new tests

* Cleaned up task execution to now have separate paths for async and sync execution. Updating all kickoff functions to return CrewOutput. WIP. Waiting for Joao feedback on async task execution with task_output

* Consistently storing async and sync output for context

* outline tests I need to create going forward

* Major rehaul of TaskOutput and CrewOutput. Updated all tests to work with new change. Need to add in a few final tricky async tests and add a few more to verify output types on TaskOutput and CrewOutput.

* Encountering issues with callback. Need to test on main. WIP

* working on tests. WIP

* WIP. Figuring out disconnect issue.

* Cleaned up logs now that I've isolated the issue to the LLM

* more wip.

* WIP. It looks like usage metrics has always been broken for async

* Update parent crew who is managing for_each loop

* Merge in main to bugfix/kickoff-for-each-usage-metrics

* Clean up code for review

* Add new tests

* Final cleanup. Ready for review.

* Moving copy functionality from Agent to BaseAgent

* Fix renaming issue

* Fix linting errors

* use BaseAgent instead of Agent where applicable

* Fixing missing function. Working on tests.

* WIP. Needing team to review change

* Fixing issues brought about by merge

* WIP

* Implement major fixes from yesterdays group conversation. Now working on tests.

* The majority of tasks are working now. Need to fix converter class

* Fix final failing test

* Fix linting and type-checker issues

* Add more tests to fully test CrewOutput and TaskOutput changes

* Add in validation for async cannot depend on other async tasks.

* Update validators and tests

.github/workflows/tests.yml

@@ -19,13 +19,13 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: "3.11.9"
 
       - name: Install Requirements
         run: |
+          set -e
           pip install poetry
-          poetry lock &&
           poetry install
 
       - name: Run tests
-        run: poetry run pytest tests
+        run: poetry run pytest

.gitignore

@@ -13,4 +13,5 @@ db/
 test.py
 rc-tests/*
 *.pkl
-temp/*
+temp/*
+.vscode/*

docs/core-concepts/Agents.md

@@ -16,24 +16,24 @@ description: What are crewAI Agents and how to use them.
 
 ## Agent Attributes
 
-| Attribute                  | Description                                                                                                                                                                                                                                    |
-| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Role**                   | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
-| **Goal**                   | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
-| **Backstory**              | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
-| **LLM** *(optional)*       | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
-| **Tools** *(optional)*     | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
-| **Function Calling LLM** *(optional)* | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
-| **Max Iter** *(optional)*  | `max_iter` is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
-| **Max RPM** *(optional)*   | `max_rpm` is Tte maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
-| **Max Execution Time** *(optional)*   | `max_execution_time` is the Maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
-| **Verbose** *(optional)*   | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
-| **Allow Delegation** *(optional)* | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
-| **Step Callback** *(optional)* | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
-| **Cache** *(optional)*     | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
-| **System Template** *(optional)*     | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Prompt Template** *(optional)*     | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Response Template** *(optional)*     | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
+| Attribute                  | Parameter  | Description                                                                                                                                                                                                                                    |
+| :------------------------- | :---- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Role**                   | `role`  | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
+| **Goal**                   | `goal`  | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
+| **Backstory**              | `backstory`  | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
+| **LLM** *(optional)*       | `llm`  | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
+| **Tools** *(optional)*     | `tools`  | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
+| **Function Calling LLM** *(optional)* | `function_calling_llm`  | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
+| **Max Iter** *(optional)*  | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
+| **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
+| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the Maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
+| **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
+| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
+| **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
+| **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
+| **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
 
 ## Creating an Agent
 

docs/core-concepts/Crews.md

@@ -8,29 +8,29 @@ A crew in crewAI represents a collaborative group of agents working together to
 
 ## Crew Attributes
 
-| Attribute                   | Description                                                  |
-| :-------------------------- | :----------------------------------------------------------- |
-| **Tasks**                   | A list of tasks assigned to the crew.                        |
-| **Agents**                  | A list of agents that are part of the crew.                  |
-| **Process** *(optional)*    | The process flow (e.g., sequential, hierarchical) the crew follows. |
-| **Verbose** *(optional)*    | The verbosity level for logging during execution.            |
-| **Manager LLM** *(optional)*| The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
-| **Function Calling LLM** *(optional)* | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
-| **Config** *(optional)*     | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
-| **Max RPM** *(optional)*    | Maximum requests per minute the crew adheres to during execution. |
-| **Language**  *(optional)*  | Language used for the crew, defaults to English.             |
-| **Language File** *(optional)* | Path to the language file to be used for the crew.          |
-| **Memory** *(optional)*     | Utilized for storing execution memories (short-term, long-term, entity memory). |
-| **Cache** *(optional)*      | Specifies whether to use a cache for storing the results of tools' execution. |
-| **Embedder** *(optional)*   | Configuration for the embedder to be used by the crew. Mostly used by memory for now.       |
-| **Full Output** *(optional)*| Whether the crew should return the full output with all tasks outputs or just the final output. |
-| **Step Callback** *(optional)* | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
-| **Task Callback** *(optional)* | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
-| **Share Crew** *(optional)* | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
-| **Output Log File** *(optional)* | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
-| **Manager Agent** *(optional)* | `manager` sets a custom agent that will be used as a manager. |
-| **Manager Callbacks** *(optional)* | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
-| **Prompt File** *(optional)* | Path to the prompt JSON file to be used for the crew. |
+| Attribute                   | Parameters          | Description                                                                                              |
+| :-------------------------- | :------------------ | :------------------------------------------------------------------------------------------------------- |
+| **Tasks**                   | `tasks`               | A list of tasks assigned to the crew.                                                                    |
+| **Agents**                  | `agents`              | A list of agents that are part of the crew.                                                              |
+| **Process** *(optional)*    | `process`             | The process flow (e.g., sequential, hierarchical) the crew follows.                                      |
+| **Verbose** *(optional)*    | `verbose`             | The verbosity level for logging during execution.                                                        |
+| **Manager LLM** *(optional)*| `manager_llm`         | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
+| **Function Calling LLM** *(optional)* | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
+| **Config** *(optional)*     | `config`              | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                       |
+| **Max RPM** *(optional)*    | `max_rpm`             | Maximum requests per minute the crew adheres to during execution.                                         |
+| **Language**  *(optional)*  | `language`            | Language used for the crew, defaults to English.                                                          |
+| **Language File** *(optional)* | `language_file`       | Path to the language file to be used for the crew.                                                        |
+| **Memory** *(optional)*     | `memory`              | Utilized for storing execution memories (short-term, long-term, entity memory).                           |
+| **Cache** *(optional)*      | `cache`               | Specifies whether to use a cache for storing the results of tools' execution.                             |
+| **Embedder** *(optional)*   | `embedder`            | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                     |
+| **Full Output** *(optional)*| `full_output`         | Whether the crew should return the full output with all tasks outputs or just the final output.           |
+| **Step Callback** *(optional)* | `step_callback`       | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
+| **Task Callback** *(optional)* | `task_callback`       | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
+| **Share Crew** *(optional)* | `share_crew`          | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
+| **Output Log File** *(optional)* | `output_log_file`    | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
+| **Manager Agent** *(optional)* | `manager_agent`      | `manager` sets a custom agent that will be used as a manager.                                             |
+| **Manager Callbacks** *(optional)* | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
+| **Prompt File** *(optional)* | `prompt_file`         | Path to the prompt JSON file to be used for the crew.                                                     |
 
 !!! note "Crew Max RPM"
     The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
@@ -123,7 +123,7 @@ result = my_crew.kickoff()
 print(result)
 ```
 
-### Different wayt to Kicking Off a Crew
+### Different ways to Kicking Off a Crew
 
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 

docs/core-concepts/Memory.md

@@ -12,7 +12,7 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
 | Component            | Description                                                  |
 | :------------------- | :----------------------------------------------------------- |
 | **Short-Term Memory**| Temporarily stores recent interactions and outcomes, enabling agents to recall and utilize information relevant to their current context during the current executions. |
-| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. So Agents can remeber what they did right and wrong across multiple executions |
+| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. So Agents can remember what they did right and wrong across multiple executions |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. |
 | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
 

docs/core-concepts/Tasks.md

@@ -11,20 +11,20 @@ Tasks within crewAI can be collaborative, requiring multiple agents to work toge
 
 ## Task Attributes
 
-| Attribute              | Description                                                                                   |
-| :----------------------| :-------------------------------------------------------------------------------------------- |
-| **Description**        | A clear, concise statement of what the task entails.                                          |
-| **Agent**              | The agent responsible for the task, assigned either directly or by the crew's process.        |
-| **Expected Output**    | A detailed description of what the task's completion looks like.                              |
-| **Tools** *(optional)* | The functions or capabilities the agent can utilize to perform the task.                      |
-| **Async Execution** *(optional)* | If set, the task executes asynchronously, allowing progression without waiting for completion.|
-| **Context**  *(optional)* | Specifies tasks whose outputs are used as context for this task.                              |
-| **Config** *(optional)* | Additional configuration details for the agent executing the task, allowing further customization. |
-| **Output JSON**  *(optional)* | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.         |
-| **Output Pydantic**  *(optional)* | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
-| **Output File**  *(optional)* | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Callback**  *(optional)* | A Python callable that is executed with the task's output upon completion.                    |
-| **Human Input** *(optional)* | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
+| Attribute              | Parameters           | Description                                                                                   |
+| :----------------------| :------------------- | :-------------------------------------------------------------------------------------------- |
+| **Description**        | `description`          | A clear, concise statement of what the task entails.                                          |
+| **Agent**              | `agent`                | The agent responsible for the task, assigned either directly or by the crew's process.        |
+| **Expected Output**    | `expected_output`      | A detailed description of what the task's completion looks like.                              |
+| **Tools** *(optional)* | `tools`                | The functions or capabilities the agent can utilize to perform the task.                      |
+| **Async Execution** *(optional)* | `async_execution`      | If set, the task executes asynchronously, allowing progression without waiting for completion.|
+| **Context**  *(optional)* | `context`              | Specifies tasks whose outputs are used as context for this task.                              |
+| **Config** *(optional)* | `config`               | Additional configuration details for the agent executing the task, allowing further customization. |
+| **Output JSON**  *(optional)* | `output_json`          | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.         |
+| **Output Pydantic**  *(optional)* | `output_pydantic`      | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
+| **Output File**  *(optional)* | `output_file`          | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
+| **Callback**  *(optional)* | `callback`             | A Python callable that is executed with the task's output upon completion.                    |
+| **Human Input** *(optional)* | `human_input`         | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
 
 ## Creating a Task
 

docs/core-concepts/Training-Crew.md

@@ -8,6 +8,7 @@ The training feature in CrewAI allows you to train your AI agents using the comm
 
 During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. This helps the agents improve their understanding, decision-making, and problem-solving abilities.
 
+### Training Your Crew Using the CLI
 To use the training feature, follow these steps:
 
 1. Open your terminal or command prompt.
@@ -18,7 +19,26 @@ To use the training feature, follow these steps:
 crewai train -n <n_iterations>
 ```
 
-Replace `<n_iterations>` with the desired number of training iterations. This determines how many times the agents will go through the training process.
+### Training Your Crew Programmatically
+To train your crew programmatically, use the following steps:
+
+1. Define the number of iterations for training.
+2. Specify the input parameters for the training process.
+3. Execute the training command within a try-except block to handle potential errors.
+
+```python
+    n_iterations = 2
+    inputs = {"topic": "CrewAI Training"}
+
+    try:
+        YourCrewName_Crew().crew().train(n_iterations= n_iterations, inputs=inputs)
+
+    except Exception as e:
+        raise Exception(f"An error occurred while training the crew: {e}")
+```
+
+!!! note "Replace `<n_iterations>` with the desired number of training iterations. This determines how many times the agents will go through the training process."
+
 
 ### Key Points to Note:
 - **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.

docs/how-to/Create-Custom-Tools.md

@@ -51,7 +51,7 @@ To optimize tool performance with caching, define custom caching strategies usin
 @tool("Tool with Caching")
 def cached_tool(argument: str) -> str:
     """Tool functionality description."""
-    return "Cachable result"
+    return "Cacheable result"
 
 def my_cache_strategy(arguments: dict, result: str) -> bool:
     # Define custom caching logic

docs/how-to/Creating-a-Crew-and-kick-it-off.md

@@ -79,5 +79,4 @@ manager = Agent(
 
 1. `allow_code_execution`: Enable or disable code execution capabilities for the agent (default is False).
 2. `max_execution_time`: Set a maximum execution time (in seconds) for the agent to complete a task.
-3. `function_calling_llm`: Specify a separate language model for function calling.
-4
+3. `function_calling_llm`: Specify a separate language model for function calling.

docs/how-to/Force-Tool-Ouput-as-Result.md

@@ -0,0 +1,31 @@
+---
+title: Forcing Tool Output as Result
+description: Learn how to force tool output as the result in of an Agent's task in crewAI.
+---
+
+## Introduction
+In CrewAI, you can force the output of a tool as the result of an agent's task. This feature is useful when you want to ensure that the tool output is captured and returned as the task result, and avoid the agent modifying the output during the task execution.
+
+## Forcing Tool Output as Result
+To force the tool output as the result of an agent's task, you can set the `force_tool_output` parameter to `True` when creating the task. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
+
+Here's an example of how to force the tool output as the result of an agent's task:
+
+```python
+# ...
+# Define a custom tool that returns the result as the answer
+coding_agent =Agent(
+        role="Data Scientist",
+        goal="Product amazing resports on AI",
+        backstory="You work with data and AI",
+        tools=[MyCustomTool(result_as_answer=True)],
+    )
+# ...
+```
+
+### Workflow in Action
+
+1. **Task Execution**: The agent executes the task using the tool provided.
+2. **Tool Output**: The tool generates the output, which is captured as the task result.
+3. **Agent Interaction**: The agent my reflect and take learnings from the tool but the output is not modified.
+4. **Result Return**: The tool output is returned as the task result without any modifications.

docs/how-to/Sequential.md

@@ -37,10 +37,9 @@ writer = Agent(
   backstory='A skilled writer with a talent for crafting compelling narratives'
 )
 
-# Define the tasks in sequence
-research_task = Task(description='Gather relevant data...', agent=researcher)
-analysis_task = Task(description='Analyze the data...', agent=analyst)
-writing_task = Task(description='Compose the report...', agent=writer)
+research_task = Task(description='Gather relevant data...', agent=researcher, expected_output='Raw Data')
+analysis_task = Task(description='Analyze the data...', agent=analyst, expected_output='Data Insights')
+writing_task = Task(description='Compose the report...', agent=writer, expected_output='Final Report')
 
 # Form the crew with a sequential process
 report_crew = Crew(
@@ -83,4 +82,4 @@ CrewAI tracks token usage across all tasks and agents. You can access these metr
 1. **Order Matters**: Arrange tasks in a logical sequence where each task builds upon the previous one.
 2. **Clear Task Descriptions**: Provide detailed descriptions for each task to guide the agents effectively.
 3. **Appropriate Agent Selection**: Match agents' skills and roles to the requirements of each task.
-4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones
+4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones

docs/how-to/Start-a-New-CrewAI-Project.md

@@ -0,0 +1,137 @@
+---
+title: Starting a New CrewAI Project
+description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
+---
+
+# Starting Your CrewAI Project
+
+Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
+
+## Prerequisites
+
+We assume you have already installed CrewAI. If not, please refer to the [installation guide](how-to/Installing-CrewAI.md) to install CrewAI and its dependencies.
+
+## Creating a New Project
+
+To create a new project, run the following CLI command:
+
+```shell
+$ crewai create my_project
+```
+
+This command will create a new project folder with the following structure:
+
+```shell
+my_project/
+├── .gitignore
+├── pyproject.toml
+├── README.md
+└── src/
+    └── my_project/
+        ├── __init__.py
+        ├── main.py
+        ├── crew.py
+        ├── tools/
+        │   ├── custom_tool.py
+        │   └── __init__.py
+        └── config/
+            ├── agents.yaml
+            └── tasks.yaml
+```
+
+You can now start developing your project by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of your project, and the `crew.py` file is where you define your agents and tasks.
+
+## Customizing Your Project
+
+To customize your project, you can:
+- Modify `src/my_project/config/agents.yaml` to define your agents.
+- Modify `src/my_project/config/tasks.yaml` to define your tasks.
+- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
+- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
+- Add your environment variables into the `.env` file.
+
+### Example: Defining Agents and Tasks
+
+#### agents.yaml
+
+```yaml
+researcher:
+  role: >
+    Job Candidate Researcher
+  goal: >
+    Find potential candidates for the job
+  backstory: >
+    You are adept at finding the right candidates by exploring various online
+    resources. Your skill in identifying suitable candidates ensures the best
+    match for job positions.
+```
+
+#### tasks.yaml
+
+```yaml
+research_candidates_task:
+  description: >
+    Conduct thorough research to find potential candidates for the specified job.
+    Utilize various online resources and databases to gather a comprehensive list of potential candidates.
+    Ensure that the candidates meet the job requirements provided.
+
+    Job Requirements:
+    {job_requirements}
+  expected_output: >
+    A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
+```
+
+## Installing Dependencies
+
+To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
+
+```shell
+$ cd my_project
+$ poetry lock
+$ poetry install
+```
+
+This will install the dependencies specified in the `pyproject.toml` file.
+
+## Interpolating Variables
+
+Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
+
+#### agents.yaml
+
+```yaml
+research_task:
+  description: >
+    Conduct a thorough research about the customer and competitors in the context
+    of {customer_domain}.
+    Make sure you find any interesting and relevant information given the
+    current year is 2024.
+  expected_output: >
+    A complete report on the customer and their customers and competitors,
+    including their demographics, preferences, market positioning and audience engagement.
+```
+
+#### main.py
+
+```python
+# main.py
+def run():
+    inputs = {
+        "customer_domain": "crewai.com"
+    }
+    MyProjectCrew(inputs).crew().kickoff(inputs=inputs)
+```
+
+## Running Your Project
+
+To run your project, use the following command:
+
+```shell
+$ poetry run my_project
+```
+
+This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
+
+## Deploying Your Project
+
+The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

docs/index.md

@@ -48,6 +48,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
     <div style="width:30%">
         <h2>How-To Guides</h2>
         <ul>
+            <li>
+                <a href="./how-to/Start-a-New-CrewAI-Project">
+                    Starting Your crewAI Project
+                </a>
+            </li>
             <li>
                 <a href="./how-to/Installing-CrewAI">
                     Installing crewAI
@@ -88,6 +93,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Coding Agents
                 </a>
             </li>
+            <li>
+                <a href="./how-to/Force-Tool-Ouput-as-Result">
+                    Forcing Tool Output as Result
+                </a>
+            </li>
             <li>
                 <a href="./how-to/Human-Input-on-Execution">
                     Human Input on Execution

docs/tools/GitHubSearchTool.md

@@ -4,7 +4,7 @@
     We are still working on improving tools, so there might be unexpected behavior or changes in the future.
 
 ## Description
-The GithubSearchTool is a Read, Append, and Generate (RAG) tool specifically designed for conducting semantic searches within GitHub repositories. Utilizing advanced semantic search capabilities, it sifts through code, pull requests, issues, and repositories, making it an essential tool for developers, researchers, or anyone in need of precise information from GitHub.
+The GithubSearchTool is a Retrieval-Augmented Generation (RAG) tool specifically designed for conducting semantic searches within GitHub repositories. Utilizing advanced semantic search capabilities, it sifts through code, pull requests, issues, and repositories, making it an essential tool for developers, researchers, or anyone in need of precise information from GitHub.
 
 ## Installation
 To use the GithubSearchTool, first ensure the crewai_tools package is installed in your Python environment:

docs/tools/MDXSearchTool.md

@@ -4,7 +4,7 @@
     The MDXSearchTool is in continuous development. Features may be added or removed, and functionality could change unpredictably as we refine the tool.
 
 ## Description
-The MDX Search Tool is a component of the `crewai_tools` package aimed at facilitating advanced market data extraction. This tool is invaluable for researchers and analysts seeking quick access to market insights, especially within the AI sector. It simplifies the task of acquiring, interpreting, and organizing market data by interfacing with various data sources.
+The MDX Search Tool is a component of the `crewai_tools` package aimed at facilitating advanced markdown language extraction. It enables users to effectively search and extract relevant information from MD files using query-based searches. This tool is invaluable for data analysis, information management, and research tasks, streamlining the process of finding specific information within large document collections.
 
 ## Installation
 Before using the MDX Search Tool, ensure the `crewai_tools` package is installed. If it is not, you can install it with the following command:
@@ -59,4 +59,4 @@ tool = MDXSearchTool(
         ),
     )
 )
-```
+```

docs/tools/TXTSearchTool.md

@@ -31,7 +31,7 @@ tool = TXTSearchTool(txt='path/to/text/file.txt')
 ```
 
 ## Arguments
-- `txt` (str): **Optinal**. The path to the text file you want to search. This argument is only required if the tool was not initialized with a specific text file; otherwise, the search will be conducted within the initially provided text file.
+- `txt` (str): **Optional**. The path to the text file you want to search. This argument is only required if the tool was not initialized with a specific text file; otherwise, the search will be conducted within the initially provided text file.
 
 ## Custom model and embeddings
 

mkdocs.yml

@@ -131,6 +131,7 @@ nav:
     - Using LangChain Tools: 'core-concepts/Using-LangChain-Tools.md'
     - Using LlamaIndex Tools: 'core-concepts/Using-LlamaIndex-Tools.md'
   - How to Guides:
+    - Starting Your crewAI Project: 'how-to/Start-a-New-CrewAI-Project.md'
     - Installing CrewAI: 'how-to/Installing-CrewAI.md'
     - Getting Started: 'how-to/Creating-a-Crew-and-kick-it-off.md'
     - Create Custom Tools: 'how-to/Create-Custom-Tools.md'
@@ -140,6 +141,7 @@ nav:
     - Connecting to any LLM: 'how-to/LLM-Connections.md'
     - Customizing Agents: 'how-to/Customizing-Agents.md'
     - Coding Agents: 'how-to/Coding-Agents.md'
+    - Forcing Tool Output as Result: 'how-to/Force-Tool-Ouput-as-Result.md'
     - Human Input on Execution: 'how-to/Human-Input-on-Execution.md'
     - Kickoff a Crew Asynchronously: 'how-to/Kickoff-async.md'
     - Kickoff a Crew for a List: 'how-to/Kickoff-for-each.md'

poetry.lock

@@ -1,5 +1,25 @@
 # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
+[[package]]
+name = "agentops"
+version = "0.1.12"
+description = "Python SDK for developing AI agent evals and observability"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "agentops-0.1.12-py3-none-any.whl", hash = "sha256:b4b47c990638b74810cc1c38624ada162094b46e3fdd63883642a16bc5258386"},
+    {file = "agentops-0.1.12.tar.gz", hash = "sha256:c4f762482fb240fc3503907f52498f2d8d9e4f80236ee4a12bf039317a85fcd7"},
+]
+
+[package.dependencies]
+packaging = "23.2"
+psutil = "5.9.8"
+requests = "2.31.0"
+
+[package.extras]
+dev = ["pytest (==7.4.0)", "requests-mock (==1.11.0)"]
+langchain = ["langchain (>=1.19,<2.0)"]
+
 [[package]]
 name = "aiohttp"
 version = "3.9.5"
@@ -323,17 +343,17 @@ lxml = ["lxml"]
 
 [[package]]
 name = "boto3"
-version = "1.34.137"
+version = "1.34.140"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.137-py3-none-any.whl", hash = "sha256:7cb697d67fd138ceebc6f789919ae370c092a50c6b0ccc4ef483027935502eab"},
-    {file = "boto3-1.34.137.tar.gz", hash = "sha256:0b21b84db4619b3711a6f643d465a5a25e81231ee43615c55a20ff6b89c6cc3c"},
+    {file = "boto3-1.34.140-py3-none-any.whl", hash = "sha256:23ca8d8f7a30c3bbd989808056b5fc5d68ff5121c02c722c6167b6b1bb7f8726"},
+    {file = "boto3-1.34.140.tar.gz", hash = "sha256:578bbd5e356005719b6b610d03edff7ea1b0824d078afe62d3fb8bea72f83a87"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.137,<1.35.0"
+botocore = ">=1.34.140,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -342,13 +362,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.137"
+version = "1.34.140"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.137-py3-none-any.whl", hash = "sha256:a980fa4adec4bfa23fff70a3512622e9412c69c791898a52cafc2458b0be6040"},
-    {file = "botocore-1.34.137.tar.gz", hash = "sha256:e29c8e9bfda0b20a1997792968e85868bfce42fefad9730f633a81adcff3f2ef"},
+    {file = "botocore-1.34.140-py3-none-any.whl", hash = "sha256:43940d3a67d946ba3301631ba4078476a75f1015d4fb0fb0272d0b754b2cf9de"},
+    {file = "botocore-1.34.140.tar.gz", hash = "sha256:86302b2226c743b9eec7915a4c6cfaffd338ae03989cd9ee181078ef39d1ab39"},
 ]
 
 [package.dependencies]
@@ -438,13 +458,13 @@ test = ["flake8", "isort", "pytest"]
 
 [[package]]
 name = "certifi"
-version = "2024.6.2"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
-    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]
 
 [[package]]
@@ -727,13 +747,13 @@ all = ["pycocotools (==2.0.6)"]
 
 [[package]]
 name = "clarifai-grpc"
-version = "10.5.4"
+version = "10.6.1"
 description = "Clarifai gRPC API Client"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "clarifai_grpc-10.5.4-py3-none-any.whl", hash = "sha256:ae4c4d8985fdd2bf326cec27ee834571e44d0e989fb12686dd681f9b553ae218"},
-    {file = "clarifai_grpc-10.5.4.tar.gz", hash = "sha256:c67ce0dde186e8bab0d42a9923d28ddb4a05017b826c8e52ac7a86ec6df5f12a"},
+    {file = "clarifai_grpc-10.6.1-py3-none-any.whl", hash = "sha256:7f07c262f46042995b11af10cdd552718c4487e955db1b3f1253fcb0c2ab1ce1"},
+    {file = "clarifai_grpc-10.6.1.tar.gz", hash = "sha256:f692e3d6a051a1228ca371c3a9dc705cc9a61334eecc454d056f7af0b6f4dbad"},
 ]
 
 [package.dependencies]
@@ -820,13 +840,13 @@ files = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.4.5"
+version = "0.4.8"
 description = "Set of tools for the crewAI framework"
 optional = false
 python-versions = "<=3.13,>=3.10"
 files = [
-    {file = "crewai_tools-0.4.5-py3-none-any.whl", hash = "sha256:d970a152a69d039eb23150755d4dc9e7c6ef9176b19a80348142619518e39b17"},
-    {file = "crewai_tools-0.4.5.tar.gz", hash = "sha256:1d7763f20afd95c6be70d31f9f0e9334cb42be127c17feddce368907077a6543"},
+    {file = "crewai_tools-0.4.8-py3-none-any.whl", hash = "sha256:628b08515ee0e06c751da1dd66b0cff70c9b2644775891c8f59883cb5debfef4"},
+    {file = "crewai_tools-0.4.8.tar.gz", hash = "sha256:ae190bd187f980163523c86ee7e1eb2ed78896f935d6caff98908dd7ab6c982b"},
 ]
 
 [package.dependencies]
@@ -834,9 +854,9 @@ beautifulsoup4 = ">=4.12.3,<5.0.0"
 chromadb = ">=0.4.22,<0.5.0"
 docker = ">=7.1.0,<8.0.0"
 docx2txt = ">=0.8,<0.9"
-embedchain = ">=0.1.113,<0.2.0"
+embedchain = ">=0.1.114,<0.2.0"
 lancedb = ">=0.5.4,<0.6.0"
-langchain = ">=0.1.4,<0.2.0"
+langchain = ">0.2,<=0.3"
 openai = ">=1.12.0,<2.0.0"
 pydantic = ">=2.6.1,<3.0.0"
 pyright = ">=1.1.350,<2.0.0"
@@ -1034,13 +1054,13 @@ idna = ">=2.0.0"
 
 [[package]]
 name = "embedchain"
-version = "0.1.113"
+version = "0.1.116"
 description = "Simplest open source retrieval (RAG) framework"
 optional = false
 python-versions = "<=3.13,>=3.9"
 files = [
-    {file = "embedchain-0.1.113-py3-none-any.whl", hash = "sha256:f37b029d8f8509a5db99d1579168ab2ba7d5841c280289f6a2ae702601caf96f"},
-    {file = "embedchain-0.1.113.tar.gz", hash = "sha256:5477012d37912a0e89758263b1a8db4699b7d0dedd7f18ccc89f3381d6b9173d"},
+    {file = "embedchain-0.1.116-py3-none-any.whl", hash = "sha256:388835d047f9ff4542ebf50e3fa633ef596db262cbe506195ee4976b91a49172"},
+    {file = "embedchain-0.1.116.tar.gz", hash = "sha256:3e4d6418df2e749c2bd3cd3153c3857cbecd7227afe40b87d5ac3df629c394b2"},
 ]
 
 [package.dependencies]
@@ -1048,11 +1068,14 @@ alembic = ">=1.13.1,<2.0.0"
 beautifulsoup4 = ">=4.12.2,<5.0.0"
 chromadb = ">=0.4.24,<0.5.0"
 clarifai = ">=10.0.1,<11.0.0"
+cohere = ">=5.3,<6.0"
 google-cloud-aiplatform = ">=1.26.1,<2.0.0"
 gptcache = ">=0.1.43,<0.2.0"
-langchain = ">=0.1.4,<0.2.0"
+langchain = ">0.2,<=0.3"
 langchain-cohere = ">=0.1.4,<0.2.0"
+langchain-community = ">=0.2.6,<0.3.0"
 langchain-openai = ">=0.1.7,<0.2.0"
+memzero = ">=0.0.7,<0.0.8"
 openai = ">=1.1.1"
 posthog = ">=3.0.2,<4.0.0"
 pypdf = ">=4.0.1,<5.0.0"
@@ -1065,7 +1088,6 @@ tiktoken = ">=0.7.0,<0.8.0"
 
 [package.extras]
 aws-bedrock = ["boto3 (>=1.34.20,<2.0.0)"]
-cohere = ["cohere (>=5.3,<6.0)"]
 dataloaders = ["docx2txt (>=0.8,<0.9)", "duckduckgo-search (>=6.1.5,<7.0.0)", "pytube (>=15.0.0,<16.0.0)", "sentence-transformers (>=2.2.2,<3.0.0)", "youtube-transcript-api (>=0.6.1,<0.7.0)"]
 discord = ["discord (>=2.3.2,<3.0.0)"]
 dropbox = ["dropbox (>=11.36.2,<12.0.0)"]
@@ -1078,7 +1100,7 @@ huggingface-hub = ["huggingface_hub (>=0.17.3,<0.18.0)"]
 lancedb = ["lancedb (>=0.6.2,<0.7.0)"]
 llama2 = ["replicate (>=0.15.4,<0.16.0)"]
 milvus = ["pymilvus (==2.4.3)"]
-mistralai = ["langchain-mistralai (>=0.0.3,<0.0.4)"]
+mistralai = ["langchain-mistralai (>=0.1.9,<0.2.0)"]
 modal = ["modal (>=0.56.4329,<0.57.0)"]
 mysql = ["mysql-connector-python (>=8.1.0,<9.0.0)"]
 opensearch = ["opensearch-py (==2.3.1)"]
@@ -1089,7 +1111,7 @@ qdrant = ["qdrant-client (>=1.6.3,<2.0.0)"]
 rss-feed = ["feedparser (>=6.0.10,<7.0.0)", "listparser (>=0.19,<0.20)", "newspaper3k (>=0.2.8,<0.3.0)"]
 slack = ["flask (>=2.3.3,<3.0.0)", "slack-sdk (==3.21.3)"]
 together = ["together (>=0.2.8,<0.3.0)"]
-vertexai = ["langchain-google-vertexai (>=0.0.5,<0.0.6)"]
+vertexai = ["langchain-google-vertexai (>=1.0.6,<2.0.0)"]
 weaviate = ["weaviate-client (>=3.24.1,<4.0.0)"]
 whatsapp = ["flask (>=2.3.3,<3.0.0)", "twilio (>=8.5.0,<9.0.0)"]
 youtube = ["youtube-transcript-api (>=0.6.1,<0.7.0)", "yt_dlp (>=2023.11.14,<2024.0.0)"]
@@ -1423,13 +1445,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.57.0"
+version = "1.58.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.57.0.tar.gz", hash = "sha256:113905f100cb0a9ad744a2445a7675f92f28600233ba499614aa704d11a809b7"},
-    {file = "google_cloud_aiplatform-1.57.0-py2.py3-none-any.whl", hash = "sha256:ca5391a56e0cc8f4ed39a2beb7be02f51936ff04fd5304775a72a86c345d0e47"},
+    {file = "google-cloud-aiplatform-1.58.0.tar.gz", hash = "sha256:7a05aceac4a6c7eaa26e684e9f202b829cc7e57f82bffe7281684275a553fcad"},
+    {file = "google_cloud_aiplatform-1.58.0-py2.py3-none-any.whl", hash = "sha256:21f1320860f4916183ec939fdf2ff3fc1d7fdde97fe5795974257ab21f9458ec"},
 ]
 
 [package.dependencies]
@@ -1450,7 +1472,7 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
 cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
-full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"]
+full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "tenacity (<=8.3)"]
 langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
@@ -1459,12 +1481,12 @@ pipelines = ["pyyaml (>=5.3.1,<7)"]
 prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<=0.109.1)", "httpx (>=0.23.0,<0.25.0)", "starlette (>=0.17.1)", "uvicorn[standard] (>=0.16.0)"]
 preview = ["cloudpickle (<3.0)", "google-cloud-logging (<4.0)"]
 private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
-rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"]
+rapid-evaluation = ["pandas (>=1.0.0,<2.2.0)", "tqdm (>=4.23.0)"]
 ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "setuptools (<70.0.0)"]
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 tokenization = ["sentencepiece (>=0.2.0)"]
 vizier = ["google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
@@ -2030,13 +2052,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "identify"
-version = "2.5.36"
+version = "2.6.0"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.5.36-py2.py3-none-any.whl", hash = "sha256:37d93f380f4de590500d9dba7db359d0d3da95ffe7f9de1753faa159e71e7dfa"},
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+    {file = "identify-2.6.0-py2.py3-none-any.whl", hash = "sha256:e79ae4406387a9d300332b5fd366d8994f1525e8414984e1a59e058b2eda2dd0"},
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 ]
 
 [package.extras]
@@ -2357,156 +2379,140 @@ tests = ["aiohttp", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "py
 
 [[package]]
 name = "langchain"
-version = "0.1.20"
+version = "0.2.6"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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-langchain-core = ">=0.1.52,<0.2.0"
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+    {version = ">=1,<2", markers = "python_version < \"3.12\""},
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 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
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 description = "An integration package connecting Cohere and LangChain"
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 python-versions = "<4.0,>=3.8.1"
 files = [
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 [[package]]
 name = "langchain-community"
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 description = "Community contributed LangChain integrations."
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 python-versions = "<4.0,>=3.8.1"
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 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
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-
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-extended-testing = ["jinja2 (>=3,<4)"]
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-openai"
-version = "0.1.7"
+version = "0.1.14"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = "<4.0,>=3.8.1"
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 tiktoken = ">=0.7,<1"
 
 [[package]]
 name = "langchain-text-splitters"
-version = "0.0.2"
+version = "0.2.2"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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-
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+langchain-core = ">=0.2.10,<0.3.0"
 
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+version = "0.1.84"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
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+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
 ]
 requests = ">=2,<3"
 
@@ -2667,6 +2673,22 @@ files = [
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 ]
 
+[[package]]
+name = "memzero"
+version = "0.0.7"
+description = "Long-term memory for AI Agents"
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
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+posthog = ">=3.5.0,<4.0.0"
+pydantic = ">=2.7.3,<3.0.0"
+
 [[package]]
 name = "mergedeep"
 version = "1.3.4"
@@ -2742,13 +2764,13 @@ pyyaml = ">=5.1"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.27"
+version = "9.5.28"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -3214,13 +3236,13 @@ sympy = "*"
 
 [[package]]
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+version = "1.35.10"
 description = "The official Python library for the openai API"
 optional = false
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 files = [
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 [package.dependencies]
@@ -3419,57 +3441,62 @@ files = [
 
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+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
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+
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 version = "3.5.0"
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 ]
 
 [package.dependencies]
@@ -4163,13 +4219,13 @@ files = [
 
 [[package]]
 name = "pyright"
-version = "1.1.369"
+version = "1.1.370"
 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pyright-1.1.369-py3-none-any.whl", hash = "sha256:06d5167a8d7be62523ced0265c5d2f1e022e110caf57a25d92f50fb2d07bcda0"},
-    {file = "pyright-1.1.369.tar.gz", hash = "sha256:ad290710072d021e213b98cc7a2f90ae3a48609ef5b978f749346d1a47eb9af8"},
+    {file = "pyright-1.1.370-py3-none-any.whl", hash = "sha256:fc721601e480a69989775bfc210534a6ca0110ebd0c065244a8d3a151294fc61"},
+    {file = "pyright-1.1.370.tar.gz", hash = "sha256:d0d559d506fc41e3297f721aaa05a1b9f06beda5acc9ac64ca371ce94c28f960"},
 ]
 
 [package.dependencies]
@@ -4588,13 +4644,13 @@ files = [
 
 [[package]]
 name = "requests"
-version = "2.32.3"
+version = "2.31.0"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
-    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
+    {file = "requests-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
+    {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
 ]
 
 [package.dependencies]
@@ -4994,13 +5050,13 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tenacity"
-version = "8.4.2"
+version = "8.5.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tenacity-8.4.2-py3-none-any.whl", hash = "sha256:9e6f7cf7da729125c7437222f8a522279751cdfbe6b67bfe64f75d3a348661b2"},
-    {file = "tenacity-8.4.2.tar.gz", hash = "sha256:cd80a53a79336edba8489e767f729e4f391c896956b57140b5d7511a64bbd3ef"},
+    {file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
+    {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
 ]
 
 [package.extras]
@@ -5227,13 +5283,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "trio"
-version = "0.25.1"
+version = "0.26.0"
 description = "A friendly Python library for async concurrency and I/O"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "trio-0.25.1-py3-none-any.whl", hash = "sha256:e42617ba091e7b2e50c899052e83a3c403101841de925187f61e7b7eaebdf3fb"},
-    {file = "trio-0.25.1.tar.gz", hash = "sha256:9f5314f014ea3af489e77b001861c535005c3858d38ec46b6b071ebfa339d7fb"},
+    {file = "trio-0.26.0-py3-none-any.whl", hash = "sha256:bb9c1b259591af941fccfbabbdc65bc7ed764bd2db76428454c894cd5e3d2032"},
+    {file = "trio-0.26.0.tar.gz", hash = "sha256:67c5ec3265dd4abc7b1d1ab9ca4fe4c25b896f9c93dac73713778adab487f9c4"},
 ]
 
 [package.dependencies]
@@ -6028,9 +6084,10 @@ doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linke
 test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
+agentops = ["agentops"]
 tools = ["crewai-tools"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<=3.13"
-content-hash = "d4ea0d71723ecc2bad629c387dd786b6a96c553b1e3e298516fdfcd2059d1019"
+content-hash = "0dbf6f6e2e841fb3eec4ff87ea5d6b430f29702118fee91307983c6b2581e59e"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.35.7"
+version = "0.36.0"
 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."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
@@ -14,22 +14,24 @@ Repository = "https://github.com/joaomdmoura/crewai"
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
 pydantic = "^2.4.2"
-langchain = ">=0.1.4,<0.2.0"
+langchain = ">0.2,<=0.3"
 openai = "^1.13.3"
 opentelemetry-api = "^1.22.0"
 opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
 regex = "^2023.12.25"
-crewai-tools = { version = "^0.4.5", optional = true }
+crewai-tools = { version = "^0.4.8", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
 jsonref = "^1.1.0"
-embedchain = "^0.1.113"
+agentops = { version = "^0.1.9", optional = true }
+embedchain = "^0.1.114"
 
 [tool.poetry.extras]
 tools = ["crewai-tools"]
+agentops = ["agentops"]
 
 [tool.poetry.group.dev.dependencies]
 isort = "^5.13.2"
@@ -43,7 +45,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.4.5"
+crewai-tools = "^0.4.8"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"
@@ -60,4 +62,4 @@ exclude = ["cli/templates/main.py", "cli/templates/crew.py"]
 
 [build-system]
 requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+build-backend = "poetry.core.masonry.api"

src/crewai/agent.py

@@ -7,19 +7,31 @@ from langchain.tools.render import render_text_description
 from langchain_core.agents import AgentAction
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_openai import ChatOpenAI
-
 from pydantic import Field, InstanceOf, model_validator
 
 from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser
+from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.tools.agent_tools import AgentTools
-from crewai.utilities import Prompts, Converter
+from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.token_counter_callback import TokenCalcHandler
-from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.utilities.training_handler import CrewTrainingHandler
 
+agentops = None
+try:
+    import agentops  # type: ignore # Name "agentops" already defined on line 21
+    from agentops import track_agent
+except ImportError:
 
+    def track_agent():
+        def noop(f):
+            return f
+
+        return noop
+
+
+@track_agent()
 class Agent(BaseAgent):
     """Represents an agent in a system.
 
@@ -48,6 +60,8 @@ class Agent(BaseAgent):
         default=None,
         description="Maximum execution time for an agent to execute a task",
     )
+    agent_ops_agent_name: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
+    agent_ops_agent_id: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
     cache_handler: InstanceOf[CacheHandler] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
@@ -76,7 +90,9 @@ class Agent(BaseAgent):
     response_template: Optional[str] = Field(
         default=None, description="Response format for the agent."
     )
-
+    tools_results: Optional[List[Any]] = Field(
+        default=[], description="Results of the tools used by the agent."
+    )
     allow_code_execution: Optional[bool] = Field(
         default=False, description="Enable code execution for the agent."
     )
@@ -84,10 +100,11 @@ class Agent(BaseAgent):
     def __init__(__pydantic_self__, **data):
         config = data.pop("config", {})
         super().__init__(**config, **data)
+        __pydantic_self__.agent_ops_agent_name = __pydantic_self__.role
 
     @model_validator(mode="after")
     def set_agent_executor(self) -> "Agent":
-        """Ensure agent executor and token process is set."""
+        """Ensure agent executor and token process are set."""
         if hasattr(self.llm, "model_name"):
             token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)
 
@@ -101,6 +118,13 @@ class Agent(BaseAgent):
             ):
                 self.llm.callbacks.append(token_handler)
 
+            if agentops and not any(
+                isinstance(handler, agentops.LangchainCallbackHandler)
+                for handler in self.llm.callbacks
+            ):
+                agentops.stop_instrumenting()
+                self.llm.callbacks.append(agentops.LangchainCallbackHandler())
+
         if not self.agent_executor:
             if not self.cache_handler:
                 self.cache_handler = CacheHandler()
@@ -124,8 +148,7 @@ class Agent(BaseAgent):
             Output of the agent
         """
         if self.tools_handler:
-            # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
-            self.tools_handler.last_used_tool = {}
+            self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
 
         task_prompt = task.prompt()
 
@@ -145,8 +168,8 @@ class Agent(BaseAgent):
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
         tools = tools or self.tools
-        # type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]"
-        parsed_tools = self._parse_tools(tools or [])
+
+        parsed_tools = self._parse_tools(tools or [])  # type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]"
         self.create_agent_executor(tools=tools)
         self.agent_executor.tools = parsed_tools
         self.agent_executor.task = task
@@ -168,6 +191,14 @@ class Agent(BaseAgent):
         )["output"]
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
+
+        # If there was any tool in self.tools_results that had result_as_answer
+        # set to True, return the results of the last tool that had
+        # result_as_answer set to True
+        for tool_result in self.tools_results:  # type: ignore # Item "None" of "list[Any] | None" has no attribute "__iter__" (not iterable)
+            if tool_result.get("result_as_answer", False):
+                result = tool_result["result"]
+
         return result
 
     def format_log_to_str(
@@ -268,7 +299,7 @@ class Agent(BaseAgent):
     def get_output_converter(self, llm, text, model, instructions):
         return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
-    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:
+    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:  # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
         """Parse tools to be used for the task."""
         tools_list = []
         try:

src/crewai/agents/agent_builder/base_agent.py

@@ -1,22 +1,26 @@
-from copy import deepcopy
 import uuid
-from typing import Any, Dict, List, Optional
 from abc import ABC, abstractmethod
+from copy import copy as shallow_copy
+from typing import Any, Dict, List, Optional, TypeVar
+
 from pydantic import (
     UUID4,
     BaseModel,
+    ConfigDict,
     Field,
     InstanceOf,
+    PrivateAttr,
     field_validator,
     model_validator,
-    ConfigDict,
-    PrivateAttr,
 )
 from pydantic_core import PydanticCustomError
 
-from crewai.utilities import I18N, RPMController, Logger
-from crewai.agents import CacheHandler, ToolsHandler
-from crewai.utilities.token_counter_callback import TokenProcess
+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.utilities import I18N, Logger, RPMController
+
+T = TypeVar("T", bound="BaseAgent")
 
 
 class BaseAgent(ABC, BaseModel):
@@ -187,6 +191,31 @@ class BaseAgent(ABC, BaseModel):
         """Get the converter class for the agent to create json/pydantic outputs."""
         pass
 
+    def copy(self: T) -> T:  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"
+        """Create a deep copy of the Agent."""
+        exclude = {
+            "id",
+            "_logger",
+            "_rpm_controller",
+            "_request_within_rpm_limit",
+            "_token_process",
+            "agent_executor",
+            "tools",
+            "tools_handler",
+            "cache_handler",
+            "llm",
+        }
+
+        # Copy llm and clear callbacks
+        existing_llm = shallow_copy(self.llm)
+        existing_llm.callbacks = []
+        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)
+
+        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:
@@ -216,35 +245,6 @@ class BaseAgent(ABC, BaseModel):
     def increment_formatting_errors(self) -> None:
         self.formatting_errors += 1
 
-    def copy(self):
-        exclude = {
-            "id",
-            "_logger",
-            "_rpm_controller",
-            "_request_within_rpm_limit",
-            "token_process",
-            "agent_executor",
-            "tools",
-            "tools_handler",
-            "cache_handler",
-            "crew",
-            "llm",
-        }
-
-        copied_data = self.model_dump(exclude=exclude, exclude_unset=True)
-        copied_agent = self.__class__(**copied_data)
-
-        # Copy mutable attributes separately
-        copied_agent.tools = deepcopy(self.tools)
-        copied_agent.config = deepcopy(self.config)
-
-        # Preserve original values for interpolation
-        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 set_rpm_controller(self, rpm_controller: RPMController) -> None:
         """Set the rpm controller for the agent.
 

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -1,65 +1,109 @@
 import time
+from typing import TYPE_CHECKING, Optional
 
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.utilities.converter import ConverterError
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
+from crewai.utilities import I18N
+
+
+if TYPE_CHECKING:
+    from crewai.crew import Crew
+    from crewai.task import Task
+    from crewai.agents.agent_builder.base_agent import BaseAgent
 
 
 class CrewAgentExecutorMixin:
+    crew: Optional["Crew"]
+    crew_agent: Optional["BaseAgent"]
+    task: Optional["Task"]
+    iterations: int
+    force_answer_max_iterations: int
+    have_forced_answer: bool
+    _i18n: I18N
+
     def _should_force_answer(self) -> bool:
+        """Determine if a forced answer is required based on iteration count."""
         return (
             self.iterations == self.force_answer_max_iterations
         ) 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 (
+            self.crew
+            and self.crew_agent
+            and self.task
+            and "Action: Delegate work to coworker" not in output.log
+        ):
+            try:
+                memory = ShortTermMemoryItem(
+                    data=output.log,
+                    agent=self.crew_agent.role,
+                    metadata={
+                        "observation": self.task.description,
+                    },
+                )
+                if (
+                    hasattr(self.crew, "_short_term_memory")
+                    and self.crew._short_term_memory
+                ):
+                    self.crew._short_term_memory.save(memory)
+            except Exception as e:
+                print(f"Failed to add to short term memory: {e}")
+                pass
+
+    def _create_long_term_memory(self, output) -> None:
+        """Create and save long-term and entity memory items based on evaluation."""
         if (
             self.crew
             and self.crew.memory
-            and "Action: Delegate work to coworker" not in output.log
+            and self.crew._long_term_memory
+            and self.crew._entity_memory
+            and self.task
+            and self.crew_agent
         ):
-            memory = ShortTermMemoryItem(
-                data=output.log,
-                agent=self.crew_agent.role,
-                metadata={
-                    "observation": self.task.description,
-                },
-            )
-            self.crew._short_term_memory.save(memory)
+            try:
+                ltm_agent = TaskEvaluator(self.crew_agent)
+                evaluation = ltm_agent.evaluate(self.task, output.log)
 
-    def _create_long_term_memory(self, output) -> None:
-        if self.crew and self.crew.memory:
-            ltm_agent = TaskEvaluator(self.crew_agent)
-            evaluation = ltm_agent.evaluate(self.task, output.log)
+                if isinstance(evaluation, ConverterError):
+                    return
 
-            if isinstance(evaluation, ConverterError):
-                return
-
-            long_term_memory = LongTermMemoryItem(
-                task=self.task.description,
-                agent=self.crew_agent.role,
-                quality=evaluation.quality,
-                datetime=str(time.time()),
-                expected_output=self.task.expected_output,
-                metadata={
-                    "suggestions": evaluation.suggestions,
-                    "quality": evaluation.quality,
-                },
-            )
-            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]),
+                long_term_memory = LongTermMemoryItem(
+                    task=self.task.description,
+                    agent=self.crew_agent.role,
+                    quality=evaluation.quality,
+                    datetime=str(time.time()),
+                    expected_output=self.task.expected_output,
+                    metadata={
+                        "suggestions": evaluation.suggestions,
+                        "quality": evaluation.quality,
+                    },
                 )
-                self.crew._entity_memory.save(entity_memory)
+                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)
+            except AttributeError as e:
+                print(f"Missing attributes for long term memory: {e}")
+                pass
+            except Exception as e:
+                print(f"Failed to add to long term memory: {e}")
+                pass
 
     def _ask_human_input(self, final_answer: dict) -> str:
-        """Get human input."""
+        """Prompt human input for final decision making."""
         return input(
             self._i18n.slice("getting_input").format(final_answer=final_answer)
         )

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

@@ -1,6 +1,8 @@
 from abc import ABC, abstractmethod
 from typing import List, Optional, Union
+
 from pydantic import BaseModel, Field
+
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
 from crewai.utilities import I18N
@@ -53,7 +55,7 @@ class BaseAgentTools(BaseModel, ABC):
             # {"task": "....", "coworker": "...."}
             agent_name = agent.casefold().replace('"', "").replace("\n", "")
 
-            agent = [
+            agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
                 available_agent
                 for available_agent in self.agents
                 if available_agent.role.casefold().replace("\n", "") == agent_name
@@ -73,9 +75,9 @@ class BaseAgentTools(BaseModel, ABC):
             )
 
         agent = agent[0]
-        task = Task(
+        task = Task(  # type: ignore # Incompatible types in assignment (expression has type "Task", variable has type "str")
             description=task,
             agent=agent,
             expected_output="Your best answer to your coworker asking you this, accounting for the context shared.",
         )
-        return agent.execute_task(task, context)
+        return agent.execute_task(task, context)  # type: ignore # "str" has no attribute "execute_task"

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

@@ -1,7 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import Any, Optional
 
-
 from pydantic import BaseModel, Field, PrivateAttr
 
 
@@ -27,8 +26,8 @@ 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_attemps: Optional[int] = Field(
-        description="Max number of attemps to try to get the output formated.",
+    max_attempts: Optional[int] = Field(
+        description="Max number of attempts to try to get the output formatted.",
         default=3,
     )
 
@@ -42,7 +41,7 @@ class OutputConverter(BaseModel, ABC):
         """Convert text to json."""
         pass
 
-    @abstractmethod
-    def _is_gpt(self, llm):
+    @abstractmethod  # type: ignore #  Name "_is_gpt" already defined on line 25
+    def _is_gpt(self, llm):  # type: ignore #  Name "_is_gpt" already defined on line 25
         """Return if llm provided is of gpt from openai."""
         pass

src/crewai/agents/executor.py

@@ -1,18 +1,27 @@
 import threading
 import time
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+from typing import (
+    Any,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Union,
+)
 
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain_core.agents import AgentAction, AgentFinish, AgentStep
 from langchain_core.exceptions import OutputParserException
-
 from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
 from pydantic import InstanceOf
-from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 
+from crewai.agents.agent_builder.base_agent_executor_mixin import (
+    CrewAgentExecutorMixin,
+)
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N
@@ -36,7 +45,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
     tools_handler: Optional[InstanceOf[ToolsHandler]] = None
     max_iterations: Optional[int] = 15
     have_forced_answer: bool = False
-    force_answer_max_iterations: Optional[int] = None
+    force_answer_max_iterations: Optional[int] = None  # type: ignore # Incompatible types in assignment (expression has type "int | None", base class "CrewAgentExecutorMixin" defined the type as "int")
     step_callback: Optional[Any] = None
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
@@ -233,6 +242,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                 tools_names=self.tools_names,
                 function_calling_llm=self.function_calling_llm,
                 task=self.task,
+                agent=self.crew_agent,
                 action=agent_action,
             )
             tool_calling = tool_usage.parse(agent_action.log)

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

@@ -12,4 +12,4 @@ reporting_task:
     Make sure the report is detailed and contains any and all relevant information.
   expected_output: >
     A fully fledge reports with the mains topics, each with a full section of information.
-    Formated as markdown with out '```'
+    Formatted as markdown without '```'

src/crewai/cli/templates/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.35.7" }
+crewai = { extras = ["tools"], version = "^0.35.8" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"

src/crewai/crew.py

@@ -1,7 +1,8 @@
 import asyncio
 import json
 import uuid
-from typing import Any, Dict, List, Optional, Union
+from concurrent.futures import Future
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 from langchain_core.callbacks import BaseCallbackHandler
 from pydantic import (
@@ -20,17 +21,29 @@ from pydantic_core import PydanticCustomError
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
+from crewai.crews.crew_output import CrewOutput
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.process import Process
 from crewai.task import Task
+from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools import AgentTools
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
+from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
+from crewai.utilities.formatter import (
+    aggregate_raw_outputs_from_task_outputs,
+    aggregate_raw_outputs_from_tasks,
+)
 from crewai.utilities.training_handler import CrewTrainingHandler
 
+try:
+    import agentops
+except ImportError:
+    agentops = None
+
 
 class Crew(BaseModel):
     """
@@ -51,7 +64,6 @@ class Crew(BaseModel):
         max_rpm: Maximum number of requests per minute for the crew execution to be respected.
         prompt_file: Path to the prompt json file to be used for the crew.
         id: A unique identifier for the crew instance.
-        full_output: Whether the crew should return the full output with all tasks outputs and token usage metrics or just the final output.
         task_callback: Callback to be executed after each task for every agents execution.
         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.
@@ -87,10 +99,6 @@ class Crew(BaseModel):
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
-    full_output: Optional[bool] = Field(
-        default=False,
-        description="Whether the crew should return the full output with all tasks outputs and token usage metrics or just the final output.",
-    )
     manager_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
@@ -219,6 +227,90 @@ class Crew(BaseModel):
                     agent.set_rpm_controller(self._rpm_controller)
         return self
 
+    @model_validator(mode="after")
+    def validate_tasks(self):
+        if self.process == Process.sequential:
+            for task in self.tasks:
+                if task.agent is None:
+                    raise PydanticCustomError(
+                        "missing_agent_in_task",
+                        f"Sequential process error: Agent is missing in the task with the following description: {task.description}",  # type: ignore # Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
+                        {},
+                    )
+
+        return self
+
+    @model_validator(mode="after")
+    def check_tasks_in_hierarchical_process_not_async(self):
+        """Validates that the tasks in hierarchical process are not flagged with async_execution."""
+        if self.process == Process.hierarchical:
+            for task in self.tasks:
+                if task.async_execution:
+                    raise PydanticCustomError(
+                        "async_execution_in_hierarchical_process",
+                        "Hierarchical process error: Tasks cannot be flagged with async_execution.",
+                        {},
+                    )
+
+        return self
+
+    @model_validator(mode="after")
+    def validate_end_with_at_most_one_async_task(self):
+        """Validates that the crew ends with at most one asynchronous task."""
+        final_async_task_count = 0
+
+        # Traverse tasks backward
+        for task in reversed(self.tasks):
+            if task.async_execution:
+                final_async_task_count += 1
+            else:
+                break  # Stop traversing as soon as a non-async task is encountered
+
+        if final_async_task_count > 1:
+            raise PydanticCustomError(
+                "async_task_count",
+                "The crew must end with at most one asynchronous task.",
+                {},
+            )
+
+        return self
+
+    @model_validator(mode="after")
+    def validate_async_task_cannot_include_sequential_async_tasks_in_context(self):
+        """
+        Validates that if a task is set to be executed asynchronously,
+        it cannot include other asynchronous tasks in its context unless
+        separated by a synchronous task.
+        """
+        for i, task in enumerate(self.tasks):
+            if task.async_execution and task.context:
+                for context_task in task.context:
+                    if context_task.async_execution:
+                        for j in range(i - 1, -1, -1):
+                            if self.tasks[j] == context_task:
+                                raise ValueError(
+                                    f"Task '{task.description}' is asynchronous and cannot include other sequential asynchronous tasks in its context."
+                                )
+                            if not self.tasks[j].async_execution:
+                                break
+        return self
+
+    @model_validator(mode="after")
+    def validate_context_no_future_tasks(self):
+        """Validates that a task's context does not include future tasks."""
+        task_indices = {id(task): i for i, task in enumerate(self.tasks)}
+
+        for task in self.tasks:
+            if task.context:
+                for context_task in task.context:
+                    if id(context_task) not in task_indices:
+                        continue  # Skip context tasks not in the main tasks list
+                    if task_indices[id(context_task)] > task_indices[id(task)]:
+                        raise ValueError(
+                            f"Task '{task.description}' has a context dependency on a future task '{context_task.description}', which is not allowed."
+                        )
+        return self
+
     def _setup_from_config(self):
         assert self.config is not None, "Config should not be None."
 
@@ -257,6 +349,9 @@ class Crew(BaseModel):
         for agent in self.agents:
             agent.allow_delegation = False
 
+        CrewTrainingHandler(TRAINING_DATA_FILE).initialize_file()
+        CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).initialize_file()
+
     def train(self, n_iterations: int, inputs: Optional[Dict[str, Any]] = {}) -> None:
         """Trains the crew for a given number of iterations."""
         self._setup_for_training()
@@ -265,46 +360,42 @@ class Crew(BaseModel):
             self._train_iteration = n_iteration
             self.kickoff(inputs=inputs)
 
-        training_data = CrewTrainingHandler("training_data.pkl").load()
+        training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
 
         for agent in self.agents:
             result = TaskEvaluator(agent).evaluate_training_data(
                 training_data=training_data, agent_id=str(agent.id)
             )
 
-            CrewTrainingHandler("trained_agents_data.pkl").save_trained_data(
+            CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).save_trained_data(
                 agent_id=str(agent.role), trained_data=result.model_dump()
             )
 
     def kickoff(
         self,
-        inputs: Optional[Dict[str, Any]] = {},
-    ) -> Union[str, Dict[str, Any]]:
+        inputs: Optional[Dict[str, Any]] = None,
+    ) -> CrewOutput:
         """Starts the crew to work on its assigned tasks."""
         self._execution_span = self._telemetry.crew_execution_span(self, inputs)
-        # type: ignore # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
-        self._interpolate_inputs(inputs)
+        if inputs is not None:
+            self._interpolate_inputs(inputs)
         self._set_tasks_callbacks()
 
         i18n = I18N(prompt_file=self.prompt_file)
 
         for agent in self.agents:
-            # type: ignore # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
             agent.i18n = i18n
             # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
             agent.crew = self  # type: ignore[attr-defined]
             # TODO: Create an AgentFunctionCalling protocol for future refactoring
-            if (
-                hasattr(agent, "function_calling_llm")
-                and not agent.function_calling_llm
-            ):
-                agent.function_calling_llm = self.function_calling_llm
+            if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
+                agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
 
-            if hasattr(agent, "allow_code_execution") and agent.allow_code_execution:
-                agent.tools += agent.get_code_execution_tools()
+            if agent.allow_code_execution:  # type: ignore # BaseAgent" has no attribute "allow_code_execution"
+                agent.tools += agent.get_code_execution_tools()  # type: ignore # "BaseAgent" has no attribute "get_code_execution_tools"; maybe "get_delegation_tools"?
 
-            if hasattr(agent, "step_callback") and not agent.step_callback:
-                agent.step_callback = self.step_callback
+            if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
+                agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
 
             agent.create_agent_executor()
 
@@ -313,17 +404,12 @@ class Crew(BaseModel):
         if self.process == Process.sequential:
             result = self._run_sequential_process()
         elif self.process == Process.hierarchical:
-            # type: ignore # Unpacking a string is disallowed
-            result, manager_metrics = self._run_hierarchical_process()
-            # type: ignore # Cannot determine type of "manager_metrics"
-            metrics.append(manager_metrics)
+            result = self._run_hierarchical_process()  # type: ignore # Incompatible types in assignment (expression has type "str | dict[str, Any]", variable has type "str")
         else:
             raise NotImplementedError(
                 f"The process '{self.process}' is not implemented yet."
             )
-        metrics = metrics + [
-            agent._token_process.get_summary() for agent in self.agents
-        ]
+        metrics += [agent._token_process.get_summary() for agent in self.agents]
 
         self.usage_metrics = {
             key: sum([m[key] for m in metrics if m is not None]) for key in metrics[0]
@@ -331,52 +417,88 @@ class Crew(BaseModel):
 
         return result
 
-    def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List:
+    def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
         """Executes the Crew's workflow for each input in the list and aggregates results."""
-        results = []
+        results: List[CrewOutput] = []
+
+        # Initialize the parent crew's usage metrics
+        total_usage_metrics = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+        }
 
         for input_data in inputs:
             crew = self.copy()
 
-            for task in crew.tasks:
-                task.interpolate_inputs(input_data)
-            for agent in crew.agents:
-                agent.interpolate_inputs(input_data)
+            output = crew.kickoff(inputs=input_data)
+
+            if crew.usage_metrics:
+                for key in total_usage_metrics:
+                    total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
 
-            output = crew.kickoff()
             results.append(output)
 
+        self.usage_metrics = total_usage_metrics
         return results
 
-    async def kickoff_async(
-        self, inputs: Optional[Dict[str, Any]] = {}
-    ) -> Union[str, Dict]:
+    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = {}) -> CrewOutput:
         """Asynchronous kickoff method to start the crew execution."""
         return await asyncio.to_thread(self.kickoff, inputs)
 
-    async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[Any]:
-        async def run_crew(input_data):
-            crew = self.copy()
+    async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[CrewOutput]:
+        crew_copies = [self.copy() for _ in inputs]
 
-            for task in crew.tasks:
-                task.interpolate_inputs(input_data)
-            for agent in crew.agents:
-                agent.interpolate_inputs(input_data)
+        async def run_crew(crew, input_data):
+            return await crew.kickoff_async(inputs=input_data)
 
-            return await crew.kickoff_async()
-
-        tasks = [asyncio.create_task(run_crew(input_data)) for input_data in inputs]
+        tasks = [
+            asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
+            for i in range(len(inputs))
+        ]
+        tasks = [
+            asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
+            for i in range(len(inputs))
+        ]
 
         results = await asyncio.gather(*tasks)
 
+        total_usage_metrics = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+        }
+        for crew in crew_copies:
+            if crew.usage_metrics:
+                for key in total_usage_metrics:
+                    total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
+
+        self.usage_metrics = total_usage_metrics
+
+        total_usage_metrics = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+        }
+        for crew in crew_copies:
+            if crew.usage_metrics:
+                for key in total_usage_metrics:
+                    total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
+
+        self.usage_metrics = total_usage_metrics
+
         return results
 
-    def _run_sequential_process(self) -> str:
+    def _run_sequential_process(self) -> CrewOutput:
         """Executes tasks sequentially and returns the final output."""
-        task_output = ""
-        token_usage = []
+        task_outputs: List[TaskOutput] = []
+        futures: List[Tuple[Task, Future[TaskOutput]]] = []
+
         for task in self.tasks:
-            if task.agent.allow_delegation:  # type: ignore #  Item "None" of "Agent | None" has no attribute "allow_delegation"
+            if task.agent and task.agent.allow_delegation:
                 agents_for_delegation = [
                     agent for agent in self.agents if agent != task.agent
                 ]
@@ -393,34 +515,85 @@ class Crew(BaseModel):
                 self._file_handler.log(
                     agent=role, task=task.description, status="started"
                 )
-            output = task.execute(context=task_output)
 
-            if not task.async_execution:
-                task_output = output
+            if task.async_execution:
+                context = (
+                    aggregate_raw_outputs_from_tasks(task.context)
+                    if task.context
+                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
+                )
+                future = task.execute_async(
+                    agent=task.agent, context=context, tools=task.tools
+                )
+                futures.append((task, future))
+            else:
+                # Before executing a synchronous task, wait for all async tasks to complete
+                if futures:
+                    # Clear task_outputs before processing async tasks
+                    task_outputs = []
+                    for future_task, future in futures:
+                        task_output = future.result()
+                        task_outputs.append(task_output)
+                        self._process_task_result(future_task, task_output)
 
-            role = task.agent.role if task.agent is not None else "None"
-            self._logger.log("debug", f"== [{role}] Task output: {task_output}\n\n")
-            token_summ = task.agent._token_process.get_summary()
+                    # Clear the futures list after processing all async results
+                    futures.clear()
 
-            token_usage.append(token_summ)
+                context = (
+                    aggregate_raw_outputs_from_tasks(task.context)
+                    if task.context
+                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
+                )
+                task_output = task.execute_sync(
+                    agent=task.agent, context=context, tools=task.tools
+                )
+                task_outputs = [task_output]
+                self._process_task_result(task, task_output)
 
-            if self.output_log_file:
-                self._file_handler.log(agent=role, task=task_output, status="completed")
+        if futures:
+            # Clear task_outputs before processing async tasks
+            task_outputs = []
+            for future_task, future in futures:
+                task_output = future.result()
+                task_outputs.append(task_output)
+                self._process_task_result(future_task, task_output)
 
-        token_usage_formatted = self.aggregate_token_usage(token_usage)
-        self._finish_execution(task_output)
+        # Important: There should only be one task output in the list
+        #            If there are more or 0, something went wrong.
+        if len(task_outputs) != 1:
+            raise ValueError(
+                "Something went wrong. Kickoff should return only one task output."
+            )
 
-        # type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
-        return self._format_output(task_output, token_usage_formatted)
+        final_task_output = task_outputs[0]
 
-    def _run_hierarchical_process(self) -> Union[str, Dict[str, Any]]:
+        final_string_output = final_task_output.raw
+        self._finish_execution(final_string_output)
+
+        token_usage = self.calculate_usage_metrics()
+
+        return CrewOutput(
+            raw=final_task_output.raw,
+            pydantic=final_task_output.pydantic,
+            json_dict=final_task_output.json_dict,
+            tasks_output=[task.output for task in self.tasks if task.output],
+            token_usage=token_usage,
+        )
+
+    def _process_task_result(self, task: Task, output: TaskOutput) -> None:
+        role = task.agent.role if task.agent is not None else "None"
+        self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
+        if self.output_log_file:
+            self._file_handler.log(agent=role, task=output, status="completed")
+
+    # TODO: @joao, Breaking change. Changed return type. Usage metrics is included in crewoutput
+    def _run_hierarchical_process(self) -> CrewOutput:
         """Creates and assigns a manager agent to make sure the crew completes the tasks."""
-
         i18n = I18N(prompt_file=self.prompt_file)
         if self.manager_agent is not None:
             self.manager_agent.allow_delegation = True
             manager = self.manager_agent
-            if len(manager.tools) > 0:
+            if manager.tools is not None and len(manager.tools) > 0:
                 raise Exception("Manager agent should not have tools")
             manager.tools = self.manager_agent.get_delegation_tools(self.agents)
         else:
@@ -432,9 +605,12 @@ class Crew(BaseModel):
                 llm=self.manager_llm,
                 verbose=self.verbose,
             )
+            self.manager_agent = manager
 
-        task_output = ""
-        token_usage = []
+        task_outputs: List[TaskOutput] = []
+        futures: List[Tuple[Task, Future[TaskOutput]]] = []
+
+        # TODO: IF USER OVERRIDE THE CONTEXT, PASS THAT
         for task in self.tasks:
             self._logger.log("debug", f"Working Agent: {manager.role}")
             self._logger.log("info", f"Starting Task: {task.description}")
@@ -444,29 +620,70 @@ class Crew(BaseModel):
                     agent=manager.role, task=task.description, status="started"
                 )
 
-            task_output = task.execute(
-                agent=manager, context=task_output, tools=manager.tools
+            if task.async_execution:
+                context = (
+                    aggregate_raw_outputs_from_tasks(task.context)
+                    if task.context
+                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
+                )
+                future = task.execute_async(
+                    agent=manager, context=context, tools=manager.tools
+                )
+                futures.append((task, future))
+            else:
+                # Before executing a synchronous task, wait for all async tasks to complete
+                if futures:
+                    # Clear task_outputs before processing async tasks
+                    task_outputs = []
+                    for future_task, future in futures:
+                        task_output = future.result()
+                        task_outputs.append(task_output)
+                        self._process_task_result(future_task, task_output)
+
+                    # Clear the futures list after processing all async results
+                    futures.clear()
+
+                context = (
+                    aggregate_raw_outputs_from_tasks(task.context)
+                    if task.context
+                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
+                )
+                task_output = task.execute_sync(
+                    agent=manager, context=context, tools=manager.tools
+                )
+                task_outputs = [task_output]
+                self._process_task_result(task, task_output)
+
+        # Process any remaining async results
+        if futures:
+            # Clear task_outputs before processing async tasks
+            task_outputs = []
+            for future_task, future in futures:
+                task_output = future.result()
+                task_outputs.append(task_output)
+                self._process_task_result(future_task, task_output)
+
+        # Important: There should only be one task output in the list
+        #            If there are more or 0, something went wrong.
+        if len(task_outputs) != 1:
+            raise ValueError(
+                "Something went wrong. Kickoff should return only one task output."
             )
 
-            self._logger.log("debug", f"[{manager.role}] Task output: {task_output}")
-            if hasattr(task, "agent._token_process"):
-                token_summ = task.agent._token_process.get_summary()
-                token_usage.append(token_summ)
-            if self.output_log_file:
-                self._file_handler.log(
-                    agent=manager.role, task=task_output, status="completed"
-                )
+        final_task_output = task_outputs[0]
 
-        self._finish_execution(task_output)
+        final_string_output = final_task_output.raw
+        self._finish_execution(final_string_output)
 
-        # type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
-        manager_token_usage = manager._token_process.get_summary()
-        token_usage.append(manager_token_usage)
-        token_usage_formatted = self.aggregate_token_usage(token_usage)
+        token_usage = self.calculate_usage_metrics()
 
-        return self._format_output(
-            task_output, token_usage_formatted
-        ), manager_token_usage
+        return CrewOutput(
+            raw=final_task_output.raw,
+            pydantic=final_task_output.pydantic,
+            json_dict=final_task_output.json_dict,
+            tasks_output=[task.output for task in self.tasks if task.output],
+            token_usage=token_usage,
+        )
 
     def copy(self):
         """Create a deep copy of the Crew."""
@@ -481,12 +698,13 @@ class Crew(BaseModel):
             "_short_term_memory",
             "_long_term_memory",
             "_entity_memory",
+            "_telemetry",
             "agents",
             "tasks",
         }
 
         cloned_agents = [agent.copy() for agent in self.agents]
-        cloned_tasks = [task.copy() for task in self.tasks]
+        cloned_tasks = [task.copy(cloned_agents) for task in self.tasks]
 
         copied_data = self.model_dump(exclude=exclude)
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
@@ -517,32 +735,37 @@ class Crew(BaseModel):
         for agent in self.agents:
             agent.interpolate_inputs(inputs)
 
-    def _format_output(
-        self, output: str, token_usage: Optional[Dict[str, Any]] = None
-    ) -> Union[str, Dict[str, Any]]:
-        """
-        Formats the output of the crew execution.
-        If full_output is True, then returned data type will be a dictionary else returned outputs are string
-        """
-        if self.full_output:
-            return {  # type: ignore # Incompatible return value type (got "dict[str, Sequence[str | TaskOutput | None]]", expected "str")
-                "final_output": output,
-                "tasks_outputs": [task.output for task in self.tasks if task],
-                "usage_metrics": token_usage,
-            }
-        else:
-            return output
-
-    def _finish_execution(self, output) -> None:
+    def _finish_execution(self, final_string_output: str) -> None:
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
-        self._telemetry.end_crew(self, output)
+        if agentops:
+            agentops.end_session(
+                end_state="Success",
+                end_state_reason="Finished Execution",
+            )
+        self._telemetry.end_crew(self, final_string_output)
+
+    def calculate_usage_metrics(self) -> Dict[str, int]:
+        """Calculates and returns the usage metrics."""
+        total_usage_metrics = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+        }
+
+        for agent in self.agents:
+            if hasattr(agent, "_token_process"):
+                token_sum = agent._token_process.get_summary()
+                for key in total_usage_metrics:
+                    total_usage_metrics[key] += token_sum.get(key, 0)
+
+        if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
+            token_sum = self.manager_agent._token_process.get_summary()
+            for key in total_usage_metrics:
+                total_usage_metrics[key] += token_sum.get(key, 0)
+
+        return total_usage_metrics
 
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
-
-    def aggregate_token_usage(self, token_usage_list: List[Dict[str, Any]]):
-        return {
-            key: sum([m[key] for m in token_usage_list if m is not None])
-            for key in token_usage_list[0]
-        }

src/crewai/crews/__init__.py

@@ -0,0 +1 @@
+from .crew_output import CrewOutput

src/crewai/crews/crew_output.py

@@ -0,0 +1,60 @@
+import json
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, Field
+
+from crewai.tasks.output_format import OutputFormat
+from crewai.tasks.task_output import TaskOutput
+
+
+class CrewOutput(BaseModel):
+    """Class that represents the result of a crew."""
+
+    raw: str = Field(description="Raw output of crew", default="")
+    pydantic: Optional[BaseModel] = Field(
+        description="Pydantic output of Crew", default=None
+    )
+    json_dict: Optional[Dict[str, Any]] = Field(
+        description="JSON dict output of Crew", default=None
+    )
+    tasks_output: list[TaskOutput] = Field(
+        description="Output of each task", default=[]
+    )
+    token_usage: Dict[str, Any] = Field(
+        description="Processed token summary", default={}
+    )
+
+    # TODO: Joao - Adding this safety check breakes when people want to see
+    #                   The full output of a CrewOutput.
+    # @property
+    # def pydantic(self) -> Optional[BaseModel]:
+    #     # Check if the final task output included a pydantic model
+    #     if self.tasks_output[-1].output_format != OutputFormat.PYDANTIC:
+    #         raise ValueError(
+    #             "No pydantic model found in the final task. Please make sure to set the output_pydantic property in the final task in your crew."
+    #         )
+
+    #     return self._pydantic
+
+    @property
+    def json(self) -> Optional[str]:
+        if self.tasks_output[-1].output_format != OutputFormat.JSON:
+            raise ValueError(
+                "No JSON output found in the final task. Please make sure to set the output_json property in the final task in your crew."
+            )
+
+        return json.dumps(self.json_dict)
+
+    def to_dict(self) -> Dict[str, Any]:
+        if self.json_dict:
+            return self.json_dict
+        if self.pydantic:
+            return self.pydantic.model_dump()
+        raise ValueError("No output to convert to dictionary")
+
+    def __str__(self):
+        if self.pydantic:
+            return str(self.pydantic)
+        if self.json_dict:
+            return str(self.json_dict)
+        return self.raw

src/crewai/task.py

@@ -1,9 +1,11 @@
+import json
 import os
 import re
 import threading
 import uuid
-from copy import deepcopy
-from typing import Any, Dict, List, Optional, Type
+from concurrent.futures import Future
+from copy import copy
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 from langchain_openai import ChatOpenAI
 from opentelemetry.trace import Span
@@ -11,9 +13,12 @@ from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
-from crewai.utilities import I18N, ConverterError, Printer
+from crewai.utilities.converter import Converter, ConverterError
+from crewai.utilities.i18n import I18N
+from crewai.utilities.printer import Printer
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
 
@@ -95,6 +100,10 @@ class Task(BaseModel):
         description="Whether the task should have a human review the final answer of the agent",
         default=False,
     )
+    converter_cls: Optional[Type[Converter]] = Field(
+        description="A converter class used to export structured output",
+        default=None,
+    )
 
     _telemetry: Telemetry
     _execution_span: Span | None = None
@@ -155,82 +164,74 @@ class Task(BaseModel):
             )
         return self
 
-    def wait_for_completion(self) -> str | BaseModel:
-        """Wait for asynchronous task completion and return the output."""
-        assert self.async_execution, "Task is not set to be executed asynchronously."
+    def execute_sync(
+        self,
+        agent: Optional[BaseAgent] = None,
+        context: Optional[str] = None,
+        tools: Optional[List[Any]] = None,
+    ) -> TaskOutput:
+        """Execute the task synchronously."""
+        return self._execute_core(agent, context, tools)
 
-        if self._thread:
-            self._thread.join()
-            self._thread = None
-
-        assert self.output, "Task output is not set."
-
-        return self.output.exported_output
-
-    def execute(  # type: ignore # Missing return statement
+    def execute_async(
         self,
         agent: BaseAgent | None = None,
         context: Optional[str] = None,
         tools: Optional[List[Any]] = None,
-    ) -> str:
-        """Execute the task.
+    ) -> Future[TaskOutput]:
+        """Execute the task asynchronously."""
+        future: Future[TaskOutput] = Future()
+        threading.Thread(
+            target=self._execute_task_async, args=(agent, context, tools, future)
+        ).start()
+        return future
 
-        Returns:
-            Output of the task.
-        """
-
-        self._execution_span = self._telemetry.task_started(self)
+    def _execute_task_async(
+        self,
+        agent: Optional[BaseAgent],
+        context: Optional[str],
+        tools: Optional[List[Any]],
+        future: Future[TaskOutput],
+    ) -> None:
+        """Execute the task asynchronously with context handling."""
+        result = self._execute_core(agent, context, tools)
+        future.set_result(result)
 
+    def _execute_core(
+        self,
+        agent: Optional[BaseAgent],
+        context: Optional[str],
+        tools: Optional[List[Any]],
+    ) -> TaskOutput:
+        """Run the core execution logic of the task."""
         agent = agent or self.agent
         if not agent:
             raise Exception(
                 f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
             )
 
-        if self.context:
-            # type: ignore # Incompatible types in assignment (expression has type "list[Never]", variable has type "str | None")
-            context = []
-            for task in self.context:
-                if task.async_execution:
-                    task.wait_for_completion()
-                if task.output:
-                    # type: ignore # Item "str" of "str | None" has no attribute "append"
-                    context.append(task.output.raw_output)
-            # type: ignore # Argument 1 to "join" of "str" has incompatible type "str | None"; expected "Iterable[str]"
-            context = "\n".join(context)
+        self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
 
         self.prompt_context = context
-        tools = tools or self.tools
+        tools = tools or self.tools or []
 
-        if self.async_execution:
-            self._thread = threading.Thread(
-                target=self._execute, args=(agent, self, context, tools)
-            )
-            self._thread.start()
-        else:
-            result = self._execute(
-                task=self,
-                agent=agent,
-                context=context,
-                tools=tools,
-            )
-            return result
-
-    def _execute(self, agent, task, context, tools):
         result = agent.execute_task(
-            task=task,
+            task=self,
             context=context,
             tools=tools,
         )
-        exported_output = self._export_output(result)
 
-        # type: ignore # the responses are usually str but need to figure out a more elegant solution here
-        self.output = TaskOutput(
+        pydantic_output, json_output = self._export_output(result)
+
+        task_output = TaskOutput(
             description=self.description,
-            exported_output=exported_output,
-            raw_output=result,
+            raw=result,
+            pydantic=pydantic_output,
+            json_dict=json_output,
             agent=agent.role,
+            output_format=self._get_output_format(),
         )
+        self.output = task_output
 
         if self.callback:
             self.callback(self.output)
@@ -239,7 +240,15 @@ class Task(BaseModel):
             self._telemetry.task_ended(self._execution_span, self)
             self._execution_span = None
 
-        return exported_output
+        if self.output_file:
+            content = (
+                json_output
+                if json_output
+                else pydantic_output.model_dump_json() if pydantic_output else result
+            )
+            self._save_file(content)
+
+        return task_output
 
     def prompt(self) -> str:
         """Prompt the task.
@@ -274,7 +283,7 @@ class Task(BaseModel):
         """Increment the delegations counter."""
         self.delegations += 1
 
-    def copy(self):
+    def copy(self, agents: List["BaseAgent"]) -> "Task":
         """Create a deep copy of the Task."""
         exclude = {
             "id",
@@ -287,10 +296,14 @@ class Task(BaseModel):
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
 
         cloned_context = (
-            [task.copy() for task in self.context] if self.context else None
+            [task.copy(agents) for task in self.context] if self.context else None
         )
-        cloned_agent = self.agent.copy() if self.agent else None
-        cloned_tools = deepcopy(self.tools) if self.tools else []
+
+        def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
+            return next((agent for agent in agents if agent.role == role), None)
+
+        cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
+        cloned_tools = copy(self.tools) if self.tools else []
 
         copied_task = Task(
             **copied_data,
@@ -298,81 +311,126 @@ class Task(BaseModel):
             agent=cloned_agent,
             tools=cloned_tools,
         )
+
         return copied_task
 
-    def _export_output(self, result: str) -> Any:
-        exported_result = result
-        instructions = "I'm gonna convert this raw text into valid JSON."
+    def _create_converter(self, *args, **kwargs) -> Converter:
+        """Create a converter instance."""
+        converter = self.agent.get_output_converter(*args, **kwargs)
+        if self.converter_cls:
+            converter = self.converter_cls(*args, **kwargs)
+        return converter
+
+    def _export_output(
+        self, result: str
+    ) -> Tuple[Optional[BaseModel], Optional[Dict[str, Any]]]:
+        pydantic_output: Optional[BaseModel] = None
+        json_output: Optional[Dict[str, Any]] = None
 
         if self.output_pydantic or self.output_json:
-            model = self.output_pydantic or self.output_json
+            model_output = self._convert_to_model(result)
+            pydantic_output = (
+                model_output if isinstance(model_output, BaseModel) else None
+            )
+            if isinstance(model_output, str):
+                try:
+                    json_output = json.loads(model_output)
+                except json.JSONDecodeError:
+                    json_output = None
+            else:
+                json_output = model_output if isinstance(model_output, dict) else None
 
-            # try to convert task_output directly to pydantic/json
+        return pydantic_output, json_output
+
+    def _convert_to_model(self, result: str) -> Union[dict, BaseModel, str]:
+        model = self.output_pydantic or self.output_json
+        if model is None:
+            return result
+
+        try:
+            return self._validate_model(result, model)
+        except Exception:
+            return self._handle_partial_json(result, model)
+
+    def _validate_model(
+        self, result: str, model: Type[BaseModel]
+    ) -> Union[dict, BaseModel]:
+        exported_result = model.model_validate_json(result)
+        if self.output_json:
+            return exported_result.model_dump()
+        return exported_result
+
+    def _handle_partial_json(
+        self, result: str, model: Type[BaseModel]
+    ) -> Union[dict, BaseModel, str]:
+        match = re.search(r"({.*})", result, re.DOTALL)
+        if match:
             try:
-                # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
-                exported_result = model.model_validate_json(result)
+                exported_result = model.model_validate_json(match.group(0))
                 if self.output_json:
-                    # type: ignore # "str" has no attribute "model_dump"
                     return exported_result.model_dump()
                 return exported_result
             except Exception:
-                # sometimes the response contains valid JSON in the middle of text
-                match = re.search(r"({.*})", result, re.DOTALL)
-                if match:
-                    try:
-                        # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
-                        exported_result = model.model_validate_json(match.group(0))
-                        if self.output_json:
-                            # type: ignore # "str" has no attribute "model_dump"
-                            return exported_result.model_dump()
-                        return exported_result
-                    except Exception:
-                        pass
+                pass
 
-            # type: ignore # Item "None" of "Agent | None" has no attribute "function_calling_llm"
-            llm = getattr(self.agent, "function_calling_llm", None) or self.agent.llm
-            if not self._is_gpt(llm):
-                # type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
-                model_schema = PydanticSchemaParser(model=model).get_schema()
-                instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
+        return self._convert_with_instructions(result, model)
 
-            converter = self.agent.get_output_converter(
-                llm=llm, text=result, model=model, instructions=instructions
+    def _convert_with_instructions(
+        self, result: str, model: Type[BaseModel]
+    ) -> Union[dict, BaseModel, str]:
+        llm = self.agent.function_calling_llm or self.agent.llm
+        instructions = self._get_conversion_instructions(model, llm)
+
+        converter = self._create_converter(
+            llm=llm, text=result, model=model, instructions=instructions
+        )
+        exported_result = (
+            converter.to_pydantic() if self.output_pydantic else converter.to_json()
+        )
+
+        if isinstance(exported_result, ConverterError):
+            Printer().print(
+                content=f"{exported_result.message} Using raw output instead.",
+                color="red",
             )
-
-            if self.output_pydantic:
-                exported_result = converter.to_pydantic()
-            elif self.output_json:
-                exported_result = converter.to_json()
-
-            if isinstance(exported_result, ConverterError):
-                Printer().print(
-                    content=f"{exported_result.message} Using raw output instead.",
-                    color="red",
-                )
-                exported_result = result
-
-        if self.output_file:
-            content = (
-                # type: ignore # "str" has no attribute "json"
-                exported_result if not self.output_pydantic else exported_result.json()
-            )
-            self._save_file(content)
+            return result
 
         return exported_result
 
+    def _get_output_format(self) -> OutputFormat:
+        if self.output_json:
+            return OutputFormat.JSON
+        if self.output_pydantic:
+            return OutputFormat.PYDANTIC
+        return OutputFormat.RAW
+
+    def _get_conversion_instructions(self, model: Type[BaseModel], llm: Any) -> str:
+        instructions = "I'm gonna convert this raw text into valid JSON."
+        if not self._is_gpt(llm):
+            model_schema = PydanticSchemaParser(model=model).get_schema()
+            instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
+        return instructions
+
+    def _save_output(self, content: str) -> None:
+        if not self.output_file:
+            raise Exception("Output file path is not set.")
+
+        directory = os.path.dirname(self.output_file)
+        if directory and not os.path.exists(directory):
+            os.makedirs(directory)
+        with open(self.output_file, "w", encoding="utf-8") as file:
+            file.write(content)
+
     def _is_gpt(self, llm) -> bool:
         return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
 
     def _save_file(self, result: Any) -> None:
-        # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
-        directory = os.path.dirname(self.output_file)
+        directory = os.path.dirname(self.output_file)  # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
 
         if directory and not os.path.exists(directory):
             os.makedirs(directory)
 
-        # type: ignore # Argument 1 to "open" has incompatible type "str | None"; expected "int | str | bytes | PathLike[str] | PathLike[bytes]"
-        with open(self.output_file, "w", encoding="utf-8") as file:
+        with open(self.output_file, "w", encoding="utf-8") as file:  # type: ignore # Argument 1 to "open" has incompatible type "str | None"; expected "int | str | bytes | PathLike[str] | PathLike[bytes]"
             file.write(result)
         return None
 

src/crewai/tasks/__init__.py

@@ -0,0 +1,4 @@
+from crewai.tasks.output_format import OutputFormat
+from crewai.tasks.task_output import TaskOutput
+
+__all__ = ["OutputFormat", "TaskOutput"]

src/crewai/tasks/output_format.py

@@ -0,0 +1,9 @@
+from enum import Enum
+
+
+class OutputFormat(str, Enum):
+    """Enum that represents the output format of a task."""
+
+    JSON = "json"
+    PYDANTIC = "pydantic"
+    RAW = "raw"

src/crewai/tasks/task_output.py

@@ -1,24 +1,78 @@
-from typing import Optional, Union
+import json
+from typing import Any, Dict, Optional
 
 from pydantic import BaseModel, Field, model_validator
 
+from crewai.tasks.output_format import OutputFormat
+
 
 class TaskOutput(BaseModel):
     """Class that represents the result of a task."""
 
     description: str = Field(description="Description of the task")
     summary: Optional[str] = Field(description="Summary of the task", default=None)
-    exported_output: Union[str, BaseModel] = Field(
-        description="Output of the task", default=None
+    raw: str = Field(
+        description="Raw output of the task", default=""
+    )  # TODO: @joao: breaking change, by renaming raw_output to raw, but now consistent with CrewOutput
+    pydantic: Optional[BaseModel] = Field(
+        description="Pydantic output of task", default=None
+    )
+    json_dict: Optional[Dict[str, Any]] = Field(
+        description="JSON dictionary of task", default=None
     )
     agent: str = Field(description="Agent that executed the task")
-    raw_output: str = Field(description="Result of the task")
+    output_format: OutputFormat = Field(
+        description="Output format of the task", default=OutputFormat.RAW
+    )
 
     @model_validator(mode="after")
     def set_summary(self):
+        """Set the summary field based on the description."""
         excerpt = " ".join(self.description.split(" ")[:10])
         self.summary = f"{excerpt}..."
         return self
 
-    def result(self):
-        return self.exported_output
+    # TODO: Joao - Adding this safety check breakes when people want to see
+    #                   The full output of a TaskOutput or CrewOutput.
+    # @property
+    # def pydantic(self) -> Optional[BaseModel]:
+    #     # Check if the final task output included a pydantic model
+    #     if self.output_format != OutputFormat.PYDANTIC:
+    #         raise ValueError(
+    #             """
+    #             Invalid output format requested.
+    #             If you would like to access the pydantic model,
+    #             please make sure to set the output_pydantic property for the task.
+    #             """
+    #         )
+
+    #     return self._pydantic
+
+    @property
+    def json(self) -> Optional[str]:
+        if self.output_format != OutputFormat.JSON:
+            raise ValueError(
+                """
+                Invalid output format requested.
+                If you would like to access the JSON output,
+                please make sure to set the output_json property for the task
+                """
+            )
+
+        return json.dumps(self.json_dict)
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert json_output and pydantic_output to a dictionary."""
+        output_dict = {}
+        if self.json_dict:
+            output_dict.update(self.json_dict)
+        if self.pydantic:
+            output_dict.update(self.pydantic.model_dump())
+        return output_dict
+
+    def __str__(self) -> str:
+        if self.pydantic:
+            return str(self.pydantic)
+        if self.json_dict:
+            return str(self.json_dict)
+        return self.raw

src/crewai/telemetry/telemetry.py

@@ -156,18 +156,35 @@ class Telemetry:
             except Exception:
                 pass
 
-    def task_started(self, task: Task) -> Span | None:
+    def task_started(self, crew: Crew, task: Task) -> Span | None:
         """Records task started in a crew."""
         if self.ready:
             try:
                 tracer = trace.get_tracer("crewai.telemetry")
                 span = tracer.start_span("Task Execution")
 
+                created_span = tracer.start_span("Task Created")
+
+                self._add_attribute(created_span, "task_id", str(task.id))
+
+                if crew.share_crew:
+                    self._add_attribute(
+                        created_span, "formatted_description", task.description
+                    )
+                    self._add_attribute(
+                        created_span, "formatted_expected_output", task.expected_output
+                    )
+
+                created_span.set_status(Status(StatusCode.OK))
+                created_span.end()
+
                 self._add_attribute(span, "task_id", str(task.id))
-                self._add_attribute(span, "formatted_description", task.description)
-                self._add_attribute(
-                    span, "formatted_expected_output", task.expected_output
-                )
+
+                if crew.share_crew:
+                    self._add_attribute(span, "formatted_description", task.description)
+                    self._add_attribute(
+                        span, "formatted_expected_output", task.expected_output
+                    )
 
                 return span
             except Exception:
@@ -320,7 +337,7 @@ class Telemetry:
             except Exception:
                 pass
 
-    def end_crew(self, crew, output):
+    def end_crew(self, crew, final_string_output):
         if (self.ready) and (crew.share_crew):
             try:
                 self._add_attribute(
@@ -328,7 +345,9 @@ class Telemetry:
                     "crewai_version",
                     pkg_resources.get_distribution("crewai").version,
                 )
-                self._add_attribute(crew._execution_span, "crew_output", output)
+                self._add_attribute(
+                    crew._execution_span, "crew_output", final_string_output
+                )
                 self._add_attribute(
                     crew._execution_span,
                     "crew_tasks_output",

src/crewai/tools/tool_calling.py

@@ -8,7 +8,7 @@ from pydantic.v1 import BaseModel, Field
 class ToolCalling(BaseModel):
     tool_name: str = Field(..., description="The name of the tool to be called.")
     arguments: Optional[Dict[str, Any]] = Field(
-        ..., description="A dictinary of arguments to be passed to the tool."
+        ..., description="A dictionary of arguments to be passed to the tool."
     )
 
 
@@ -17,5 +17,5 @@ class InstructorToolCalling(PydanticBaseModel):
         ..., description="The name of the tool to be called."
     )
     arguments: Optional[Dict[str, Any]] = PydanticField(
-        ..., description="A dictinary of arguments to be passed to the tool."
+        ..., description="A dictionary of arguments to be passed to the tool."
     )

src/crewai/tools/tool_usage.py

@@ -11,6 +11,11 @@ from crewai.telemetry import Telemetry
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.utilities import I18N, Converter, ConverterError, Printer
 
+try:
+    import agentops
+except ImportError:
+    agentops = None
+
 OPENAI_BIGGER_MODELS = ["gpt-4"]
 
 
@@ -45,6 +50,7 @@ class ToolUsage:
         tools_names: str,
         task: Any,
         function_calling_llm: Any,
+        agent: Any,
         action: Any,
     ) -> None:
         self._i18n: I18N = I18N()
@@ -53,6 +59,7 @@ class ToolUsage:
         self._run_attempts: int = 1
         self._max_parsing_attempts: int = 3
         self._remember_format_after_usages: int = 3
+        self.agent = agent
         self.tools_description = tools_description
         self.tools_names = tools_names
         self.tools_handler = tools_handler
@@ -98,7 +105,8 @@ class ToolUsage:
         tool_string: str,
         tool: BaseTool,
         calling: Union[ToolCalling, InstructorToolCalling],
-    ) -> str:  # TODO: Fix this return type --> finecwg : I updated return type to str
+    ) -> str:  # TODO: Fix this return type
+        tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None
         if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
@@ -111,7 +119,7 @@ class ToolUsage:
                     attempts=self._run_attempts,
                 )
                 result = self._format_result(result=result)  # type: ignore #  "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
-                return result  # type: ignore # Fix the reutrn type of this function
+                return result  # type: ignore # Fix the return type of this function
 
             except Exception:
                 self.task.increment_tools_errors()
@@ -123,7 +131,11 @@ class ToolUsage:
                 tool=calling.tool_name, input=calling.arguments
             )
 
-        if result is None: #! finecwg: if not result --> if result is None
+        original_tool = next(
+            (ot for ot in self.original_tools if ot.name == tool.name), None
+        )
+
+        if result is None:  #! finecwg: if not result --> if result is None
             try:
                 if calling.tool_name in [
                     "Delegate work to coworker",
@@ -139,16 +151,12 @@ class ToolUsage:
                             for k, v in calling.arguments.items()
                             if k in acceptable_args
                         }
-                        result = tool._run(**arguments)
+                        result = tool.invoke(input=arguments)
                     except Exception:
-                        if tool.args_schema:
-                            arguments = calling.arguments
-                            result = tool._run(**arguments)
-                        else:
-                            arguments = calling.arguments.values()  # type: ignore # Incompatible types in assignment (expression has type "dict_values[str, Any]", variable has type "dict[str, Any]")
-                            result = tool._run(*arguments)
+                        arguments = calling.arguments
+                        result = tool.invoke(input=arguments)
                 else:
-                    result = tool._run()
+                    result = tool.invoke(input={})
             except Exception as e:
                 self._run_attempts += 1
                 if self._run_attempts > self._max_parsing_attempts:
@@ -164,13 +172,14 @@ class ToolUsage:
                     return error  # type: ignore # No return value expected
 
                 self.task.increment_tools_errors()
+                if agentops:
+                    agentops.record(
+                        agentops.ErrorEvent(exception=e, trigger_event=tool_event)
+                    )
                 return self.use(calling=calling, tool_string=tool_string)  # type: ignore # No return value expected
 
             if self.tools_handler:
                 should_cache = True
-                original_tool = next(
-                    (ot for ot in self.original_tools if ot.name == tool.name), None
-                )
                 if (
                     hasattr(original_tool, "cache_function")
                     and original_tool.cache_function  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
@@ -184,12 +193,29 @@ class ToolUsage:
                 )
 
         self._printer.print(content=f"\n\n{result}\n", color="purple")
+        if agentops:
+            agentops.record(tool_event)
         self._telemetry.tool_usage(
             llm=self.function_calling_llm,
             tool_name=tool.name,
             attempts=self._run_attempts,
         )
         result = self._format_result(result=result)  # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
+        data = {
+            "result": result,
+            "tool_name": tool.name,
+            "tool_args": calling.arguments,
+        }
+
+        if (
+            hasattr(original_tool, "result_as_answer")
+            and original_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+        ):
+            result_as_answer = original_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
+            data["result_as_answer"] = result_as_answer
+
+        self.agent.tools_results.append(data)
+
         return result  # type: ignore # No return value expected
 
     def _format_result(self, result: Any) -> None:
@@ -290,7 +316,7 @@ class ToolUsage:
               Example:
               {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
                     ),
-                    max_attemps=1,
+                    max_attempts=1,
                 )
                 calling = converter.to_pydantic()
 

src/crewai/translations/en.json

@@ -16,8 +16,8 @@
     "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/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task\nYour 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} \n you MUST return the actual complete content as the final answer, not a summary.",
-    "human_feedback": "You got human feedback on your work, re-avaluate it and give a new Final Answer when ready.\n {human_feedback}",
-    "getting_input": "This is the agent final answer: {final_answer}\nPlease provide a feedback: "
+    "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
+    "getting_input": "This is the agent's final answer: {final_answer}\nPlease provide feedback: "
   },
   "errors": {
     "force_final_answer": "Tool won't be use because it's time to give your final answer. Don't use tools and just your absolute BEST Final answer.",

src/crewai/utilities/converter.py

@@ -32,7 +32,7 @@ class Converter(OutputConverter):
             else:
                 return self._create_chain().invoke({})
         except Exception as e:
-            if current_attempt < self.max_attemps:
+            if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
             return ConverterError(
                 f"Failed to convert text into a pydantic model due to the following error: {e}"
@@ -46,7 +46,7 @@ class Converter(OutputConverter):
             else:
                 return json.dumps(self._create_chain().invoke({}).model_dump())
         except Exception:
-            if current_attempt < self.max_attemps:
+            if current_attempt < self.max_attempts:
                 return self.to_json(current_attempt + 1)
             return ConverterError("Failed to convert text into JSON.")
 
@@ -56,7 +56,7 @@ class Converter(OutputConverter):
 
         inst = Instructor(
             llm=self.llm,
-            max_attemps=self.max_attemps,
+            max_attempts=self.max_attempts,
             model=self.model,
             content=self.text,
             instructions=self.instructions,

src/crewai/utilities/evaluators/task_evaluator.py

@@ -6,6 +6,17 @@ from pydantic import BaseModel, Field
 from crewai.utilities import Converter
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
+agentops = None
+try:
+    from agentops import track_agent
+except ImportError:
+
+    def track_agent(name):
+        def noop(f):
+            return f
+
+        return noop
+
 
 class Entity(BaseModel):
     name: str = Field(description="The name of the entity.")
@@ -38,6 +49,7 @@ class TrainingTaskEvaluation(BaseModel):
     )
 
 
+@track_agent(name="Task Evaluator")
 class TaskEvaluator:
     def __init__(self, original_agent):
         self.llm = original_agent.llm

src/crewai/utilities/file_handler.py

@@ -31,9 +31,8 @@ class PickleHandler:
         - file_name (str): The name of the file for saving and loading data.
         """
         self.file_path = os.path.join(os.getcwd(), file_name)
-        self._initialize_file()
 
-    def _initialize_file(self) -> None:
+    def initialize_file(self) -> None:
         """
         Initialize the file with an empty dictionary if it does not exist or is empty.
         """

src/crewai/utilities/formatter.py

@@ -0,0 +1,20 @@
+from typing import List
+
+from crewai.task import Task
+from crewai.tasks.task_output import TaskOutput
+
+
+def aggregate_raw_outputs_from_task_outputs(task_outputs: List[TaskOutput]) -> str:
+    """Generate string context from the task outputs."""
+    dividers = "\n\n----------\n\n"
+
+    # Join task outputs with dividers
+    context = dividers.join(output.raw for output in task_outputs)
+    return context
+
+
+def aggregate_raw_outputs_from_tasks(tasks: List[Task]) -> str:
+    """Generate string context from the tasks."""
+    task_outputs = [task.output for task in tasks if task.output is not None]
+
+    return aggregate_raw_outputs_from_task_outputs(task_outputs)

src/crewai/utilities/token_counter_callback.py

@@ -8,18 +8,18 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 
 
 class TokenCalcHandler(BaseCallbackHandler):
-    model: str = ""
+    model_name: str = ""
     token_cost_process: TokenProcess
 
-    def __init__(self, model, token_cost_process):
-        self.model = model
+    def __init__(self, model_name, token_cost_process):
+        self.model_name = model_name
         self.token_cost_process = token_cost_process
 
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
         try:
-            encoding = tiktoken.encoding_for_model(self.model)
+            encoding = tiktoken.encoding_for_model(self.model_name)
         except KeyError:
             encoding = tiktoken.get_encoding("cl100k_base")
 

tests/agent_test.py

@@ -12,7 +12,6 @@ from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.executor import CrewAgentExecutor
 from crewai.agents.parser import CrewAgentParser
-
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities import RPMController
@@ -632,8 +631,9 @@ def test_agent_use_specific_tasks_output_as_context(capsys):
 
     crew = Crew(agents=[agent1, agent2], tasks=tasks)
     result = crew.kickoff()
-    assert "bye" not in result.lower()
-    assert "hi" in result.lower() or "hello" in result.lower()
+    print("LOWER RESULT", result.raw)
+    assert "bye" not in result.raw.lower()
+    assert "hi" in result.raw.lower() or "hello" in result.raw.lower()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -645,7 +645,7 @@ def test_agent_step_callback():
     with patch.object(StepCallback, "callback") as callback:
 
         @tool
-        def learn_about_AI(topic) -> float:
+        def learn_about_AI(topic) -> str:
             """Useful for when you need to learn about AI to write an paragraph about it."""
             return "AI is a very broad field."
 
@@ -679,7 +679,7 @@ def test_agent_function_calling_llm():
     with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
 
         @tool
-        def learn_about_AI(topic) -> float:
+        def learn_about_AI(topic) -> str:
             """Useful for when you need to learn about AI to write an paragraph about it."""
             return "AI is a very broad field."
 
@@ -724,6 +724,74 @@ def test_agent_count_formatting_error():
         mock_count_errors.assert_called_once()
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
+    from crewai_tools import BaseTool
+
+    class MyCustomTool(BaseTool):
+        name: str = "Get Greetings"
+        description: str = "Get a random greeting back"
+
+        def _run(self) -> str:
+            return "Howdy!"
+
+    agent1 = Agent(
+        role="Data Scientist",
+        goal="Product amazing resports on AI",
+        backstory="You work with data and AI",
+        tools=[MyCustomTool(result_as_answer=True)],
+    )
+
+    essay = Task(
+        description="Write and then review an small paragraph on AI until it's AMAZING. But first use the `Get Greetings` tool to get a greeting.",
+        expected_output="The final paragraph with the full review on AI and no greeting.",
+        agent=agent1,
+    )
+    tasks = [essay]
+    crew = Crew(agents=[agent1], tasks=tasks)
+
+    result = crew.kickoff()
+    print("RESULT: ", result.raw)
+    assert result.raw == "Howdy!"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_tool_usage_information_is_appended_to_agent():
+    from crewai_tools import BaseTool
+
+    class MyCustomTool(BaseTool):
+        name: str = "Decide Greetings"
+        description: str = "Decide what is the appropriate greeting to use"
+
+        def _run(self) -> str:
+            return "Howdy!"
+
+    agent1 = Agent(
+        role="Friendly Neighbor",
+        goal="Make everyone feel welcome",
+        backstory="You are the friendly neighbor",
+        tools=[MyCustomTool(result_as_answer=True)],
+    )
+
+    greeting = Task(
+        description="Say an appropriate greeting.",
+        expected_output="The greeting.",
+        agent=agent1,
+    )
+    tasks = [greeting]
+    crew = Crew(agents=[agent1], tasks=tasks)
+
+    crew.kickoff()
+    assert agent1.tools_results == [
+        {
+            "result": "Howdy!",
+            "tool_name": "Decide Greetings",
+            "tool_args": {},
+            "result_as_answer": True,
+        }
+    ]
+
+
 def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
     agent1 = Agent(
         role="test role",
@@ -734,7 +802,7 @@ def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
 
     assert len(agent1.llm.callbacks) == 1
     assert agent1.llm.callbacks[0].__class__.__name__ == "TokenCalcHandler"
-    assert agent1.llm.callbacks[0].model == "gpt-4o"
+    assert agent1.llm.callbacks[0].model_name == "gpt-4o"
     assert (
         agent1.llm.callbacks[0].token_cost_process.__class__.__name__ == "TokenProcess"
     )

tests/cassettes/test_crew_async_kickoff.yaml

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+        xnbMXv/P5X+tL/rPf/0XAAAA//8DAPM57x7eIAAA
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 89de84040d6cdab1-MIA
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Thu, 04 Jul 2024 10:43:40 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      access-control-allow-origin:
+      - '*'
+      alt-svc:
+      - h3=":443"; ma=86400
+      openai-model:
+      - text-embedding-ada-002
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '15'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains
       x-ratelimit-limit-requests:
       - '10000'
       x-ratelimit-limit-tokens:
@@ -398,7 +393,7 @@ interactions:
       x-ratelimit-reset-tokens:
       - 0s
       x-request-id:
-      - req_834c84237ace9a79492cb9e8d1f68737
+      - req_c684605ab95c8ee822c1b082fc95d416
     status:
       code: 200
       message: OK

tests/task_test.py

@@ -9,6 +9,7 @@ from pydantic_core import ValidationError
 
 from crewai import Agent, Crew, Process, Task
 from crewai.tasks.task_output import TaskOutput
+from crewai.utilities.converter import Converter
 
 
 def test_task_tool_reflect_agent_tools():
@@ -80,7 +81,7 @@ def test_task_prompt_includes_expected_output():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute()
+        task.execute_sync()
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -103,11 +104,13 @@ def test_task_callback():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute()
+        task.execute_sync()
         task_completed.assert_called_once_with(task.output)
 
 
 def test_task_callback_returns_task_ouput():
+    from crewai.tasks.output_format import OutputFormat
+
     researcher = Agent(
         role="Researcher",
         goal="Make the best research and analysis on content about AI and AI agents",
@@ -126,7 +129,7 @@ def test_task_callback_returns_task_ouput():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "exported_ok"
-        task.execute()
+        task.execute_sync()
         # Ensure the callback is called with a TaskOutput object serialized to JSON
         task_completed.assert_called_once()
         callback_data = task_completed.call_args[0][0]
@@ -139,10 +142,12 @@ def test_task_callback_returns_task_ouput():
         output_dict = json.loads(callback_data)
         expected_output = {
             "description": task.description,
-            "exported_output": "exported_ok",
-            "raw_output": "exported_ok",
+            "raw": "exported_ok",
+            "pydantic": None,
+            "json_dict": None,
             "agent": researcher.role,
             "summary": "Give me a list of 5 interesting ideas to explore...",
+            "output_format": OutputFormat.RAW,
         }
         assert output_dict == expected_output
 
@@ -161,7 +166,7 @@ def test_execute_with_agent():
     )
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
-        task.execute(agent=researcher)
+        task.execute_sync(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -181,7 +186,7 @@ def test_async_execution():
     )
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
-        task.execute(agent=researcher)
+        task.execute_async(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -199,7 +204,7 @@ def test_multiple_output_type_error():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_pydantic():
+def test_output_pydantic_sequential():
     class ScoreOutput(BaseModel):
         score: int
 
@@ -217,13 +222,46 @@ def test_output_pydantic():
         agent=scorer,
     )
 
-    crew = Crew(agents=[scorer], tasks=[task])
+    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
     result = crew.kickoff()
-    assert isinstance(result, ScoreOutput)
+    assert isinstance(result.pydantic, ScoreOutput)
+    assert result.to_dict() == {"score": 4}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json():
+def test_output_pydantic_hierarchical():
+    from langchain_openai import ChatOpenAI
+
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_pydantic=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(
+        agents=[scorer],
+        tasks=[task],
+        process=Process.hierarchical,
+        manager_llm=ChatOpenAI(model="gpt-4o"),
+    )
+    result = crew.kickoff()
+    assert isinstance(result.pydantic, ScoreOutput)
+    assert result.to_dict() == {"score": 4}
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_output_json_sequential():
     class ScoreOutput(BaseModel):
         score: int
 
@@ -241,9 +279,126 @@ def test_output_json():
         agent=scorer,
     )
 
-    crew = Crew(agents=[scorer], tasks=[task])
+    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
     result = crew.kickoff()
-    assert '{\n  "score": 4\n}' == result
+    assert '{"score": 4}' == result.json
+    assert result.to_dict() == {"score": 4}
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_output_json_hierarchical():
+    from langchain_openai import ChatOpenAI
+
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_json=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(
+        agents=[scorer],
+        tasks=[task],
+        process=Process.hierarchical,
+        manager_llm=ChatOpenAI(model="gpt-4o"),
+    )
+    result = crew.kickoff()
+    assert '{"score": 4}' == result.json
+    assert result.to_dict() == {"score": 4}
+
+
+def test_json_property_without_output_json():
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_pydantic=ScoreOutput,  # Using output_pydantic instead of output_json
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
+    result = crew.kickoff()
+
+    with pytest.raises(ValueError) as excinfo:
+        _ = result.json  # Attempt to access the json property
+
+    assert "No JSON output found in the final task." in str(excinfo.value)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_output_json_dict_sequential():
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_json=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
+    result = crew.kickoff()
+    assert {"score": 4} == result.json_dict
+    assert result.to_dict() == {"score": 4}
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_output_json_dict_hierarchical():
+    from langchain_openai import ChatOpenAI
+
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_json=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(
+        agents=[scorer],
+        tasks=[task],
+        process=Process.hierarchical,
+        manager_llm=ChatOpenAI(model="gpt-4o"),
+    )
+    result = crew.kickoff()
+    assert {"score": 4} == result.json_dict
+    assert result.to_dict() == {"score": 4}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -279,7 +434,11 @@ def test_output_pydantic_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2], verbose=2)
     result = crew.kickoff()
-    assert 5 == result.score
+    pydantic_result = result.pydantic
+    assert isinstance(
+        pydantic_result, ScoreOutput
+    ), "Expected pydantic result to be of type ScoreOutput"
+    assert 5 == pydantic_result.score
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -310,7 +469,7 @@ def test_output_json_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2])
     result = crew.kickoff()
-    assert '{\n  "score": 3\n}' == result
+    assert '{"score": 5}' == result.json
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -362,7 +521,9 @@ def test_save_task_json_output():
     with patch.object(Task, "_save_file") as save_file:
         save_file.return_value = None
         crew.kickoff()
-        save_file.assert_called_once_with('{\n  "score": 4\n}')
+        save_file.assert_called_once_with(
+            {"score": 4}
+        )  # TODO: @Joao, should this be a dict or a json string?
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -393,6 +554,38 @@ def test_save_task_pydantic_output():
         save_file.assert_called_once_with('{"score":4}')
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_custom_converter_cls():
+    class ScoreOutput(BaseModel):
+        score: int
+
+    class ScoreConverter(Converter):
+        pass
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_pydantic=ScoreOutput,
+        converter_cls=ScoreConverter,
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task])
+
+    with patch.object(
+        ScoreConverter, "to_pydantic", return_value=ScoreOutput(score=5)
+    ) as mock_to_pydantic:
+        crew.kickoff()
+        mock_to_pydantic.assert_called_once()
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_increment_delegations_for_hierarchical_process():
     from langchain_openai import ChatOpenAI
@@ -413,31 +606,29 @@ def test_increment_delegations_for_hierarchical_process():
         agents=[scorer],
         tasks=[task],
         process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4-0125-preview"),
+        manager_llm=ChatOpenAI(model="gpt-4o"),
     )
 
     with patch.object(Task, "increment_delegations") as increment_delegations:
         increment_delegations.return_value = None
         crew.kickoff()
-        increment_delegations.assert_called_once
+        increment_delegations.assert_called_once()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_increment_delegations_for_sequential_process():
-    pass
-
     manager = Agent(
         role="Manager",
         goal="Coordinate scoring processes",
         backstory="You're great at delegating work about scoring.",
-        allow_delegation=False,
+        allow_delegation=True,
     )
 
     scorer = Agent(
         role="Scorer",
         goal="Score the title",
         backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
+        allow_delegation=True,
     )
 
     task = Task(
@@ -455,7 +646,7 @@ def test_increment_delegations_for_sequential_process():
     with patch.object(Task, "increment_delegations") as increment_delegations:
         increment_delegations.return_value = None
         crew.kickoff()
-        increment_delegations.assert_called_once
+        increment_delegations.assert_called_once()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -490,7 +681,7 @@ def test_increment_tool_errors():
     with patch.object(Task, "increment_tools_errors") as increment_tools_errors:
         increment_tools_errors.return_value = None
         crew.kickoff()
-        increment_tools_errors.assert_called_once
+        assert len(increment_tools_errors.mock_calls) == 3
 
 
 def test_task_definition_based_on_dict():
@@ -525,3 +716,80 @@ def test_interpolate_inputs():
         == "Give me a list of 5 interesting ideas about ML to explore for an article, what makes them unique and interesting."
     )
     assert task.expected_output == "Bullet point list of 5 interesting ideas about ML."
+
+
+def test_task_output_str_with_pydantic():
+    from crewai.tasks.output_format import OutputFormat
+
+    class ScoreOutput(BaseModel):
+        score: int
+
+    score_output = ScoreOutput(score=4)
+    task_output = TaskOutput(
+        description="Test task",
+        agent="Test Agent",
+        pydantic=score_output,
+        output_format=OutputFormat.PYDANTIC,
+    )
+
+    assert str(task_output) == str(score_output)
+
+
+def test_task_output_str_with_json_dict():
+    from crewai.tasks.output_format import OutputFormat
+
+    json_dict = {"score": 4}
+    task_output = TaskOutput(
+        description="Test task",
+        agent="Test Agent",
+        json_dict=json_dict,
+        output_format=OutputFormat.JSON,
+    )
+
+    assert str(task_output) == str(json_dict)
+
+
+def test_task_output_str_with_raw():
+    from crewai.tasks.output_format import OutputFormat
+
+    raw_output = "Raw task output"
+    task_output = TaskOutput(
+        description="Test task",
+        agent="Test Agent",
+        raw=raw_output,
+        output_format=OutputFormat.RAW,
+    )
+
+    assert str(task_output) == raw_output
+
+
+def test_task_output_str_with_pydantic_and_json_dict():
+    from crewai.tasks.output_format import OutputFormat
+
+    class ScoreOutput(BaseModel):
+        score: int
+
+    score_output = ScoreOutput(score=4)
+    json_dict = {"score": 4}
+    task_output = TaskOutput(
+        description="Test task",
+        agent="Test Agent",
+        pydantic=score_output,
+        json_dict=json_dict,
+        output_format=OutputFormat.PYDANTIC,
+    )
+
+    # When both pydantic and json_dict are present, pydantic should take precedence
+    assert str(task_output) == str(score_output)
+
+
+def test_task_output_str_with_none():
+    from crewai.tasks.output_format import OutputFormat
+
+    task_output = TaskOutput(
+        description="Test task",
+        agent="Test Agent",
+        output_format=OutputFormat.RAW,
+    )
+
+    assert str(task_output) == ""

tests/utilities/test_file_handler.py

@@ -17,6 +17,10 @@ class TestPickleHandler(unittest.TestCase):
             os.remove(self.file_path)
 
     def test_initialize_file(self):
+        assert os.path.exists(self.file_path) is False
+
+        self.handler.initialize_file()
+
         assert os.path.exists(self.file_path) is True
         assert os.path.getsize(self.file_path) >= 0