in template_vars:
@@ -276,7 +269,9 @@ class Task(BaseModel):
     @model_validator(mode="after")
     def check_tools(self):
         """Check if the tools are set."""
-        if not self.tools and self.agent and self.agent.tools:
+        if self.agent and self.agent.tools:
+            if self.tools is None:
+                self.tools = []
             self.tools.extend(self.agent.tools)
         return self
 
@@ -309,12 +304,6 @@ class Task(BaseModel):
 
         return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
 
-    @property
-    def execution_duration(self) -> float | None:
-        if not self.start_time or not self.end_time:
-            return None
-        return (self.end_time - self.start_time).total_seconds()
-
     def execute_async(
         self,
         agent: BaseAgent | None = None,
@@ -355,13 +344,14 @@ class Task(BaseModel):
                 f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
             )
 
-        self.start_time = datetime.datetime.now()
+        start_time = self._set_start_execution_time()
         self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
 
         self.prompt_context = context
         tools = tools or self.tools or []
 
-        self.processed_by_agents.add(agent.role)
+        if agent and agent.role:
+            self.processed_by_agents.add(agent.role)
 
         result = agent.execute_task(
             task=self,
@@ -391,14 +381,10 @@ class Task(BaseModel):
                     )
 
                 self.retry_count += 1
-                context = self.i18n.errors("validation_error").format(
-                    guardrail_result_error=guardrail_result.error,
-                    task_output=task_output.raw,
-                )
-                printer = Printer()
-                printer.print(
-                    content=f"Guardrail blocked, retrying, due to: {guardrail_result.error}\n",
-                    color="yellow",
+                context = (
+                    f"### Previous attempt failed validation: {guardrail_result.error}\n\n\n"
+                    f"### Previous result:\n{task_output.raw}\n\n\n"
+                    "Try again, making sure to address the validation error."
                 )
                 return self._execute_core(agent, context, tools)
 
@@ -409,17 +395,15 @@ class Task(BaseModel):
 
             if isinstance(guardrail_result.result, str):
                 task_output.raw = guardrail_result.result
-                pydantic_output, json_output = self._export_output(
-                    guardrail_result.result
-                )
+                pydantic_output, json_output = self._export_output(guardrail_result.result)
                 task_output.pydantic = pydantic_output
                 task_output.json_dict = json_output
             elif isinstance(guardrail_result.result, TaskOutput):
                 task_output = guardrail_result.result
 
         self.output = task_output
-        self.end_time = datetime.datetime.now()
 
+        self._set_end_execution_time(start_time)
         if self.callback:
             self.callback(self.output)
 
@@ -451,16 +435,13 @@ class Task(BaseModel):
         tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
 
-    def interpolate_inputs_and_add_conversation_history(
-        self, inputs: Dict[str, Union[str, int, float]]
-    ) -> None:
+    def interpolate_inputs(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.
-
+        
         Args:
             inputs: Dictionary mapping template variables to their values.
                    Supported value types are strings, integers, and floats.
-
+        
         Raises:
             ValueError: If a required template variable is missing from inputs.
         """
@@ -477,9 +458,7 @@ class Task(BaseModel):
         try:
             self.description = self._original_description.format(**inputs)
         except KeyError as e:
-            raise ValueError(
-                f"Missing required template variable '{e.args[0]}' in description"
-            ) from e
+            raise ValueError(f"Missing required template variable '{e.args[0]}' in description") from e
         except ValueError as e:
             raise ValueError(f"Error interpolating description: {str(e)}") from e
 
@@ -496,49 +475,22 @@ class Task(BaseModel):
                     input_string=self._original_output_file, inputs=inputs
                 )
             except (KeyError, ValueError) as e:
-                raise ValueError(
-                    f"Error interpolating output_file path: {str(e)}"
-                ) from e
+                raise ValueError(f"Error interpolating output_file path: {str(e)}") from e
 
-        if "crew_chat_messages" in inputs and inputs["crew_chat_messages"]:
-            conversation_instruction = self.i18n.slice(
-                "conversation_history_instruction"
-            )
-
-            crew_chat_messages_json = str(inputs["crew_chat_messages"])
-
-            try:
-                crew_chat_messages = json.loads(crew_chat_messages_json)
-            except json.JSONDecodeError as e:
-                print("An error occurred while parsing crew chat messages:", e)
-                raise
-
-            conversation_history = "\n".join(
-                f"{msg['role'].capitalize()}: {msg['content']}"
-                for msg in crew_chat_messages
-                if isinstance(msg, dict) and "role" in msg and "content" in msg
-            )
-
-            self.description += (
-                f"\n\n{conversation_instruction}\n\n{conversation_history}"
-            )
-
-    def interpolate_only(
-        self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]
-    ) -> str:
+    def interpolate_only(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.
-
+        
         Args:
             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, 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 required template variable is missing from inputs.
             KeyError: If a template variable is not found in the inputs dictionary.
@@ -548,17 +500,13 @@ class Task(BaseModel):
         if "{" not in input_string and "}" not in input_string:
             return input_string
         if not inputs:
-            raise ValueError(
-                "Inputs dictionary cannot be empty when interpolating variables"
-            )
+            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__}"
-                    )
+                    raise ValueError(f"Value for key '{key}' must be a string, integer, or float, got {type(value).__name__}")
 
             escaped_string = input_string.replace("{", "{{").replace("}", "}}")
 
@@ -567,9 +515,7 @@ class Task(BaseModel):
 
             return escaped_string.format(**inputs)
         except KeyError as e:
-            raise KeyError(
-                f"Template variable '{e.args[0]}' not found in inputs dictionary"
-            ) from e
+            raise KeyError(f"Template variable '{e.args[0]}' not found in inputs dictionary") from e
         except ValueError as e:
             raise ValueError(f"Error during string interpolation: {str(e)}") from e
 
@@ -654,10 +600,10 @@ class Task(BaseModel):
 
     def _save_file(self, result: Any) -> None:
         """Save task output to a file.
-
+        
         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
@@ -675,7 +621,6 @@ class Task(BaseModel):
             with resolved_path.open("w", encoding="utf-8") as file:
                 if isinstance(result, dict):
                     import json
-
                     json.dump(result, file, ensure_ascii=False, indent=2)
                 else:
                     file.write(str(result))

src/crewai/tasks/task_output.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, Optional
+from typing import Any, Callable, Dict, Optional, Union
 
 from pydantic import BaseModel, Field, model_validator
 
@@ -34,8 +34,19 @@ class TaskOutput(BaseModel):
         self.summary = f"{excerpt}..."
         return self
 
-    @property
-    def json(self) -> Optional[str]:
+    def json(
+        self,
+        *,
+        include: Union[set[str], None] = None,
+        exclude: Union[set[str], None] = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        encoder: Optional[Callable[[Any], Any]] = None,
+        models_as_dict: bool = True,
+        **dumps_kwargs: Any,
+    ) -> str:
         if self.output_format != OutputFormat.JSON:
             raise ValueError(
                 """
@@ -45,7 +56,7 @@ class TaskOutput(BaseModel):
                 """
             )
 
-        return json.dumps(self.json_dict)
+        return json.dumps(self.json_dict, default=encoder, **dumps_kwargs)
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert json_output and pydantic_output to a dictionary."""

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Optional
+from typing import Optional, Union
 
 from pydantic import Field
 
@@ -19,13 +19,13 @@ class BaseAgentTool(BaseTool):
         default_factory=I18N, description="Internationalization settings"
     )
 
-    def sanitize_agent_name(self, name: str) -> str:
+    def sanitize_agent_name(self, name: Optional[str]) -> str:
         """
         Sanitize agent role name by normalizing whitespace and setting to lowercase.
         Converts all whitespace (including newlines) to single spaces and removes quotes.
 
         Args:
-            name (str): The agent role name to sanitize
+            name (Optional[str]): The agent role name to sanitize
 
         Returns:
             str: The sanitized agent role name, with whitespace normalized,
@@ -54,12 +54,12 @@ class BaseAgentTool(BaseTool):
     ) -> str:
         """
         Execute delegation to an agent with case-insensitive and whitespace-tolerant matching.
-
+        
         Args:
             agent_name: Name/role of the agent to delegate to (case-insensitive)
             task: The specific question or task to delegate
             context: Optional additional context for the task execution
-
+            
         Returns:
             str: The execution result from the delegated agent or an error message
                  if the agent cannot be found

src/crewai/tools/base_tool.py

@@ -1,23 +1,12 @@
-import warnings
 from abc import ABC, abstractmethod
 from inspect import signature
 from typing import Any, Callable, Type, get_args, get_origin
 
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    Field,
-    PydanticDeprecatedSince20,
-    create_model,
-    validator,
-)
+from pydantic import BaseModel, ConfigDict, Field, create_model, validator
 from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
-# Ignore all "PydanticDeprecatedSince20" warnings globally
-warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
-
 
 class BaseTool(BaseModel, ABC):
     class _ArgsSchemaPlaceholder(PydanticBaseModel):

src/crewai/tools/structured_tool.py

@@ -142,7 +142,12 @@ class CrewStructuredTool:
 
         # Create model
         schema_name = f"{name.title()}Schema"
-        return create_model(schema_name, **fields)
+        return create_model(
+            schema_name,
+            __base__=BaseModel,
+            __config__=None,
+            **{k: v for k, v in fields.items()}
+        )
 
     def _validate_function_signature(self) -> None:
         """Validate that the function signature matches the args schema."""
@@ -170,7 +175,7 @@ class CrewStructuredTool:
                         f"not found in args_schema"
                     )
 
-    def _parse_args(self, raw_args: Union[str, dict]) -> dict:
+    def _parse_args(self, raw_args: Union[str, dict[str, Any]]) -> dict[str, Any]:
         """Parse and validate the input arguments against the schema.
 
         Args:
@@ -178,6 +183,9 @@ class CrewStructuredTool:
 
         Returns:
             The validated arguments as a dictionary
+
+        Raises:
+            ValueError: If the arguments cannot be parsed or fail validation
         """
         if isinstance(raw_args, str):
             try:
@@ -195,8 +203,8 @@ class CrewStructuredTool:
 
     async def ainvoke(
         self,
-        input: Union[str, dict],
-        config: Optional[dict] = None,
+        input: Union[str, dict[str, Any]],
+        config: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
         """Asynchronously invoke the tool.
@@ -229,7 +237,10 @@ class CrewStructuredTool:
         return self.invoke(input_dict)
 
     def invoke(
-        self, input: Union[str, dict], config: Optional[dict] = None, **kwargs: Any
+        self,
+        input: Union[str, dict[str, Any]],
+        config: Optional[dict[str, Any]] = None,
+        **kwargs: Any
     ) -> Any:
         """Main method for tool execution."""
         parsed_args = self._parse_args(input)

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,
@@ -181,7 +169,7 @@ class ToolUsage:
 
                 if calling.arguments:
                     try:
-                        acceptable_args = tool.args_schema.model_json_schema()["properties"].keys()  # type: ignore
+                        acceptable_args = tool.args_schema.schema()["properties"].keys()  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
                         arguments = {
                             k: v
                             for k, v in calling.arguments.items()
@@ -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

@@ -9,11 +9,11 @@
     "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 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}",
@@ -23,11 +23,10 @@
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
-    "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"",
-    "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals."
+    "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}\""
   },
   "errors": {
-    "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",
+    "force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}",
     "force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.",
     "agent_tool_unexisting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
     "task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",
@@ -35,15 +34,14 @@
     "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",
     "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.",
     "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}",
-    "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}",
-    "validation_error": "### Previous attempt failed validation: {guardrail_result_error}\n\n\n### Previous result:\n{task_output}\n\n\nTry again, making sure to address the validation error."
+    "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}"
   },
   "tools": {
     "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",
     "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/types/crew_chat.py

@@ -1,40 +0,0 @@
-from typing import List
-
-from pydantic import BaseModel, Field
-
-
-class ChatInputField(BaseModel):
-    """
-    Represents a single required input for the crew, with a name and short description.
-    Example:
-        {
-            "name": "topic",
-            "description": "The topic to focus on for the conversation"
-        }
-    """
-
-    name: str = Field(..., description="The name of the input field")
-    description: str = Field(..., description="A short description of the input field")
-
-
-class ChatInputs(BaseModel):
-    """
-    Holds a high-level crew_description plus a list of ChatInputFields.
-    Example:
-        {
-            "crew_name": "topic-based-qa",
-            "crew_description": "Use this crew for topic-based Q&A",
-            "inputs": [
-                {"name": "topic", "description": "The topic to focus on"},
-                {"name": "username", "description": "Name of the user"},
-            ]
-        }
-    """
-
-    crew_name: str = Field(..., description="The name of the crew")
-    crew_description: str = Field(
-        ..., description="A description of the crew's purpose"
-    )
-    inputs: List[ChatInputField] = Field(
-        default_factory=list, description="A list of input fields for the crew"
-    )

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/crew_json_encoder.py

@@ -1,5 +1,3 @@
-"""JSON encoder for handling CrewAI specific types."""
-
 import json
 from datetime import date, datetime
 from decimal import Decimal
@@ -10,7 +8,6 @@ from pydantic import BaseModel
 
 
 class CrewJSONEncoder(json.JSONEncoder):
-    """Custom JSON encoder for CrewAI objects and special types."""
     def default(self, obj):
         if isinstance(obj, BaseModel):
             return self._handle_pydantic_model(obj)

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -6,10 +6,9 @@ from pydantic import BaseModel, ValidationError
 
 from crewai.agents.parser import OutputParserException
 
-"""Parser for converting text outputs into Pydantic models."""
 
 class CrewPydanticOutputParser:
-    """Parses text outputs into specified Pydantic models."""
+    """Parses the text into pydantic models"""
 
     pydantic_object: Type[BaseModel]
 

src/crewai/utilities/embedding_configurator.py

@@ -14,7 +14,6 @@ class EmbeddingConfigurator:
             "vertexai": self._configure_vertexai,
             "google": self._configure_google,
             "cohere": self._configure_cohere,
-            "voyageai": self._configure_voyageai,
             "bedrock": self._configure_bedrock,
             "huggingface": self._configure_huggingface,
             "watson": self._configure_watson,
@@ -125,17 +124,6 @@ class EmbeddingConfigurator:
             api_key=config.get("api_key"),
         )
 
-    @staticmethod
-    def _configure_voyageai(config, model_name):
-        from chromadb.utils.embedding_functions.voyageai_embedding_function import (
-            VoyageAIEmbeddingFunction,
-        )
-
-        return VoyageAIEmbeddingFunction(
-            model_name=model_name,
-            api_key=config.get("api_key"),
-        )
-
     @staticmethod
     def _configure_bedrock(config, model_name):
         from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (

src/crewai/utilities/errors.py

@@ -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/crew_evaluator_handler.py

@@ -180,12 +180,12 @@ class CrewEvaluator:
             self._test_result_span = self._telemetry.individual_test_result_span(
                 self.crew,
                 evaluation_result.pydantic.quality,
-                current_task.execution_duration,
+                current_task._execution_time,
                 self.openai_model_name,
             )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(
-                current_task.execution_duration
+                current_task._execution_time
             )
         else:
             raise ValueError("Evaluation result is not in the expected format")

src/crewai/utilities/i18n.py

@@ -4,10 +4,8 @@ from typing import Dict, Optional, Union
 
 from pydantic import BaseModel, Field, PrivateAttr, model_validator
 
-"""Internationalization support for CrewAI prompts and messages."""
 
 class I18N(BaseModel):
-    """Handles loading and retrieving internationalized prompts."""
     _prompts: Dict[str, Dict[str, str]] = PrivateAttr()
     prompt_file: Optional[str] = Field(
         default=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

@@ -1,181 +0,0 @@
-import os
-from typing import Any, Dict, List, Optional, Union
-
-from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
-from crewai.llm import LLM
-
-
-def create_llm(
-    llm_value: Union[str, LLM, Any, None] = None,
-) -> Optional[LLM]:
-    """
-    Creates or returns an LLM instance based on the given llm_value.
-
-    Args:
-        llm_value (str | LLM | Any | None):
-            - str: The model name (e.g., "gpt-4").
-            - LLM: Already instantiated LLM, returned as-is.
-            - Any: Attempt to extract known attributes like model_name, temperature, etc.
-            - None: Use environment-based or fallback default model.
-
-    Returns:
-        An LLM instance if successful, or None if something fails.
-    """
-
-    # 1) If llm_value is already an LLM object, return it directly
-    if isinstance(llm_value, LLM):
-        return llm_value
-
-    # 2) If llm_value is a string (model name)
-    if isinstance(llm_value, str):
-        try:
-            created_llm = LLM(model=llm_value)
-            return created_llm
-        except Exception as e:
-            print(f"Failed to instantiate LLM with model='{llm_value}': {e}")
-            return None
-
-    # 3) If llm_value is None, parse environment variables or use default
-    if llm_value is None:
-        return _llm_via_environment_or_fallback()
-
-    # 4) Otherwise, attempt to extract relevant attributes from an unknown object
-    try:
-        # Extract attributes with explicit types
-        model = (
-            getattr(llm_value, "model_name", None)
-            or getattr(llm_value, "deployment_name", None)
-            or str(llm_value)
-        )
-        temperature: Optional[float] = getattr(llm_value, "temperature", None)
-        max_tokens: Optional[int] = getattr(llm_value, "max_tokens", None)
-        logprobs: Optional[int] = getattr(llm_value, "logprobs", None)
-        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)
-
-        created_llm = LLM(
-            model=model,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            logprobs=logprobs,
-            timeout=timeout,
-            api_key=api_key,
-            base_url=base_url,
-        )
-        return created_llm
-    except Exception as e:
-        print(f"Error instantiating LLM from unknown object type: {e}")
-        return None
-
-
-def _llm_via_environment_or_fallback() -> Optional[LLM]:
-    """
-    Helper function: if llm_value is None, we load environment variables or fallback default model.
-    """
-    model_name = (
-        os.environ.get("OPENAI_MODEL_NAME")
-        or os.environ.get("MODEL")
-        or DEFAULT_LLM_MODEL
-    )
-
-    # Initialize parameters with correct types
-    model: str = model_name
-    temperature: Optional[float] = None
-    max_tokens: Optional[int] = None
-    max_completion_tokens: Optional[int] = None
-    logprobs: Optional[int] = None
-    timeout: Optional[float] = None
-    api_key: Optional[str] = None
-    base_url: Optional[str] = None
-    api_version: Optional[str] = None
-    presence_penalty: Optional[float] = None
-    frequency_penalty: Optional[float] = None
-    top_p: Optional[float] = None
-    n: Optional[int] = None
-    stop: Optional[Union[str, List[str]]] = None
-    logit_bias: Optional[Dict[int, float]] = None
-    response_format: Optional[Dict[str, Any]] = None
-    seed: Optional[int] = None
-    top_logprobs: Optional[int] = None
-    callbacks: List[Any] = []
-
-    # Optional base URL from env
-    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
-    llm_params: Dict[str, Any] = {
-        "model": model,
-        "temperature": temperature,
-        "max_tokens": max_tokens,
-        "max_completion_tokens": max_completion_tokens,
-        "logprobs": logprobs,
-        "timeout": timeout,
-        "api_key": api_key,
-        "base_url": base_url,
-        "api_version": api_version,
-        "presence_penalty": presence_penalty,
-        "frequency_penalty": frequency_penalty,
-        "top_p": top_p,
-        "n": n,
-        "stop": stop,
-        "logit_bias": logit_bias,
-        "response_format": response_format,
-        "seed": seed,
-        "top_logprobs": top_logprobs,
-        "callbacks": callbacks,
-    }
-
-    UNACCEPTED_ATTRIBUTES = [
-        "AWS_ACCESS_KEY_ID",
-        "AWS_SECRET_ACCESS_KEY",
-        "AWS_REGION_NAME",
-    ]
-    set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
-
-    if set_provider in ENV_VARS:
-        env_vars_for_provider = ENV_VARS[set_provider]
-        if isinstance(env_vars_for_provider, (list, tuple)):
-            for env_var in env_vars_for_provider:
-                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:
-                        # Map environment variable names to recognized parameters
-                        param_key = _normalize_key_name(key_name.lower())
-                        llm_params[param_key] = env_value
-                elif isinstance(env_var, dict):
-                    if env_var.get("default", False):
-                        for key, value in env_var.items():
-                            if key not in ["prompt", "key_name", "default"]:
-                                llm_params[key.lower()] = value
-                else:
-                    print(
-                        f"Expected env_var to be a dictionary, but got {type(env_var)}"
-                    )
-
-    # Remove None values
-    llm_params = {k: v for k, v in llm_params.items() if v is not None}
-
-    # Try creating the LLM
-    try:
-        new_llm = LLM(**llm_params)
-        return new_llm
-    except Exception as e:
-        print(
-            f"Error instantiating LLM from environment/fallback: {type(e).__name__}: {e}"
-        )
-        return None
-
-
-def _normalize_key_name(key_name: str) -> str:
-    """
-    Maps environment variable names to recognized litellm parameter keys,
-    using patterns from LITELLM_PARAMS.
-    """
-    for pattern in LITELLM_PARAMS:
-        if pattern in key_name:
-            return pattern
-    return key_name

src/crewai/utilities/logger.py

@@ -10,8 +10,24 @@ class Logger(BaseModel):
     _printer: Printer = PrivateAttr(default_factory=Printer)
 
     def log(self, level, message, color="bold_yellow"):
-        if self.verbose:
+        if self.verbose or level.upper() in ["WARNING", "ERROR"]:
             timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
             self._printer.print(
                 f"\n[{timestamp}][{level.upper()}]: {message}", color=color
             )
+
+    def debug(self, message: str) -> None:
+        """Log a debug message if verbose is enabled."""
+        self.log("debug", message, color="bold_blue")
+
+    def info(self, message: str) -> None:
+        """Log an info message if verbose is enabled."""
+        self.log("info", message, color="bold_green")
+
+    def warning(self, message: str) -> None:
+        """Log a warning message."""
+        self.log("warning", message, color="bold_yellow")
+
+    def error(self, message: str) -> None:
+        """Log an error message."""
+        self.log("error", message, color="bold_red")

src/crewai/utilities/paths.py

@@ -3,24 +3,17 @@ from pathlib import Path
 
 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():
     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():
-    """Returns the current project directory name."""
     project_directory_name = os.environ.get("CREWAI_STORAGE_DIR")
 
     if project_directory_name:
@@ -28,4 +21,4 @@ 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/planning_handler.py

@@ -1,3 +1,4 @@
+import json
 import logging
 from typing import Any, List, Optional
 
@@ -6,11 +7,10 @@ from pydantic import BaseModel, Field
 from crewai.agent import Agent
 from crewai.task import Task
 
-"""Handles planning and coordination of crew tasks."""
 logger = logging.getLogger(__name__)
 
+
 class PlanPerTask(BaseModel):
-    """Represents a plan for a specific task."""
     task: str = Field(..., description="The task for which the plan is created")
     plan: str = Field(
         ...,
@@ -19,7 +19,6 @@ class PlanPerTask(BaseModel):
 
 
 class PlannerTaskPydanticOutput(BaseModel):
-    """Output format for task planning results."""
     list_of_plans_per_task: List[PlanPerTask] = Field(
         ...,
         description="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
@@ -27,7 +26,6 @@ class PlannerTaskPydanticOutput(BaseModel):
 
 
 class CrewPlanner:
-    """Plans and coordinates the execution of crew tasks."""
     def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None):
         self.tasks = tasks
 
@@ -77,10 +75,10 @@ class CrewPlanner:
     def _get_agent_knowledge(self, task: Task) -> List[str]:
         """
         Safely retrieve knowledge source content from the task's agent.
-
+        
         Args:
             task: The task containing an agent with potential knowledge sources
-
+            
         Returns:
             List[str]: A list of knowledge source strings
         """
@@ -107,6 +105,6 @@ class CrewPlanner:
                     f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
                     f',\n                "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
                 )
-
+            
             tasks_summary.append(task_summary)
         return " ".join(tasks_summary)

src/crewai/utilities/printer.py

@@ -1,11 +1,7 @@
-"""Utility for colored console output."""
-
 from typing import Optional
 
 
 class Printer:
-    """Handles colored console output formatting."""
-
     def print(self, content: str, color: Optional[str] = None):
         if color == "purple":
             self._print_purple(content)
@@ -21,16 +17,6 @@ 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 +40,3 @@ 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/prompts.py

@@ -63,16 +63,32 @@ class Prompts(BaseModel):
                 for component in components
                 if component != "task"
             ]
-            system = system_template.replace("{{ .System }}", "".join(prompt_parts))
-            prompt = prompt_template.replace(
-                "{{ .Prompt }}", "".join(self.i18n.slice("task"))
-            )
-            response = response_template.split("{{ .Response }}")[0]
-            prompt = f"{system}\n{prompt}\n{response}"
+            system = ""
+            if system_template:
+                system = system_template.replace("{{ .System }}", "".join(prompt_parts))
+            
+            prompt_text = ""
+            if prompt_template:
+                prompt_text = prompt_template.replace(
+                    "{{ .Prompt }}", "".join(self.i18n.slice("task"))
+                )
+            
+            response = ""
+            if response_template:
+                response = response_template.split("{{ .Response }}")[0]
+            
+            parts = [p for p in [system, prompt_text, response] if p]
+            prompt = "\n".join(parts) if parts else ""
 
+        # Get agent attributes with default values
+        goal = str(getattr(self.agent, 'goal', '') or '')
+        role = str(getattr(self.agent, 'role', '') or '')
+        backstory = str(getattr(self.agent, 'backstory', '') or '')
+        
+        # Replace placeholders with agent attributes
         prompt = (
-            prompt.replace("{goal}", self.agent.goal)
-            .replace("{role}", self.agent.role)
-            .replace("{backstory}", self.agent.backstory)
+            prompt.replace("{goal}", goal)
+            .replace("{role}", role)
+            .replace("{backstory}", backstory)
         )
         return prompt

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/rpm_controller.py

@@ -6,12 +6,8 @@ from pydantic import BaseModel, Field, PrivateAttr, model_validator
 
 from crewai.utilities.logger import Logger
 
-"""Controls request rate limiting for API calls."""
-
 
 class RPMController(BaseModel):
-    """Manages requests per minute limiting."""
-
     max_rpm: Optional[int] = Field(default=None)
     logger: Logger = Field(default_factory=lambda: Logger(verbose=False))
     _current_rpm: int = PrivateAttr(default=0)

src/crewai/utilities/task_output_storage_handler.py

@@ -8,10 +8,8 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (
 )
 from crewai.task import Task
 
-"""Handles storage and retrieval of task execution outputs."""
 
 class ExecutionLog(BaseModel):
-    """Represents a log entry for task execution."""
     task_id: str
     expected_output: Optional[str] = None
     output: Dict[str, Any]
@@ -24,8 +22,6 @@ class ExecutionLog(BaseModel):
         return getattr(self, key)
 
 
-"""Manages storage and retrieval of task outputs."""
-
 class TaskOutputStorageHandler:
     def __init__(self) -> None:
         self.storage = KickoffTaskOutputsSQLiteStorage()

src/crewai/utilities/token_counter_callback.py

@@ -1,5 +1,4 @@
 import warnings
-from typing import Any, Dict, Optional
 
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.types.utils import Usage
@@ -8,30 +7,20 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 
 
 class TokenCalcHandler(CustomLogger):
-    def __init__(self, token_cost_process: Optional[TokenProcess]):
+    def __init__(self, token_cost_process: TokenProcess):
         self.token_cost_process = token_cost_process
 
-    def log_success_event(
-        self,
-        kwargs: Dict[str, Any],
-        response_obj: Dict[str, Any],
-        start_time: float,
-        end_time: float,
-    ) -> None:
+    def log_success_event(self, kwargs, response_obj, start_time, end_time):
         if self.token_cost_process is None:
             return
 
         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/token_process.py

@@ -0,0 +1,74 @@
+"""Token processing utility for tracking and managing token usage."""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Union
+
+from crewai.types.usage_metrics import UsageMetrics
+
+
+class TokenProcess:
+    """Handles token processing and tracking for agents."""
+    
+    def __init__(self):
+        """Initialize the token processor."""
+        self._total_tokens = 0
+        self._prompt_tokens = 0
+        self._completion_tokens = 0
+        self._cached_prompt_tokens = 0
+        self._successful_requests = 0
+    
+    def sum_prompt_tokens(self, count: int) -> None:
+        """Add to prompt token count.
+        
+        Args:
+            count (int): Number of prompt tokens to add
+        """
+        self._prompt_tokens += count
+        self._total_tokens += count
+    
+    def sum_completion_tokens(self, count: int) -> None:
+        """Add to completion token count.
+        
+        Args:
+            count (int): Number of completion tokens to add
+        """
+        self._completion_tokens += count
+        self._total_tokens += count
+    
+    def sum_cached_prompt_tokens(self, count: int) -> None:
+        """Add to cached prompt token count.
+        
+        Args:
+            count (int): Number of cached prompt tokens to add
+        """
+        self._cached_prompt_tokens += count
+    
+    def sum_successful_requests(self, count: int) -> None:
+        """Add to successful requests count.
+        
+        Args:
+            count (int): Number of successful requests to add
+        """
+        self._successful_requests += count
+    
+    def reset(self) -> None:
+        """Reset all token counts to zero."""
+        self._total_tokens = 0
+        self._prompt_tokens = 0
+        self._completion_tokens = 0
+        self._cached_prompt_tokens = 0
+        self._successful_requests = 0
+        
+    def get_summary(self) -> UsageMetrics:
+        """Get a summary of token usage.
+        
+        Returns:
+            UsageMetrics: Object containing token usage metrics
+        """
+        return UsageMetrics(
+            total_tokens=self._total_tokens,
+            prompt_tokens=self._prompt_tokens,
+            cached_prompt_tokens=self._cached_prompt_tokens,
+            completion_tokens=self._completion_tokens,
+            successful_requests=self._successful_requests
+        )

tests/agent_test.py

@@ -16,7 +16,7 @@ from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.tools.tool_usage_events import ToolUsageFinished
-from crewai.utilities import Printer, RPMController
+from crewai.utilities import RPMController
 from crewai.utilities.events import Emitter
 
 
@@ -114,6 +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(
@@ -536,7 +565,7 @@ def test_agent_moved_on_after_max_iterations():
         task=task,
         tools=[get_final_answer],
     )
-    assert output == "42"
+    assert output == "The final answer is 42."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -545,6 +574,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
     def get_final_answer() -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
+        return 42
 
     agent = Agent(
         role="test role",
@@ -611,14 +641,15 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_without_max_rpm_respects_crew_rpm(capsys):
+def test_agent_without_max_rpm_respet_crew_rpm(capsys):
     from unittest.mock import patch
 
     from crewai.tools import tool
 
     @tool
     def get_final_answer() -> float:
-        """Get the final answer but don't give it yet, just re-use this tool non-stop."""
+        """Get the final answer but don't give it yet, just re-use this
+        tool non-stop."""
         return 42
 
     agent1 = Agent(
@@ -635,30 +666,23 @@ def test_agent_without_max_rpm_respects_crew_rpm(capsys):
         role="test role2",
         goal="test goal2",
         backstory="test backstory2",
-        max_iter=5,
+        max_iter=1,
         verbose=True,
         allow_delegation=False,
     )
 
     tasks = [
         Task(
-            description="Just say hi.",
-            agent=agent1,
-            expected_output="Your greeting.",
+            description="Just say hi.", agent=agent1, expected_output="Your greeting."
         ),
         Task(
-            description=(
-                "NEVER give a Final Answer, unless you are told otherwise, "
-                "instead keep using the `get_final_answer` tool non-stop, "
-                "until you must give your best final answer"
-            ),
+            description="NEVER give a Final Answer, unless you are told otherwise, instead keep using the `get_final_answer` tool non-stop, until you must give you best final answer",
             expected_output="The final answer",
             tools=[get_final_answer],
             agent=agent2,
         ),
     ]
 
-    # Set crew's max_rpm to 1 to trigger RPM limit
     crew = Crew(agents=[agent1, agent2], tasks=tasks, max_rpm=1, verbose=True)
 
     with patch.object(RPMController, "_wait_for_next_minute") as moveon:
@@ -1433,7 +1457,7 @@ def test_agent_with_ollama_llama3():
     assert agent.llm.model == "ollama/llama3.2:3b"
     assert agent.llm.base_url == "http://localhost:11434"
 
-    task = "Respond in 20 words. Which model are you?"
+    task = "Respond in 20 words. Who are you?"
     response = agent.llm.call([{"role": "user", "content": task}])
 
     assert response
@@ -1449,9 +1473,7 @@ def test_llm_call_with_ollama_llama3():
         temperature=0.7,
         max_tokens=30,
     )
-    messages = [
-        {"role": "user", "content": "Respond in 20 words. Which model are you?"}
-    ]
+    messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}]
 
     response = llm.call(messages)
 
@@ -1466,7 +1488,7 @@ 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(
@@ -1600,142 +1622,3 @@ 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_litellm_auth_error_handling():
-    """Test that LiteLLM authentication errors are handled correctly and not retried."""
-    from litellm import AuthenticationError as LiteLLMAuthenticationError
-
-    # Create an agent with a mocked LLM and max_retry_limit=0
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        llm=LLM(model="gpt-4"),
-        max_retry_limit=0,  # Disable retries for authentication errors
-    )
-
-    # Create a task
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-
-    # Mock the LLM call to raise AuthenticationError
-    with (
-        patch.object(LLM, "call") as mock_llm_call,
-        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
-    ):
-        mock_llm_call.side_effect = LiteLLMAuthenticationError(
-            message="Invalid API key", llm_provider="openai", model="gpt-4"
-        )
-        agent.execute_task(task)
-
-    # Verify the call was only made once (no retries)
-    mock_llm_call.assert_called_once()
-
-
-def test_crew_agent_executor_litellm_auth_error():
-    """Test that CrewAgentExecutor handles LiteLLM authentication errors by raising them."""
-    from litellm.exceptions import AuthenticationError
-
-    from crewai.agents.tools_handler import ToolsHandler
-    from crewai.utilities import Printer
-
-    # Create an agent and executor
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        llm=LLM(model="gpt-4", api_key="invalid_api_key"),
-    )
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-
-    # Create executor with all required parameters
-    executor = CrewAgentExecutor(
-        agent=agent,
-        task=task,
-        llm=agent.llm,
-        crew=None,
-        prompt={"system": "You are a test agent", "user": "Execute the task: {input}"},
-        max_iter=5,
-        tools=[],
-        tools_names="",
-        stop_words=[],
-        tools_description="",
-        tools_handler=ToolsHandler(),
-    )
-
-    # Mock the LLM call to raise AuthenticationError
-    with (
-        patch.object(LLM, "call") as mock_llm_call,
-        patch.object(Printer, "print") as mock_printer,
-        pytest.raises(AuthenticationError) as exc_info,
-    ):
-        mock_llm_call.side_effect = AuthenticationError(
-            message="Invalid API key", llm_provider="openai", model="gpt-4"
-        )
-        executor.invoke(
-            {
-                "input": "test input",
-                "tool_names": "",
-                "tools": "",
-            }
-        )
-
-    # Verify error handling messages
-    error_message = f"Error during LLM call: {str(mock_llm_call.side_effect)}"
-    mock_printer.assert_any_call(
-        content=error_message,
-        color="red",
-    )
-
-    # Verify the call was only made once (no retries)
-    mock_llm_call.assert_called_once()
-
-    # Assert that the exception was raised and has the expected attributes
-    assert exc_info.type is AuthenticationError
-    assert "Invalid API key".lower() in exc_info.value.message.lower()
-    assert exc_info.value.llm_provider == "openai"
-    assert exc_info.value.model == "gpt-4"
-
-
-def test_litellm_anthropic_error_handling():
-    """Test that AnthropicError from LiteLLM is handled correctly and not retried."""
-    from litellm.llms.anthropic.common_utils import AnthropicError
-
-    # Create an agent with a mocked LLM that uses an Anthropic model
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        llm=LLM(model="claude-3.5-sonnet-20240620"),
-        max_retry_limit=0,
-    )
-
-    # Create a task
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-
-    # Mock the LLM call to raise AnthropicError
-    with (
-        patch.object(LLM, "call") as mock_llm_call,
-        pytest.raises(AnthropicError, match="Test Anthropic error"),
-    ):
-        mock_llm_call.side_effect = AnthropicError(
-            status_code=500,
-            message="Test Anthropic error",
-        )
-        agent.execute_task(task)
-
-    # Verify the LLM call was only made once (no retries)
-    mock_llm_call.assert_called_once()

tests/agents/agent_builder/base_agent_test.py

@@ -7,7 +7,7 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools.base_tool import BaseTool
 
 
-class MockAgent(BaseAgent):
+class TestAgent(BaseAgent):
     def execute_task(
         self,
         task: Any,
@@ -29,7 +29,7 @@ class MockAgent(BaseAgent):
 
 
 def test_key():
-    agent = MockAgent(
+    agent = TestAgent(
         role="test role",
         goal="test goal",
         backstory="test backstory",

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+      September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions
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+      Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and Documentation
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+      or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
+      or, \\nif you are an entity, your principal place of business is in the EEA
+      or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
+      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
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+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+      and royalty-free limited license under Meta\u2019s intellectual property or
+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
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+      you distribute or make available the Llama Materials (or any derivative works
+      thereof), \\nor a product or service (including another AI model) that contains
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+      a related website, user interface, blogpost, about page, or product documentation.
+      If you use the\\nLlama Materials or any outputs or results of the Llama Materials
+      to create, train, fine tune, or\\notherwise improve an AI model, which is distributed
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+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
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+      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
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+      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
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+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
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+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
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+      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
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+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
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+      The illegal distribution of information or materials to minors, including obscene
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+      or facilitate the creation of malicious code, malware, computer viruses or do
+      anything else that could disable, overburden, interfere with or impair the proper
+      working, integrity, operation or appearance of a website or computer system\\n
+      \   7. Engage in any action, or facilitate any action, to intentionally circumvent
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tests/cassettes/test_agent_execute_task_with_tool.yaml

@@ -2,22 +2,22 @@ interactions:
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-      Arguments: {''query'': {''description'': None, ''type'': ''str''}}\nTool Description:
-      Useful for when you need to get a dummy result for a query.\n\nUse the following
+      should NEVER make up tools that are not listed here:\n\nTool Name: dummy_tool(*args:
+      Any, **kwargs: Any) -> Any\nTool Description: dummy_tool(query: ''string'')
+      - Useful for when you need to get a dummy result for a query. \nTool Arguments:
+      {''query'': {''title'': ''Query'', ''type'': ''string''}}\n\nUse the following
       format:\n\nThought: you should always think about what to do\nAction: the action
       to take, only one name of [dummy_tool], just the name, exactly as it''s written.\nAction
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       know the final answer\nFinal Answer: the final answer to the original input
-      question"}, {"role": "user", "content": "\nCurrent Task: Use the dummy tool
+      question\n"}, {"role": "user", "content": "\nCurrent Task: Use the dummy tool
       to get a result for ''test query''\n\nThis is the expect criteria for your final
       answer: The result from the dummy tool\nyou MUST return the actual complete
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       to you, use the tools available and give your best Final Answer, your job depends
-      on it!\n\nThought:"}], "model": "gpt-3.5-turbo", "stop": ["\nObservation:"],
-      "stream": false}'
+      on it!\n\nThought:"}], "model": "gpt-3.5-turbo"}'
     headers:
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       connection:
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-      - '1363'
+      - '1385'
       content-type:
       - application/json
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+        _cfuvid=lbRdAddVWV6W3f5Dm9SaOPWDUOxqtZBSPr_fTW26nEA-1727213194587-0.0.1.1-604800000
       host:
       - api.openai.com
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-      - OpenAI/Python 1.52.1
+      - OpenAI/Python 1.47.0
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -42,35 +45,32 @@ interactions:
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