preping new verison

Fix tool usage
preparing new version
2025-12-19 22:08:31 +00:00 · 2024-07-12 15:10:31 -04:00 · 2024-07-12 15:06:14 -04:00 · 2024-07-06 12:26:41 -04:00 · 2024-07-06 02:14:52 -03:00 · 2024-07-06 02:01:39 -03:00
53 changed files with 218353 additions and 15893 deletions
--- a/.github/workflows/tests.yml
+++ b/.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
--- a/.gitignore
+++ b/.gitignore
@@ -14,3 +14,4 @@ test.py
 rc-tests/*
 *.pkl
 temp/*
 .vscode/*
--- a/docs/core-concepts/Agents.md
+++ b/docs/core-concepts/Agents.md
@@ -16,24 +16,24 @@ description: What are crewAI Agents and how to use them.
 ## Agent Attributes
-| Attribute                  | Description                                                                                                                                                                                                                                    |
+| Attribute                  | Parameter  | Description                                                                                                                                                                                                                                    |
-| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| :------------------------- | :---- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Role**                   | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
+| **Role**                   | `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.                                                                                                                                        |
+| **Goal**                   | `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.                                                                                                                                           |
+| **Backstory**              | `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.                                                         |
+| **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)*     | 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.             |
+| **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)* | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
+| **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` is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
+| **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` 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 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` 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.                                                                     |
+| **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)*   | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
+| **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)* | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
+| **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)* | 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`.                                                               |
+| **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)*     | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
+| **Cache** *(optional)*     | `cache`  | 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`.                                                                                                                                                                  |
+| **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Prompt Template** *(optional)*     | Specifies the prompt 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)*     | Specifies the response 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
--- a/docs/core-concepts/Crews.md
+++ b/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                                                  |
+| Attribute                   | Parameters          | Description                                                                                              |
-| :-------------------------- | :----------------------------------------------------------- |
+| :-------------------------- | :------------------ | :------------------------------------------------------------------------------------------------------- |
-| **Tasks**                   | A list of tasks assigned to the crew.                        |
+| **Tasks**                   | `tasks`               | A list of tasks assigned to the crew.                                                                    |
-| **Agents**                  | A list of agents that are part of the crew.                  |
+| **Agents**                  | `agents`              | A list of agents that are part of the crew.                                                              |
-| **Process** *(optional)*    | The process flow (e.g., sequential, hierarchical) the crew follows. |
+| **Process** *(optional)*    | `process`             | The process flow (e.g., sequential, hierarchical) the crew follows.                                      |
-| **Verbose** *(optional)*    | The verbosity level for logging during execution.            |
+| **Verbose** *(optional)*    | `verbose`             | 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.** |
+| **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)* | 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. |
+| **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)*     | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
+| **Config** *(optional)*     | `config`              | 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. |
+| **Max RPM** *(optional)*    | `max_rpm`             | Maximum requests per minute the crew adheres to during execution.                                         |
-| **Language**  *(optional)*  | Language used for the crew, defaults to English.             |
+| **Language**  *(optional)*  | `language`            | Language used for the crew, defaults to English.                                                          |
-| **Language File** *(optional)* | Path to the language file to be used for the crew.          |
+| **Language File** *(optional)* | `language_file`       | Path to the language file to be used for the crew.                                                        |
-| **Memory** *(optional)*     | Utilized for storing execution memories (short-term, long-term, entity memory). |
+| **Memory** *(optional)*     | `memory`              | 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. |
+| **Cache** *(optional)*      | `cache`               | 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.       |
+| **Embedder** *(optional)*   | `embedder`            | 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. |
+| **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)* | 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`. |
+| **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)* | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
+| **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)* | 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. |
+| **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)* | 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. |
+| **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` sets a custom agent that will be used as a manager. |
+| **Manager Agent** *(optional)* | `manager_agent`      | `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. |
+| **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)* | Path to the prompt JSON file to be used for the crew. |
+| **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()`.
--- a/docs/core-concepts/Memory.md
+++ b/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. |
--- a/docs/core-concepts/Tasks.md
+++ b/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                                                                                   |
+| Attribute              | Parameters           | Description                                                                                   |
-| :----------------------| :-------------------------------------------------------------------------------------------- |
+| :----------------------| :------------------- | :-------------------------------------------------------------------------------------------- |
-| **Description**        | A clear, concise statement of what the task entails.                                          |
+| **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.        |
+| **Agent**              | `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.                              |
+| **Expected Output**    | `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.                      |
+| **Tools** *(optional)* | `tools`                | 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.|
+| **Async Execution** *(optional)* | `async_execution`      | 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.                              |
+| **Context**  *(optional)* | `context`              | 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. |
+| **Config** *(optional)* | `config`               | 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 JSON**  *(optional)* | `output_json`          | 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 Pydantic**  *(optional)* | `output_pydantic`      | 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. |
+| **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)* | A Python callable that is executed with the task's output upon completion.                    |
+| **Callback**  *(optional)* | `callback`             | 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. |
+| **Human Input** *(optional)* | `human_input`         | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
 ## Creating a Task
--- a/docs/core-concepts/Training-Crew.md
+++ b/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.
--- a/docs/how-to/Create-Custom-Tools.md
+++ b/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
--- a/docs/how-to/Creating-a-Crew-and-kick-it-off.md
+++ b/docs/how-to/Creating-a-Crew-and-kick-it-off.md
@@ -80,4 +80,3 @@ 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
--- a/docs/how-to/Sequential.md
+++ b/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, expected_output='Raw Data')
-research_task = Task(description='Gather relevant data...', agent=researcher)
+analysis_task = Task(description='Analyze the data...', agent=analyst, expected_output='Data Insights')
-analysis_task = Task(description='Analyze the data...', agent=analyst)
+writing_task = Task(description='Compose the report...', agent=writer, expected_output='Final Report')
 writing_task = Task(description='Compose the report...', agent=writer)
 # Form the crew with a sequential process
 report_crew = Crew(
--- a/docs/tools/GitHubSearchTool.md
+++ b/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:
--- a/docs/tools/TXTSearchTool.md
+++ b/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
--- a/poetry.lock
+++ b/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 = "boto3-1.34.140-py3-none-any.whl", hash = "sha256:23ca8d8f7a30c3bbd989808056b5fc5d68ff5121c02c722c6167b6b1bb7f8726"},
-    {file = "boto3-1.34.137.tar.gz", hash = "sha256:0b21b84db4619b3711a6f643d465a5a25e81231ee43615c55a20ff6b89c6cc3c"},
+    {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-1.34.140-py3-none-any.whl", hash = "sha256:43940d3a67d946ba3301631ba4078476a75f1015d4fb0fb0272d0b754b2cf9de"},
-    {file = "botocore-1.34.137.tar.gz", hash = "sha256:e29c8e9bfda0b20a1997792968e85868bfce42fefad9730f633a81adcff3f2ef"},
+    {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-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
-    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
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 [[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 = [
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+    {file = "clarifai_grpc-10.6.1-py3-none-any.whl", hash = "sha256:7f07c262f46042995b11af10cdd552718c4487e955db1b3f1253fcb0c2ab1ce1"},
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+    {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 = "0.4.7"
 description = "Set of tools for the crewAI framework"
 optional = false
 python-versions = "<=3.13,>=3.10"
 files = [
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+    {file = "crewai_tools-0.4.7-py3-none-any.whl", hash = "sha256:3ff04b2da07d2c48e72f898511295b4a10038dd3e4fe859baa93fec1fb8baf8e"},
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+    {file = "crewai_tools-0.4.7.tar.gz", hash = "sha256:4502a5e0ab94a7dae6638d000768f80049918909ca5338cdebc280351b3ce003"},
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@@ -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 = "0.1.115"
 description = "Simplest open source retrieval (RAG) framework"
 optional = false
 python-versions = "<=3.13,>=3.9"
 files = [
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+    {file = "embedchain-0.1.115-py3-none-any.whl", hash = "sha256:6f1842edea7780e6b1d21d073b72b2c916f440411c32e758165c88141b2ac1ec"},
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+    {file = "embedchain-0.1.115.tar.gz", hash = "sha256:8b259a3307e022924c99cf82b17789f141d157f11e53beb3bc82bf49efa2e31e"},
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@@ -1048,10 +1068,12 @@ alembic = ">=1.13.1,<2.0.0"
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 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"
 openai = ">=1.1.1"
 posthog = ">=3.0.2,<4.0.0"
@@ -1065,7 +1087,6 @@ tiktoken = ">=0.7.0,<0.8.0"
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 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 +1099,7 @@ 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 +1110,7 @@ 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 +1444,13 @@ 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 = [
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+    {file = "google-cloud-aiplatform-1.58.0.tar.gz", hash = "sha256:7a05aceac4a6c7eaa26e684e9f202b829cc7e57f82bffe7281684275a553fcad"},
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+    {file = "google_cloud_aiplatform-1.58.0-py2.py3-none-any.whl", hash = "sha256:21f1320860f4916183ec939fdf2ff3fc1d7fdde97fe5795974257ab21f9458ec"},
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@@ -1450,7 +1471,7 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
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@@ -1459,12 +1480,12 @@ pipelines = ["pyyaml (>=5.3.1,<7)"]
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-rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"]
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 tokenization = ["sentencepiece (>=0.2.0)"]
 vizier = ["google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
@@ -2357,156 +2378,140 @@ 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"
 langchain-text-splitters = ">=0.0.1,<0.1"
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 PyYAML = ">=5.3"
 requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
-tenacity = ">=8.1.0,<9.0.0"
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+    {file = "pydantic_core-2.20.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f6d6cff3538391e8486a431569b77921adfcdef14eb18fbf19b7c0a5294d4e6a"},
-    {file = "pydantic_core-2.20.0.tar.gz", hash = "sha256:366be8e64e0cb63d87cf79b4e1765c0703dd6313c729b22e7b9e378db6b96877"},
+    {file = "pydantic_core-2.20.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a6d511cc297ff0883bc3708b465ff82d7560193169a8b93260f74ecb0a5e08a7"},
    {file = "pydantic_core-2.20.1.tar.gz", hash = "sha256:26ca695eeee5f9f1aeeb211ffc12f10bcb6f71e2989988fda61dabd65db878d4"},
 ]
 [package.dependencies]
@@ -4163,13 +4202,13 @@ 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-1.1.370-py3-none-any.whl", hash = "sha256:fc721601e480a69989775bfc210534a6ca0110ebd0c065244a8d3a151294fc61"},
-    {file = "pyright-1.1.369.tar.gz", hash = "sha256:ad290710072d021e213b98cc7a2f90ae3a48609ef5b978f749346d1a47eb9af8"},
+    {file = "pyright-1.1.370.tar.gz", hash = "sha256:d0d559d506fc41e3297f721aaa05a1b9f06beda5acc9ac64ca371ce94c28f960"},
 ]
 [package.dependencies]
@@ -4588,13 +4627,13 @@ 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-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
-    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
+    {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
 ]
 [package.dependencies]
@@ -4994,13 +5033,13 @@ 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-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
-    {file = "tenacity-8.4.2.tar.gz", hash = "sha256:cd80a53a79336edba8489e767f729e4f391c896956b57140b5d7511a64bbd3ef"},
+    {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
 ]
 [package.extras]
@@ -5227,13 +5266,13 @@ 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-0.26.0-py3-none-any.whl", hash = "sha256:bb9c1b259591af941fccfbabbdc65bc7ed764bd2db76428454c894cd5e3d2032"},
-    {file = "trio-0.25.1.tar.gz", hash = "sha256:9f5314f014ea3af489e77b001861c535005c3858d38ec46b6b071ebfa339d7fb"},
+    {file = "trio-0.26.0.tar.gz", hash = "sha256:67c5ec3265dd4abc7b1d1ab9ca4fe4c25b896f9c93dac73713778adab487f9c4"},
 ]
 [package.dependencies]
@@ -6028,9 +6067,10 @@ 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 = "4f3e5fddb5f0fc8fd143a8abe947ecac443213d595bd0eeed745ccb82dac2312"
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.35.7"
+version = "0.36.1"
 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 = "^0.4.7", 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 = "^0.4.7"
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"
--- a/src/crewai/agent.py
+++ b/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 = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
            self.tools_handler.last_used_tool = {}
        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:
--- a/src/crewai/agents/agent_builder/base_agent.py
+++ b/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.agent_builder.utilities.base_token_process import TokenProcess
-from crewai.agents import CacheHandler, ToolsHandler
+from crewai.agents.cache.cache_handler import CacheHandler
-from crewai.utilities.token_counter_callback import TokenProcess
+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.
--- a/src/crewai/agents/agent_builder/base_agent_executor_mixin.py
+++ b/src/crewai/agents/agent_builder/base_agent_executor_mixin.py
@@ -1,24 +1,44 @@
 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.memory
+            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,
@@ -26,10 +46,26 @@ class CrewAgentExecutorMixin:
                        "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:
-        if self.crew and self.crew.memory:
+        """Create and save long-term and entity memory items based on evaluation."""
        if (
            self.crew
            and self.crew.memory
            and self.crew._long_term_memory
            and self.crew._entity_memory
            and self.task
            and self.crew_agent
        ):
            try:
                ltm_agent = TaskEvaluator(self.crew_agent)
                evaluation = ltm_agent.evaluate(self.task, output.log)
@@ -54,12 +90,20 @@ class CrewAgentExecutorMixin:
                        name=entity.name,
                        type=entity.type,
                        description=entity.description,
-                    relationships="\n".join([f"- {r}" for r in entity.relationships]),
+                        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)
        )
--- a/src/crewai/agents/agent_builder/utilities/base_agent_tool.py
+++ b/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"
--- a/src/crewai/agents/agent_builder/utilities/base_output_converter_base.py
+++ b/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(
+    max_attempts: Optional[int] = Field(
-        description="Max number of attemps to try to get the output formated.",
+        description="Max number of attempts to try to get the output formated.",
        default=3,
    )
@@ -42,7 +41,7 @@ class OutputConverter(BaseModel, ABC):
        """Convert text to json."""
        pass
-    @abstractmethod
+    @abstractmethod  # type: ignore #  Name "_is_gpt" already defined on line 25
-    def _is_gpt(self, llm):
+    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
--- a/src/crewai/agents/executor.py
+++ b/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)
--- a/src/crewai/cli/templates/pyproject.toml
+++ b/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 = "^0.36.1" }
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"
--- a/src/crewai/crew.py
+++ b/src/crewai/crew.py
@@ -1,7 +1,7 @@
 import asyncio
 import json
 import uuid
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 from langchain_core.callbacks import BaseCallbackHandler
 from pydantic import (
@@ -28,9 +28,15 @@ from crewai.task import Task
 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.training_handler import CrewTrainingHandler
 try:
    import agentops
 except ImportError:
    agentops = None
 class Crew(BaseModel):
    """
@@ -219,6 +225,33 @@ 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
    def _setup_from_config(self):
        assert self.config is not None, "Config should not be None."
@@ -257,6 +290,9 @@ 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()
@@ -265,14 +301,14 @@ 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()
            )
@@ -282,29 +318,25 @@ class Crew(BaseModel):
    ) -> Union[str, Dict[str, Any]]:
        """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)
+        self._interpolate_inputs(inputs)  # type: ignore # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
        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 (
+            if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
-                hasattr(agent, "function_calling_llm")
+                agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
                and not agent.function_calling_llm
            ):
                agent.function_calling_llm = self.function_calling_llm
-            if hasattr(agent, "allow_code_execution") and agent.allow_code_execution:
+            if agent.allow_code_execution:  # type: ignore # BaseAgent" has no attribute "allow_code_execution"
-                agent.tools += agent.get_code_execution_tools()
+                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:
+            if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
-                agent.step_callback = self.step_callback
+                agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
            agent.create_agent_executor()
@@ -313,17 +345,13 @@ 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 # Incompatible types in assignment (expression has type "str | dict[str, Any]", variable has type "str")
            result, manager_metrics = self._run_hierarchical_process()
            # type: ignore # Cannot determine type of "manager_metrics"
            metrics.append(manager_metrics)
        else:
            raise NotImplementedError(
                f"The process '{self.process}' is not implemented yet."
            )
-        metrics = metrics + [
+        metrics += [agent._token_process.get_summary() for agent in self.agents]
            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]
@@ -331,21 +359,32 @@ 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[Union[str, Dict[str, Any]]]:
        """Executes the Crew's workflow for each input in the list and aggregates results."""
        results = []
        # 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:
+            output = crew.kickoff(inputs=input_data)
-                task.interpolate_inputs(input_data)
+
-            for agent in crew.agents:
+            if crew.usage_metrics:
-                agent.interpolate_inputs(input_data)
+                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(
@@ -355,28 +394,39 @@ class Crew(BaseModel):
        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_copies = [self.copy() for _ in inputs]
            crew = self.copy()
-            for task in crew.tasks:
+        async def run_crew(crew, input_data):
-                task.interpolate_inputs(input_data)
+            return await crew.kickoff_async(inputs=input_data)
            for agent in crew.agents:
                agent.interpolate_inputs(input_data)
-            return await crew.kickoff_async()
+        tasks = [
-
+            asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
-        tasks = [asyncio.create_task(run_crew(input_data)) for input_data in inputs]
+            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
        return results
-    def _run_sequential_process(self) -> str:
+    def _run_sequential_process(self) -> Union[str, Dict[str, Any]]:
        """Executes tasks sequentially and returns the final output."""
-        task_output = ""
+        task_output = None
-        token_usage = []
+
        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
                ]
@@ -400,27 +450,26 @@ class Crew(BaseModel):
            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()
            token_usage.append(token_summ)
            if self.output_log_file:
                self._file_handler.log(agent=role, task=task_output, status="completed")
        token_usage_formatted = self.aggregate_token_usage(token_usage)
        self._finish_execution(task_output)
-        # type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
+        token_usage = self.calculate_usage_metrics()
        return self._format_output(task_output, token_usage_formatted)
-    def _run_hierarchical_process(self) -> Union[str, Dict[str, Any]]:
+        return self._format_output(task_output if task_output else "", token_usage)
    def _run_hierarchical_process(
        self,
    ) -> Tuple[Union[str, Dict[str, Any]], Dict[str, Any]]:
        """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 +481,10 @@ class Crew(BaseModel):
                llm=self.manager_llm,
                verbose=self.verbose,
            )
            self.manager_agent = manager
        task_output = None
        task_output = ""
        token_usage = []
        for task in self.tasks:
            self._logger.log("debug", f"Working Agent: {manager.role}")
            self._logger.log("info", f"Starting Task: {task.description}")
@@ -444,14 +494,15 @@ class Crew(BaseModel):
                    agent=manager.role, task=task.description, status="started"
                )
            if task.agent:
                manager.tools = task.agent.get_delegation_tools([task.agent])
            else:
                manager.tools = manager.get_delegation_tools(self.agents)
            task_output = task.execute(
                agent=manager, context=task_output, tools=manager.tools
            )
            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"
@@ -459,14 +510,11 @@ class Crew(BaseModel):
        self._finish_execution(task_output)
-        # type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
+        token_usage = self.calculate_usage_metrics()
        manager_token_usage = manager._token_process.get_summary()
        token_usage.append(manager_token_usage)
        token_usage_formatted = self.aggregate_token_usage(token_usage)
        return self._format_output(
-            task_output, token_usage_formatted
+            task_output if task_output else "", token_usage
-        ), manager_token_usage
+        ), token_usage
    def copy(self):
        """Create a deep copy of the Crew."""
@@ -481,12 +529,13 @@ 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}
@@ -524,8 +573,9 @@ class Crew(BaseModel):
        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")
+            return {
                "final_output": output,
                "tasks_outputs": [task.output for task in self.tasks if task],
                "usage_metrics": token_usage,
@@ -536,13 +586,33 @@ class Crew(BaseModel):
    def _finish_execution(self, output) -> None:
        if self.max_rpm:
            self._rpm_controller.stop_rpm_counter()
        if agentops:
            agentops.end_session(
                end_state="Success", end_state_reason="Finished Execution"
            )
        self._telemetry.end_crew(self, 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]
        }
--- a/src/crewai/task.py
+++ b/src/crewai/task.py
@@ -1,9 +1,9 @@
 import os
 import re
 import threading
 import uuid
-from copy import deepcopy
+from concurrent.futures import Future, ThreadPoolExecutor
-from typing import Any, Dict, List, Optional, Type
+from copy import copy
 from typing import Any, Dict, List, Optional, Type, Union
 from langchain_openai import ChatOpenAI
 from opentelemetry.trace import Span
@@ -13,7 +13,10 @@ from pydantic_core import PydanticCustomError
 from crewai.agents.agent_builder.base_agent import BaseAgent
 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 ConverterError
 from crewai.utilities.converter import Converter
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import Printer
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
@@ -95,12 +98,16 @@ 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
    _original_description: str | None = None
    _original_expected_output: str | None = None
-    _thread: threading.Thread | None = None
+    _future: Future | None = None
    def __init__(__pydantic_self__, **data):
        config = data.pop("config", {})
@@ -159,20 +166,20 @@ class Task(BaseModel):
        """Wait for asynchronous task completion and return the output."""
        assert self.async_execution, "Task is not set to be executed asynchronously."
-        if self._thread:
+        if self._future:
-            self._thread.join()
+            self._future.result()  # Wait for the future to complete
-            self._thread = None
+            self._future = None
        assert self.output, "Task output is not set."
        return self.output.exported_output
-    def execute(  # type: ignore # Missing return statement
+    def execute(
        self,
        agent: BaseAgent | None = None,
        context: Optional[str] = None,
        tools: Optional[List[Any]] = None,
-    ) -> str:
+    ) -> str | None:
        """Execute the task.
        Returns:
@@ -184,29 +191,28 @@ class Task(BaseModel):
        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."
+                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")
+            internal_context = []
            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"
+                    internal_context.append(task.output.raw_output)
-                    context.append(task.output.raw_output)
+            context = "\n".join(internal_context)
            # type: ignore # Argument 1 to "join" of "str" has incompatible type "str | None"; expected "Iterable[str]"
            context = "\n".join(context)
        self.prompt_context = context
        tools = tools or self.tools
        if self.async_execution:
-            self._thread = threading.Thread(
+            with ThreadPoolExecutor() as executor:
-                target=self._execute, args=(agent, self, context, tools)
+                self._future = executor.submit(
                    self._execute, agent, self, context, tools
                )
-            self._thread.start()
+            return None
        else:
            result = self._execute(
                task=self,
@@ -216,7 +222,7 @@ class Task(BaseModel):
            )
            return result
-    def _execute(self, agent, task, context, tools):
+    def _execute(self, agent: "BaseAgent", task, context, tools) -> str | None:
        result = agent.execute_task(
            task=task,
            context=context,
@@ -224,7 +230,6 @@ class Task(BaseModel):
        )
        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(
            description=self.description,
            exported_output=exported_output,
@@ -274,7 +279,7 @@ class Task(BaseModel):
        """Increment the delegations counter."""
        self.delegations += 1
-    def copy(self):
+    def copy(self, agents: Optional[List["BaseAgent"]] = None) -> "Task":  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"
        """Create a deep copy of the Task."""
        exclude = {
            "id",
@@ -289,8 +294,12 @@ class Task(BaseModel):
        cloned_context = (
            [task.copy() 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)  # type: ignore # Item "None" of "list[BaseAgent] | None" has no attribute "__iter__" (not iterable)
        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,
@@ -298,8 +307,19 @@ class Task(BaseModel):
            agent=cloned_agent,
            tools=cloned_tools,
        )
        return copied_task
    def _create_converter(self, *args, **kwargs) -> Converter: # type: ignore
      converter = self.agent.get_output_converter(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
        *args, **kwargs
      )
      if self.converter_cls:
        converter = self.converter_cls(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
          *args, **kwargs
        )          
      return converter
    def _export_output(self, result: str) -> Any:
        exported_result = result
        instructions = "I'm gonna convert this raw text into valid JSON."
@@ -309,34 +329,28 @@ class Task(BaseModel):
            # try to convert task_output directly to pydantic/json
            try:
-                # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
+                exported_result = model.model_validate_json(result)  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
                exported_result = model.model_validate_json(result)
                if self.output_json:
-                    # type: ignore # "str" has no attribute "model_dump"
+                    return exported_result.model_dump()  # 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))  # 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()  # type: ignore # "str" has no attribute "model_dump"
                            return exported_result.model_dump()
                        return exported_result
                    except Exception:
                        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  # type: ignore # Item "None" of "BaseAgent | 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()  # 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}"
-            converter = self.agent.get_output_converter(
+            converter = self._create_converter( # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
                llm=llm, text=result, model=model, instructions=instructions
            )
@@ -354,8 +368,9 @@ class Task(BaseModel):
        if self.output_file:
            content = (
-                # type: ignore # "str" has no attribute "json"
+                exported_result
-                exported_result if not self.output_pydantic else exported_result.json()
+                if not self.output_pydantic
                else exported_result.model_dump_json()  # type: ignore # "str" has no attribute "json"
            )
            self._save_file(content)
@@ -365,14 +380,12 @@ class Task(BaseModel):
        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)  # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
        directory = os.path.dirname(self.output_file)
        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:  # 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:
            file.write(result)
        return None
--- a/src/crewai/tools/tool_usage.py
+++ b/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(
@@ -123,6 +131,10 @@ class ToolUsage:
                tool=calling.tool_name, input=calling.arguments
            )
        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 [
@@ -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)
+                        result = tool.invoke(input=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.invoke(input={})
                            result = tool._run(*arguments)
                else:
                    result = tool._run()
            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()
--- a/src/crewai/translations/en.json
+++ b/src/crewai/translations/en.json
@@ -17,7 +17,7 @@
    "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: "
+    "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.",
--- a/src/crewai/utilities/converter.py
+++ b/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,
--- a/src/crewai/utilities/evaluators/task_evaluator.py
+++ b/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
--- a/src/crewai/utilities/file_handler.py
+++ b/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.
        """
--- a/src/crewai/utilities/token_counter_callback.py
+++ b/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):
+    def __init__(self, model_name, token_cost_process):
-        self.model = model
+        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")
--- a/tests/agent_test.py
+++ b/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
@@ -724,6 +723,73 @@ 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()
    assert result == "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 +800,7 @@ 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"
    )
--- a/tests/cassettes/test_agent_function_calling_llm.yaml
+++ b/tests/cassettes/test_agent_function_calling_llm.yaml
--- a/tests/cassettes/test_code_execution_flag_adds_code_tool_upon_kickoff.yaml
+++ b/tests/cassettes/test_code_execution_flag_adds_code_tool_upon_kickoff.yaml
--- a/tests/cassettes/test_crew_async_kickoff_for_each_full_ouput.yaml
+++ b/tests/cassettes/test_crew_async_kickoff_for_each_full_ouput.yaml
@@ -0,0 +1,585 @@
 interactions:
 - request:
    body: '{"messages": [{"content": "You are dog Researcher. You have a lot of experience
      with dog.\nYour personal goal is: Express hot takes on dog.To give my best complete
      final answer to the task use the exact following format:\n\nThought: I now can
      give a great answer\nFinal Answer: 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\nI MUST use these formats, my job depends on it!\nCurrent
      Task: Give me an analysis around dog.\n\nThis is the expect criteria for your
      final answer: 1 bullet point about dog that''s under 15 words. \n you MUST return
      the actual complete content as the final answer, not a summary.\n\nBegin! This
      is VERY important to you, use the tools available and give your best Final Answer,
      your job depends on it!\n\nThought:\n", "role": "user"}], "model": "gpt-4o",
      "n": 1, "stop": ["\nObservation"], "stream": true, "temperature": 0.7}'
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, br
      connection:
      - keep-alive
      content-length:
      - '951'
      content-type:
      - application/json
      host:
      - api.openai.com
      user-agent:
      - OpenAI/Python 1.34.0
      x-stainless-arch:
      - arm64
      x-stainless-async:
      - 'false'
      x-stainless-lang:
      - python
      x-stainless-os:
      - MacOS
      x-stainless-package-version:
      - 1.34.0
      x-stainless-runtime:
      - CPython
      x-stainless-runtime-version:
      - 3.12.3
    method: POST
    uri: https://api.openai.com/v1/chat/completions
  response:
    body:
      string: 'data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
        data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"content":"Thought"},"logprobs":null,"finish_reason":null}]}
        data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
        data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"content":"
        I"},"logprobs":null,"finish_reason":null}]}
        data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"content":"
        now"},"logprobs":null,"finish_reason":null}]}
        data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"content":"
        can"},"logprobs":null,"finish_reason":null}]}
        data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"content":"
        give"},"logprobs":null,"finish_reason":null}]}
        data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"content":"
        a"},"logprobs":null,"finish_reason":null}]}
        data: {"id":"chatcmpl-9gGly5pkMQFPEoB5vefeCguR5lZg5","object":"chat.completion.chunk","created":1719861278,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d576307f90","choices":[{"index":0,"delta":{"content":"
        great"},"logprobs":null,"finish_reason":null}]}
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--- a/tests/crew_test.py
+++ b/tests/crew_test.py
@@ -359,41 +359,125 @@ def test_api_calls_throttling(capsys):
        moveon.assert_called()
 # This test is not consistent, some issue is happening on the CI when it comes to Prompt tokens
 #  {'usage_metrics': {'completion_tokens': 34, 'prompt_tokens': 0, 'successful_requests': 2, 'total_tokens': 34}} CI OUTPUT
 #  {'usage_metrics': {'completion_tokens': 34, 'prompt_tokens': 314, 'successful_requests': 2, 'total_tokens': 348}}
 # The issue migh be related to the calculate_usage_metrics function
 # @pytest.mark.vcr(filter_headers=["authorization"])
 # def test_crew_full_output():
 #     agent = Agent(
 #         role="test role",
 #         goal="test goal",
 #         backstory="test backstory",
 #         allow_delegation=False,
 #         verbose=True,
 #     )
 #     task1 = Task(
 #         description="just say hi!",
 #         expected_output="your greeting",
 #         agent=agent,
 #     )
 #     task2 = Task(
 #         description="just say hello!",
 #         expected_output="your greeting",
 #         agent=agent,
 #     )
 #     crew = Crew(agents=[agent], tasks=[task1, task2], full_output=True)
 #     result = crew.kickoff()
 #     assert result == {
 #         "final_output": "Hello!",
 #         "tasks_outputs": [task1.output, task2.output],
 #         "usage_metrics": {
 #             "total_tokens": 348,
 #             "prompt_tokens": 314,
 #             "completion_tokens": 34,
 #             "successful_requests": 2,
 #         },
 #     }
@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_full_ouput():
+def test_crew_kickoff_for_each_full_ouput():
    inputs = [
        {"topic": "dog"},
        {"topic": "cat"},
        {"topic": "apple"},
    ]
    agent = Agent(
-        role="test role",
+        role="{topic} Researcher",
-        goal="test goal",
+        goal="Express hot takes on {topic}.",
-        backstory="test backstory",
+        backstory="You have a lot of experience with {topic}.",
        allow_delegation=False,
        verbose=True,
    )
-    task1 = Task(
+    task = Task(
-        description="just say hi!",
+        description="Give me an analysis around {topic}.",
-        expected_output="your greeting",
+        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    task2 = Task(
        description="just say hello!",
        expected_output="your greeting",
        agent=agent,
    )
-    crew = Crew(agents=[agent], tasks=[task1, task2], full_output=True)
+    crew = Crew(agents=[agent], tasks=[task], full_output=True)
    results = crew.kickoff_for_each(inputs=inputs)
-    result = crew.kickoff()
+    assert len(results) == len(inputs)
    for result in results:
        assert "usage_metrics" in result
        assert isinstance(result["usage_metrics"], dict)
-    assert result == {
+        # Assert that all required keys are in usage_metrics and their values are not None
-        "final_output": "Hello!",
+        for key in [
-        "tasks_outputs": [task1.output, task2.output],
+            "total_tokens",
-        "usage_metrics": {
+            "prompt_tokens",
-            "total_tokens": 517,
+            "completion_tokens",
-            "prompt_tokens": 466,
+            "successful_requests",
-            "completion_tokens": 51,
+        ]:
-            "successful_requests": 3,
+            assert key in result["usage_metrics"]
-        },
+            assert result["usage_metrics"][key] > 0
-    }
+
@pytest.mark.vcr(filter_headers=["authorization"])
@pytest.mark.asyncio
 async def test_crew_async_kickoff_for_each_full_ouput():
    inputs = [
        {"topic": "dog"},
        {"topic": "cat"},
        {"topic": "apple"},
    ]
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    crew = Crew(agents=[agent], tasks=[task], full_output=True)
    results = await crew.kickoff_for_each_async(inputs=inputs)
    assert len(results) == len(inputs)
    for result in results:
        assert "usage_metrics" in result
        assert isinstance(result["usage_metrics"], dict)
        # Assert that all required keys are in usage_metrics and their values are not None
        for key in [
            "total_tokens",
            "prompt_tokens",
            "completion_tokens",
            "successful_requests",
        ]:
            assert key in result["usage_metrics"]
            # TODO: FIX THIS WHEN USAGE METRICS ARE RE-DONE
            # assert result["usage_metrics"][key] > 0
 def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
@@ -417,13 +501,13 @@ def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
 def test_async_task_execution():
-    import threading
+    from concurrent.futures import ThreadPoolExecutor
-    from unittest.mock import patch
+    from unittest.mock import MagicMock, patch
    from crewai.tasks.task_output import TaskOutput
    list_ideas = Task(
-        description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
+        description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
        expected_output="Bullet point list of 5 important events.",
        agent=researcher,
        async_execution=True,
@@ -449,10 +533,11 @@ def test_async_task_execution():
    with patch.object(Agent, "execute_task") as execute:
        execute.return_value = "ok"
-        with patch.object(threading.Thread, "start") as start:
+        with patch.object(ThreadPoolExecutor, "submit") as submit:
-            thread = threading.Thread(target=lambda: None, args=()).start()
+            future = MagicMock()
-            start.return_value = thread
+            future.result.return_value = "ok"
-            with patch.object(threading.Thread, "join", wraps=thread.join()) as join:
+            submit.return_value = future
            list_ideas.output = TaskOutput(
                description="A 4 paragraph article about AI.",
                raw_output="ok",
@@ -464,8 +549,264 @@ def test_async_task_execution():
                agent="writer",
            )
            crew.kickoff()
-                start.assert_called()
+            submit.assert_called()
-                join.assert_called()
+            future.result.assert_called()
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_single_input():
    """Tests if kickoff_for_each works with a single input."""
    from unittest.mock import patch
    inputs = [{"topic": "dog"}]
    expected_outputs = ["Dogs are loyal companions and popular pets."]
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    with patch.object(Agent, "execute_task") as mock_execute_task:
        mock_execute_task.side_effect = expected_outputs
        crew = Crew(agents=[agent], tasks=[task])
        results = crew.kickoff_for_each(inputs=inputs)
    assert len(results) == 1
    assert results == expected_outputs
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_multiple_inputs():
    """Tests if kickoff_for_each works with multiple inputs."""
    from unittest.mock import patch
    inputs = [
        {"topic": "dog"},
        {"topic": "cat"},
        {"topic": "apple"},
    ]
    expected_outputs = [
        "Dogs are loyal companions and popular pets.",
        "Cats are independent and low-maintenance pets.",
        "Apples are a rich source of dietary fiber and vitamin C.",
    ]
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    with patch.object(Agent, "execute_task") as mock_execute_task:
        mock_execute_task.side_effect = expected_outputs
        crew = Crew(agents=[agent], tasks=[task])
        results = crew.kickoff_for_each(inputs=inputs)
    assert len(results) == len(inputs)
    for i, res in enumerate(results):
        assert res == expected_outputs[i]
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_empty_input():
    """Tests if kickoff_for_each handles an empty input list."""
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    crew = Crew(agents=[agent], tasks=[task])
    results = crew.kickoff_for_each(inputs=[])
    assert results == []
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_invalid_input():
    """Tests if kickoff_for_each raises TypeError for invalid input types."""
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    crew = Crew(agents=[agent], tasks=[task])
    with pytest.raises(TypeError):
        # Pass a string instead of a list
        crew.kickoff_for_each("invalid input")
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_error_handling():
    """Tests error handling in kickoff_for_each when kickoff raises an error."""
    from unittest.mock import patch
    inputs = [
        {"topic": "dog"},
        {"topic": "cat"},
        {"topic": "apple"},
    ]
    expected_outputs = [
        "Dogs are loyal companions and popular pets.",
        "Cats are independent and low-maintenance pets.",
        "Apples are a rich source of dietary fiber and vitamin C.",
    ]
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    crew = Crew(agents=[agent], tasks=[task])
    with patch.object(Crew, "kickoff") as mock_kickoff:
        mock_kickoff.side_effect = expected_outputs[:2] + [
            Exception("Simulated kickoff error")
        ]
        with pytest.raises(Exception, match="Simulated kickoff error"):
            crew.kickoff_for_each(inputs=inputs)
@pytest.mark.vcr(filter_headers=["authorization"])
@pytest.mark.asyncio
 async def test_kickoff_async_basic_functionality_and_output():
    """Tests the basic functionality and output of kickoff_async."""
    from unittest.mock import patch
    inputs = {"topic": "dog"}
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    # Create the crew
    crew = Crew(
        agents=[agent],
        tasks=[task],
    )
    expected_output = "This is a sample output from kickoff."
    with patch.object(Crew, "kickoff", return_value=expected_output) as mock_kickoff:
        result = await crew.kickoff_async(inputs)
        assert isinstance(result, str), "Result should be a string"
        assert result == expected_output, "Result should match expected output"
        mock_kickoff.assert_called_once_with(inputs)
@pytest.mark.vcr(filter_headers=["authorization"])
@pytest.mark.asyncio
 async def test_async_kickoff_for_each_async_basic_functionality_and_output():
    """Tests the basic functionality and output of akickoff_for_each_async."""
    from unittest.mock import patch
    inputs = [
        {"topic": "dog"},
        {"topic": "cat"},
        {"topic": "apple"},
    ]
    # Define expected outputs for each input
    expected_outputs = [
        "Dogs are loyal companions and popular pets.",
        "Cats are independent and low-maintenance pets.",
        "Apples are a rich source of dietary fiber and vitamin C.",
    ]
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    with patch.object(
        Crew, "kickoff_async", side_effect=expected_outputs
    ) as mock_kickoff_async:
        crew = Crew(agents=[agent], tasks=[task])
        results = await crew.kickoff_for_each_async(inputs)
        assert len(results) == len(inputs)
        assert results == expected_outputs
        for input_data in inputs:
            mock_kickoff_async.assert_any_call(inputs=input_data)
@pytest.mark.vcr(filter_headers=["authorization"])
@pytest.mark.asyncio
 async def test_async_kickoff_for_each_async_empty_input():
    """Tests if akickoff_for_each_async handles an empty input list."""
    agent = Agent(
        role="{topic} Researcher",
        goal="Express hot takes on {topic}.",
        backstory="You have a lot of experience with {topic}.",
    )
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="1 bullet point about {topic} that's under 15 words.",
        agent=agent,
    )
    # Create the crew
    crew = Crew(
        agents=[agent],
        tasks=[task],
    )
    # Call the function we are testing
    results = await crew.kickoff_for_each_async([])
    # Assertion
    assert results == [], "Result should be an empty list when input is empty"
 def test_set_agents_step_callback():
@@ -618,6 +959,9 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
    )
    crew = Crew(agents=[programmer], tasks=[task])
    with patch.object(Agent, "execute_task") as executor:
        executor.return_value = "ok"
        crew.kickoff()
        assert len(programmer.tools) == 1
        assert programmer.tools[0].__class__ == CodeInterpreterTool
@@ -691,6 +1035,29 @@ def test_agent_usage_metrics_are_captured_for_sequential_process():
    }
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_sequential_crew_creation_tasks_without_agents():
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
        # agent=researcher, # not having an agent on the task should throw an error
    )
    # Expected Output: The sequential crew should fail to create because the task is missing an agent
    with pytest.raises(pydantic_core._pydantic_core.ValidationError) as exec_info:
        Crew(
            tasks=[task],
            agents=[researcher],
            process=Process.sequential,
        )
    assert exec_info.value.errors()[0]["type"] == "missing_agent_in_task"
    assert (
        "Agent is missing in the task with the following description"
        in exec_info.value.errors()[0]["msg"]
    )
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_usage_metrics_are_captured_for_hierarchical_process():
    from langchain_openai import ChatOpenAI
@@ -715,13 +1082,68 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
    assert result == '"Howdy!"'
    assert crew.usage_metrics == {
-        "total_tokens": 1616,
+        "total_tokens": 1927,
-        "prompt_tokens": 1333,
+        "prompt_tokens": 1557,
-        "completion_tokens": 283,
+        "completion_tokens": 370,
-        "successful_requests": 3,
+        "successful_requests": 4,
    }
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_hierarchical_crew_creation_tasks_with_agents():
    """
    Agents are not required for tasks in a hierarchical process but sometimes they are still added
    This test makes sure that the manager still delegates the task to the agent even if the agent is passed in the task
    """
    from langchain_openai import ChatOpenAI
    task = Task(
        description="Write one amazing paragraph about AI.",
        expected_output="A single paragraph with 4 sentences.",
        agent=writer,
    )
    crew = Crew(
        tasks=[task],
        agents=[writer, researcher],
        process=Process.hierarchical,
        manager_llm=ChatOpenAI(model="gpt-4o"),
    )
    crew.kickoff()
    assert crew.manager_agent is not None
    assert crew.manager_agent.tools is not None
    assert crew.manager_agent.tools[0].description.startswith(
        "Delegate a specific task to one of the following coworkers: [Senior Writer]"
    )
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_hierarchical_crew_creation_tasks_without_async_execution():
    from langchain_openai import ChatOpenAI
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
        async_execution=True,  # should throw an error
    )
    with pytest.raises(pydantic_core._pydantic_core.ValidationError) as exec_info:
        Crew(
            tasks=[task],
            agents=[researcher],
            process=Process.hierarchical,
            manager_llm=ChatOpenAI(model="gpt-4o"),
        )
    assert (
        exec_info.value.errors()[0]["type"] == "async_execution_in_hierarchical_process"
    )
    assert (
        "Hierarchical process error: Tasks cannot be flagged with async_execution."
        in exec_info.value.errors()[0]["msg"]
    )
 def test_crew_inputs_interpolate_both_agents_and_tasks():
    agent = Agent(
        role="{topic} Researcher",
@@ -732,9 +1154,10 @@ def test_crew_inputs_interpolate_both_agents_and_tasks():
    task = Task(
        description="Give me an analysis around {topic}.",
        expected_output="{points} bullet points about {topic}.",
        agent=agent,
    )
-    crew = Crew(agents=[agent], tasks=[task], inputs={"topic": "AI", "points": 5})
+    crew = Crew(agents=[agent], tasks=[task])
    inputs = {"topic": "AI", "points": 5}
    crew._interpolate_inputs(inputs=inputs)  # Manual call for now
@@ -1039,6 +1462,7 @@ def test_crew_train_success(task_evaluator, crew_training_handler, kickoff):
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
        expected_output="5 bullet points with a paragraph for each idea.",
        agent=researcher,
    )
    crew = Crew(
@@ -1093,6 +1517,7 @@ def test_crew_train_error():
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article",
        expected_output="5 bullet points with a paragraph for each idea.",
        agent=researcher,
    )
    crew = Crew(
@@ -1114,6 +1539,7 @@ def test__setup_for_training():
    task = Task(
        description="Come up with a list of 5 interesting ideas to explore for an article",
        expected_output="5 bullet points with a paragraph for each idea.",
        agent=researcher,
    )
    crew = Crew(
--- a/tests/memory/cassettes/test_save_and_search.yaml
+++ b/tests/memory/cassettes/test_save_and_search.yaml
@@ -8,7 +8,7 @@ interactions:
      accept:
      - application/json
      accept-encoding:
-      - gzip, deflate, br
+      - gzip, deflate
      connection:
      - keep-alive
      content-length:
@@ -18,7 +18,7 @@ interactions:
      host:
      - api.openai.com
      user-agent:
-      - OpenAI/Python 1.25.1
+      - OpenAI/Python 1.35.10
      x-stainless-arch:
      - arm64
      x-stainless-async:
@@ -28,7 +28,7 @@ interactions:
      x-stainless-os:
      - MacOS
      x-stainless-package-version:
-      - 1.25.1
+      - 1.35.10
      x-stainless-runtime:
      - CPython
      x-stainless-runtime-version:
@@ -38,137 +38,135 @@ interactions:
  response:
    body:
      string: !!binary |
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    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:
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      access-control-allow-origin:
      - '*'
      alt-svc:
      - h3=":443"; ma=86400
      openai-model:
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      openai-organization:
      - crewai-iuxna1
      openai-processing-ms:
      - '15'
      openai-version:
      - '2020-10-01'
      strict-transport-security:
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      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
--- a/tests/task_test.py
+++ b/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():
@@ -310,7 +311,7 @@ def test_output_json_to_another_task():
    crew = Crew(agents=[scorer], tasks=[task1, task2])
    result = crew.kickoff()
-    assert '{\n  "score": 3\n}' == result
+    assert '{\n  "score": 5\n}' == result
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -393,6 +394,38 @@ 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 +446,29 @@ 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 +486,7 @@ 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 +521,7 @@ 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():
--- a/tests/utilities/test_file_handler.py
+++ b/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
Author	SHA1	Message	Date
João Moura	19e7ddca60	preping new verison	2024-07-12 15:10:31 -04:00
Gui Vieira	ecd02034fb	Fix tool usage	2024-07-12 15:06:14 -04:00
João Moura	a79b216875	preparing new version	2024-07-06 12:26:41 -04:00
João Moura	52217c2f63	updating dependencies and fixing tests (#878 )	2024-07-06 02:14:52 -03:00
Eelke van den Bos	7edacf6e24	Add converter_cls option to Task (#800 ) * Add converter_cls option to Task Fixes #799 * Update task_test.py * Update task.py * Update task.py * Update task_test.py * Update task.py * Update task.py * Update task.py * Update task.py --------- Co-authored-by: João Moura <joaomdmoura@gmail.com>	2024-07-06 02:01:39 -03:00
João Moura	58558a1950	TYPO	2024-07-06 00:34:50 -04:00
Ikko Eltociear Ashimine	1607c85ae5	chore: fix typo (#810 ) * chore: update converter.py attemps -> attempts * chore: update tool_usage.py attemps -> attempts	2024-07-06 01:33:48 -03:00
Alex Brinsmead	a6ff342948	Fix incorrect definition of RAG in GithubTool docs (#864 )	2024-07-06 01:31:51 -03:00
Taleb	d2eb54ebf8	Performed spell check across the entire documentation (#872 ) Thank you once again!	2024-07-06 01:30:40 -03:00
Eduardo Chiarotti	a41bd18599	Fix/async tasks (#877 ) * fix: async tasks calls * fix: some issue along with some type check errors * fix: some issue along with some type check errors * fix: async test	2024-07-06 01:30:07 -03:00
Eduardo Chiarotti	bb64c80964	fix: Fix tests (#873 ) * fix: call asserts * fix: test_increment_tool_errors * fix: test_increment_delegations_for_sequential_process * fix: test_increment_delegations_for_hierarchical_process * fix: test_code_execution_flag_adds_code_tool_upon_kickoff * fix: test_tool_usage_information_is_appended_to_agent * fix: try to fix test_crew_full_output * fix: try to fix test_crew_full_output * fix: test remove vcr to test crew_test test * fix: comment test to see if ci passes * fix: comment test to see if ci passes * fix: test changing prompt tokens to get error on CI * fix: test changing prompt tokens to get error on CI * fix: test changing prompt tokens to get error on CI * fix: test changing prompt tokens to get error on CI * fix: test new approach * fix: comment funciont not working in CI * fix: github python version * fix: remove need of vcr * fix: fix and add comments for all type checking errors	2024-07-05 09:06:56 -03:00
João Moura	2fb56f1f9f	Adding support to force a tool return to be the final answer. (#867 ) * Adding support to force a tool return to be the final answer. This will at the end of the execution return the tool output. It will return the output of the latest tool with the flag * Update src/crewai/agent.py Co-authored-by: Gui Vieira <guilherme_vieira@me.com> * Update tests/agent_test.py Co-authored-by: Gui Vieira <guilherme_vieira@me.com> --------- Co-authored-by: Gui Vieira <guilherme_vieira@me.com>	2024-07-04 16:36:00 -03:00
MO Jr	35676fe2f5	Update Crews.md (#868 ) Fix misspelling	2024-07-04 16:35:07 -03:00
Eduardo Chiarotti	81ed6f177e	fix: file_handler issue (#869 ) * fix: file_handler issue * fix: add logic for the trained_agent data	2024-07-04 16:34:43 -03:00
João Moura	4bcd1df6bb	TYPO	2024-07-03 18:41:52 -04:00
João Moura	6fae56dd60	TYPO	2024-07-03 18:41:52 -04:00
João Moura	430f0e9013	TYPO	2024-07-03 18:41:52 -04:00
João Moura	d7f080a978	fix agentops attribute	2024-07-03 18:41:52 -04:00
Lorenze Jay	5d18f73654	Lj/optional agent in task bug (#843 ) * fixed bug for manager overriding task agent and then added pydanic valditors to sequential when no agent is added to task * better test and fixed task.agent logic * fixed tests and better validator message * added validator for async_execution true in tasks whenever in hierarchical run	2024-07-03 18:45:53 -03:00
Brandon Hancock (bhancock_ai)	57fc079267	Bugfix/kickoff for each usage metrics (#844 ) * 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	2024-07-03 15:30:53 -03:00
Alex Brinsmead	706f4cd74a	Fix typos in EN "human_feedback" string (#859 ) * Fix typo in EN "human_feedback" string * Fix typos in EN "human_feedback" string	2024-07-03 15:26:58 -03:00
Taleb	2e3646cc96	Improved documentation for training module usage (#860 ) - Added detailed steps for training the crew programmatically. - Clarified the distinction between using the CLI and programmatic approaches. This update makes it easier for users to understand how to train their crew both through the CLI and programmatically, whether using a UI or API endpoints. Again Thank you to the author for the great project and the excellent foundation provided!	2024-07-03 15:26:32 -03:00
Brandon Hancock (bhancock_ai)	844cc515d5	Fix issue agentop poetry install issue (#863 ) * Fix issue agentop poetry install issue * Updated install requirements tests to fail if .lock becomes out of sync with poetry install. Cleaned up old issues that were merged back in.	2024-07-03 15:22:32 -03:00
Braelyn Boynton	f47904134b	Add back AgentOps as Optional Dependency (#543 ) * implements agentops with a langchain handler, agent tracking and tool call recording * track tool usage * end session after completion * track tool usage time * better tool and llm tracking * code cleanup * make agentops optional * optional dependency usage * remove telemetry code * optional agentops * agentops version bump * remove org key * true dependency * add crew org key to agentops * cleanup * Update pyproject.toml * Revert "true dependency" This reverts commit `e52e8e9568`. * Revert "cleanup" This reverts commit `7f5635fb9e`. * optional parent key * agentops 0.1.5 * Revert "Revert "cleanup"" This reverts commit `cea33d9a5d`. * Revert "Revert "true dependency"" This reverts commit `4d1b460b` * cleanup * Forcing version 0.1.5 * Update pyproject.toml * agentops update * noop * add crew tag * black formatting * use langchain callback handler to support all LLMs * agentops version bump * track task evaluator * merge upstream * Fix typo in instruction en.json (#676) * Enable search in docs (#663) * Clarify text in docstring (#662) * Update agent.py (#655) Changed default model value from gpt-4 to gpt-4o. Reasoning. gpt-4 costs 30$ per million tokens while gpt-4o costs 5$. This is more cost friendly for default option. * Update README.md (#652) Rework example so that if you use a custom LLM it doesn't throw code errors by uncommenting. * Update BrowserbaseLoadTool.md (#647) * Update crew.py (#644) Fixed Type on line 53 * fixes #665 (#666) * Added timestamp to logger (#646) * Added timestamp to logger Updated the logger.py file to include timestamps when logging output. For example: [2024-05-20 15:32:48][DEBUG]: == Working Agent: Researcher [2024-05-20 15:32:48][INFO]: == Starting Task: Research the topic [2024-05-20 15:33:22][DEBUG]: == [Researcher] Task output: * Update tool_usage.py * Revert "Update tool_usage.py" This reverts commit `95d18d5b6f`. incorrect bramch for this commit * support skip auto end session * conditional protect agentops use * fix crew logger bug * fix crew logger bug * Update crew.py * Update tool_usage.py --------- Co-authored-by: João Moura <joaomdmoura@gmail.com> Co-authored-by: Howard Gil <howardbgil@gmail.com> Co-authored-by: Olivier Roberdet <niox5199@gmail.com> Co-authored-by: Paul Sanders <psanders1@gmail.com> Co-authored-by: Anudeep Kolluri <50168940+Anudeep-Kolluri@users.noreply.github.com> Co-authored-by: Mike Heavers <heaversm@users.noreply.github.com> Co-authored-by: Mish Ushakov <10400064+mishushakov@users.noreply.github.com> Co-authored-by: theCyberTech - Rip&Tear <84775494+theCyberTech@users.noreply.github.com> Co-authored-by: Saif Mahmud <60409889+vmsaif@users.noreply.github.com>	2024-07-02 21:52:15 -03:00
Salman Faroz	d72b00af3c	Update Sequential.md (#849 ) To Resolve : pydantic_core._pydantic_core.ValidationError: 1 validation error for Task expected_output Field required [type=missing, input_value=, input_type=dict] For further information visit https://errors.pydantic.dev/2.6/v/missing "Expected Output" is mandatory now as it forces people to be specific about the expected result and get better result refer : https://github.com/joaomdmoura/crewAI/issues/308	2024-07-02 21:17:53 -03:00
Taleb	bd053a98c7	Enhanced documentation for readability and clarity (#855 ) - Added a "Parameters" column to attribute tables. Improved overall document formatting for enhanced readability and ease of use. Thank you to the author for the great project and the excellent foundation provided!	2024-07-02 21:17:04 -03:00
Lorenze Jay	c18208ca59	fixed mixin (#831 ) * fixed mixin * WIP: fixing types * type fixes on mixin	2024-07-02 21:16:26 -03:00
João Moura	acbe5af8ce	preparing new version	2024-07-02 09:03:20 -07:00
Eduardo Chiarotti	c81146505a	docs: Update training feature/code interpreter docs (#852 ) * docs: remove training docs from README * docs: add CodeinterpreterTool to docs and update docs * docs: fix name of tool	2024-07-02 13:00:37 -03:00