fix: fix and add comments for all type checking errors

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 remember 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 remeber what they did right and wrong across multiple executions |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. |
 | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
 

docs/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 "Cacheable result"
+    return "Cachable result"
 
 def my_cache_strategy(arguments: dict, result: str) -> bool:
     # Define custom caching logic

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

@@ -79,4 +79,5 @@ 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.
+3. `function_calling_llm`: Specify a separate language model for function calling.
+4

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

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

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

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

docs/index.md

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

docs/tools/GitHubSearchTool.md

@@ -4,7 +4,7 @@
     We are still working on improving tools, so there might be unexpected behavior or changes in the future.
 
 ## Description
-The GithubSearchTool is a 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.
+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.
 
 ## Installation
 To use the GithubSearchTool, first ensure the crewai_tools package is installed in your Python environment:

docs/tools/MDXSearchTool.md

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

docs/tools/TXTSearchTool.md

@@ -31,7 +31,7 @@ tool = TXTSearchTool(txt='path/to/text/file.txt')
 ```
 
 ## Arguments
-- `txt` (str): **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.
+- `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.
 
 ## Custom model and embeddings
 

mkdocs.yml

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

poetry.lock

@@ -343,17 +343,17 @@ lxml = ["lxml"]
 
 [[package]]
 name = "boto3"
-version = "1.34.140"
+version = "1.34.139"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.140-py3-none-any.whl", hash = "sha256:23ca8d8f7a30c3bbd989808056b5fc5d68ff5121c02c722c6167b6b1bb7f8726"},
-    {file = "boto3-1.34.140.tar.gz", hash = "sha256:578bbd5e356005719b6b610d03edff7ea1b0824d078afe62d3fb8bea72f83a87"},
+    {file = "boto3-1.34.139-py3-none-any.whl", hash = "sha256:98b2a12bcb30e679fa9f60fc74145a39db5ec2ca7b7c763f42896e3bd9b3a38d"},
+    {file = "boto3-1.34.139.tar.gz", hash = "sha256:32b99f0d76ec81fdca287ace2c9744a2eb8b92cb62bf4d26d52a4f516b63a6bf"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.140,<1.35.0"
+botocore = ">=1.34.139,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -362,13 +362,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.140"
+version = "1.34.139"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.140-py3-none-any.whl", hash = "sha256:43940d3a67d946ba3301631ba4078476a75f1015d4fb0fb0272d0b754b2cf9de"},
-    {file = "botocore-1.34.140.tar.gz", hash = "sha256:86302b2226c743b9eec7915a4c6cfaffd338ae03989cd9ee181078ef39d1ab39"},
+    {file = "botocore-1.34.139-py3-none-any.whl", hash = "sha256:dd1e085d4caa2a4c1b7d83e3bc51416111c8238a35d498e9d3b04f3b63b086ba"},
+    {file = "botocore-1.34.139.tar.gz", hash = "sha256:df023d8cf8999d574214dad4645cb90f9d2ccd1494f6ee2b57b1ab7522f6be77"},
 ]
 
 [package.dependencies]
@@ -747,13 +747,13 @@ all = ["pycocotools (==2.0.6)"]
 
 [[package]]
 name = "clarifai-grpc"
-version = "10.6.1"
+version = "10.5.4"
 description = "Clarifai gRPC API Client"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "clarifai_grpc-10.6.1-py3-none-any.whl", hash = "sha256:7f07c262f46042995b11af10cdd552718c4487e955db1b3f1253fcb0c2ab1ce1"},
-    {file = "clarifai_grpc-10.6.1.tar.gz", hash = "sha256:f692e3d6a051a1228ca371c3a9dc705cc9a61334eecc454d056f7af0b6f4dbad"},
+    {file = "clarifai_grpc-10.5.4-py3-none-any.whl", hash = "sha256:ae4c4d8985fdd2bf326cec27ee834571e44d0e989fb12686dd681f9b553ae218"},
+    {file = "clarifai_grpc-10.5.4.tar.gz", hash = "sha256:c67ce0dde186e8bab0d42a9923d28ddb4a05017b826c8e52ac7a86ec6df5f12a"},
 ]
 
 [package.dependencies]
@@ -840,13 +840,13 @@ files = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.4.8"
+version = "0.4.7"
 description = "Set of tools for the crewAI framework"
 optional = false
 python-versions = "<=3.13,>=3.10"
 files = [
-    {file = "crewai_tools-0.4.8-py3-none-any.whl", hash = "sha256:628b08515ee0e06c751da1dd66b0cff70c9b2644775891c8f59883cb5debfef4"},
-    {file = "crewai_tools-0.4.8.tar.gz", hash = "sha256:ae190bd187f980163523c86ee7e1eb2ed78896f935d6caff98908dd7ab6c982b"},
+    {file = "crewai_tools-0.4.7-py3-none-any.whl", hash = "sha256:3ff04b2da07d2c48e72f898511295b4a10038dd3e4fe859baa93fec1fb8baf8e"},
+    {file = "crewai_tools-0.4.7.tar.gz", hash = "sha256:4502a5e0ab94a7dae6638d000768f80049918909ca5338cdebc280351b3ce003"},
 ]
 
 [package.dependencies]
@@ -884,21 +884,6 @@ webencodings = "*"
 doc = ["sphinx", "sphinx_rtd_theme"]
 test = ["flake8", "isort", "pytest"]
 
-[[package]]
-name = "dataclasses-json"
-version = "0.6.7"
-description = "Easily serialize dataclasses to and from JSON."
-optional = false
-python-versions = "<4.0,>=3.7"
-files = [
-    {file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a"},
-    {file = "dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0"},
-]
-
-[package.dependencies]
-marshmallow = ">=3.18.0,<4.0.0"
-typing-inspect = ">=0.4.0,<1"
-
 [[package]]
 name = "decorator"
 version = "5.1.1"
@@ -1054,13 +1039,13 @@ idna = ">=2.0.0"
 
 [[package]]
 name = "embedchain"
-version = "0.1.116"
+version = "0.1.114"
 description = "Simplest open source retrieval (RAG) framework"
 optional = false
 python-versions = "<=3.13,>=3.9"
 files = [
-    {file = "embedchain-0.1.116-py3-none-any.whl", hash = "sha256:388835d047f9ff4542ebf50e3fa633ef596db262cbe506195ee4976b91a49172"},
-    {file = "embedchain-0.1.116.tar.gz", hash = "sha256:3e4d6418df2e749c2bd3cd3153c3857cbecd7227afe40b87d5ac3df629c394b2"},
+    {file = "embedchain-0.1.114-py3-none-any.whl", hash = "sha256:ce1b16196bcf53c679cacead0551a5466c33a9080a82be63f973e4437b0823ca"},
+    {file = "embedchain-0.1.114.tar.gz", hash = "sha256:fa5c4a29dd3c6b1137c772e1bc3e2d7ca489c58f46f4c7f7de133b3b9fc56e72"},
 ]
 
 [package.dependencies]
@@ -1068,14 +1053,11 @@ alembic = ">=1.13.1,<2.0.0"
 beautifulsoup4 = ">=4.12.2,<5.0.0"
 chromadb = ">=0.4.24,<0.5.0"
 clarifai = ">=10.0.1,<11.0.0"
-cohere = ">=5.3,<6.0"
 google-cloud-aiplatform = ">=1.26.1,<2.0.0"
 gptcache = ">=0.1.43,<0.2.0"
 langchain = ">0.2,<=0.3"
 langchain-cohere = ">=0.1.4,<0.2.0"
-langchain-community = ">=0.2.6,<0.3.0"
 langchain-openai = ">=0.1.7,<0.2.0"
-memzero = ">=0.0.7,<0.0.8"
 openai = ">=1.1.1"
 posthog = ">=3.0.2,<4.0.0"
 pypdf = ">=4.0.1,<5.0.0"
@@ -1088,6 +1070,7 @@ tiktoken = ">=0.7.0,<0.8.0"
 
 [package.extras]
 aws-bedrock = ["boto3 (>=1.34.20,<2.0.0)"]
+cohere = ["cohere (>=5.3,<6.0)"]
 dataloaders = ["docx2txt (>=0.8,<0.9)", "duckduckgo-search (>=6.1.5,<7.0.0)", "pytube (>=15.0.0,<16.0.0)", "sentence-transformers (>=2.2.2,<3.0.0)", "youtube-transcript-api (>=0.6.1,<0.7.0)"]
 discord = ["discord (>=2.3.2,<3.0.0)"]
 dropbox = ["dropbox (>=11.36.2,<12.0.0)"]
@@ -2052,13 +2035,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "identify"
-version = "2.6.0"
+version = "2.5.36"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.6.0-py2.py3-none-any.whl", hash = "sha256:e79ae4406387a9d300332b5fd366d8994f1525e8414984e1a59e058b2eda2dd0"},
-    {file = "identify-2.6.0.tar.gz", hash = "sha256:cb171c685bdc31bcc4c1734698736a7d5b6c8bf2e0c15117f4d469c8640ae5cf"},
+    {file = "identify-2.5.36-py2.py3-none-any.whl", hash = "sha256:37d93f380f4de590500d9dba7db359d0d3da95ffe7f9de1753faa159e71e7dfa"},
+    {file = "identify-2.5.36.tar.gz", hash = "sha256:e5e00f54165f9047fbebeb4a560f9acfb8af4c88232be60a488e9b68d122745d"},
 ]
 
 [package.extras]
@@ -2419,32 +2402,6 @@ files = [
 cohere = ">=5.5.6,<6.0"
 langchain-core = ">=0.2.0,<0.3"
 
-[[package]]
-name = "langchain-community"
-version = "0.2.6"
-description = "Community contributed LangChain integrations."
-optional = false
-python-versions = "<4.0,>=3.8.1"
-files = [
-    {file = "langchain_community-0.2.6-py3-none-any.whl", hash = "sha256:758cc800acfe5dd396bf8ba1b57c4792639ead0eab48ed0367f0732ec6ee1f68"},
-    {file = "langchain_community-0.2.6.tar.gz", hash = "sha256:40ce09a50ed798aa651ddb34c8978200fa8589b9813c7a28ce8af027bbf249f0"},
-]
-
-[package.dependencies]
-aiohttp = ">=3.8.3,<4.0.0"
-dataclasses-json = ">=0.5.7,<0.7"
-langchain = ">=0.2.6,<0.3.0"
-langchain-core = ">=0.2.10,<0.3.0"
-langsmith = ">=0.1.0,<0.2.0"
-numpy = [
-    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
-    {version = ">=1,<2", markers = "python_version < \"3.12\""},
-]
-PyYAML = ">=5.3"
-requests = ">=2,<3"
-SQLAlchemy = ">=1.4,<3"
-tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
-
 [[package]]
 name = "langchain-core"
 version = "0.2.11"
@@ -2499,13 +2456,13 @@ langchain-core = ">=0.2.10,<0.3.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.84"
+version = "0.1.83"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.84-py3-none-any.whl", hash = "sha256:01f3c6390dba26c583bac8dd0e551ce3d0509c7f55cad714db0b5c8d36e4c7ff"},
-    {file = "langsmith-0.1.84.tar.gz", hash = "sha256:5220c0439838b9a5bd320fd3686be505c5083dcee22d2452006c23891153bea1"},
+    {file = "langsmith-0.1.83-py3-none-any.whl", hash = "sha256:f54d8cd8479b648b6339f3f735d19292c3516d080f680933ecdca3eab4b67ed3"},
+    {file = "langsmith-0.1.83.tar.gz", hash = "sha256:5cdd947212c8ad19adb992c06471c860185a777daa6859bb47150f90daf64bf3"},
 ]
 
 [package.dependencies]
@@ -2643,25 +2600,6 @@ files = [
     {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"},
 ]
 
-[[package]]
-name = "marshmallow"
-version = "3.21.3"
-description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"},
-    {file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"},
-]
-
-[package.dependencies]
-packaging = ">=17.0"
-
-[package.extras]
-dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
-docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.3.7)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
-tests = ["pytest", "pytz", "simplejson"]
-
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -2673,22 +2611,6 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
-[[package]]
-name = "memzero"
-version = "0.0.7"
-description = "Long-term memory for AI Agents"
-optional = false
-python-versions = "<4.0,>=3.9"
-files = [
-    {file = "memzero-0.0.7-py3-none-any.whl", hash = "sha256:65f6da88d46263dbc05621fcd01bd09616d0e7f082d55ed9899dc2152491ffd2"},
-    {file = "memzero-0.0.7.tar.gz", hash = "sha256:0c1f413d8ee0ade955fe9f8b8f5aff2cf58bc94869537aca62139db3d9f50725"},
-]
-
-[package.dependencies]
-httpx = ">=0.27.0,<0.28.0"
-posthog = ">=3.5.0,<4.0.0"
-pydantic = ">=2.7.3,<3.0.0"
-
 [[package]]
 name = "mergedeep"
 version = "1.3.4"
@@ -5050,13 +4972,13 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tenacity"
-version = "8.5.0"
+version = "8.4.2"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
-    {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
+    {file = "tenacity-8.4.2-py3-none-any.whl", hash = "sha256:9e6f7cf7da729125c7437222f8a522279751cdfbe6b67bfe64f75d3a348661b2"},
+    {file = "tenacity-8.4.2.tar.gz", hash = "sha256:cd80a53a79336edba8489e767f729e4f391c896956b57140b5d7511a64bbd3ef"},
 ]
 
 [package.extras]
@@ -5283,13 +5205,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "trio"
-version = "0.26.0"
+version = "0.25.1"
 description = "A friendly Python library for async concurrency and I/O"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "trio-0.26.0-py3-none-any.whl", hash = "sha256:bb9c1b259591af941fccfbabbdc65bc7ed764bd2db76428454c894cd5e3d2032"},
-    {file = "trio-0.26.0.tar.gz", hash = "sha256:67c5ec3265dd4abc7b1d1ab9ca4fe4c25b896f9c93dac73713778adab487f9c4"},
+    {file = "trio-0.25.1-py3-none-any.whl", hash = "sha256:e42617ba091e7b2e50c899052e83a3c403101841de925187f61e7b7eaebdf3fb"},
+    {file = "trio-0.25.1.tar.gz", hash = "sha256:9f5314f014ea3af489e77b001861c535005c3858d38ec46b6b071ebfa339d7fb"},
 ]
 
 [package.dependencies]
@@ -5392,21 +5314,6 @@ files = [
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
-[[package]]
-name = "typing-inspect"
-version = "0.9.0"
-description = "Runtime inspection utilities for typing module."
-optional = false
-python-versions = "*"
-files = [
-    {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
-    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
-]
-
-[package.dependencies]
-mypy-extensions = ">=0.3.0"
-typing-extensions = ">=3.7.4"
-
 [[package]]
 name = "ujson"
 version = "5.10.0"
@@ -6090,4 +5997,4 @@ tools = ["crewai-tools"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<=3.13"
-content-hash = "0dbf6f6e2e841fb3eec4ff87ea5d6b430f29702118fee91307983c6b2581e59e"
+content-hash = "4f3e5fddb5f0fc8fd143a8abe947ecac443213d595bd0eeed745ccb82dac2312"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.36.0"
+version = "0.35.8"
 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"
@@ -21,7 +21,7 @@ opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
 regex = "^2023.12.25"
-crewai-tools = { version = "^0.4.8", optional = true }
+crewai-tools = { version = "^0.4.7", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -45,7 +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 = "^0.4.8"
+crewai-tools = "^0.4.7"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"

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

@@ -27,7 +27,7 @@ class OutputConverter(BaseModel, ABC):
     model: Any = Field(description="The model to be used to convert the text.")
     instructions: str = Field(description="Conversion instructions to the LLM.")
     max_attempts: Optional[int] = Field(
-        description="Max number of attempts to try to get the output formatted.",
+        description="Max number of attemps to try to get the output formated.",
         default=3,
     )
 

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

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

src/crewai/crew.py

@@ -30,7 +30,6 @@ 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.planning_handler import CrewPlanner
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 try:
@@ -134,10 +133,6 @@ class Crew(BaseModel):
         default=False,
         description="output_log_file",
     )
-    planning: Optional[bool] = Field(
-        default=False,
-        description="Plan the crew execution and add the plan to the crew.",
-    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -345,9 +340,6 @@ class Crew(BaseModel):
 
             agent.create_agent_executor()
 
-        if self.planning:
-            self._handle_crew_planning()
-
         metrics = []
 
         if self.process == Process.sequential:
@@ -429,25 +421,17 @@ class Crew(BaseModel):
 
         return results
 
-    def _handle_crew_planning(self):
-        """Handles the Crew planning."""
-        self._logger.log("info", "Planning the crew execution")
-        result = CrewPlanner(self.tasks)._handle_crew_planning()
-
-        for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
-            task.description += step_plan
-
-    def _run_sequential_process(self) -> Union[str, Dict[str, Any]]:
+    def _run_sequential_process(self) -> str:
         """Executes tasks sequentially and returns the final output."""
-        task_output = None
+        task_output = ""
 
         for task in self.tasks:
-            if task.agent and task.agent.allow_delegation:
+            if task.agent.allow_delegation:  # type: ignore #  Item "None" of "Agent | None" has no attribute "allow_delegation"
                 agents_for_delegation = [
                     agent for agent in self.agents if agent != task.agent
                 ]
                 if len(self.agents) > 1 and len(agents_for_delegation) > 0:
-                    task.tools += task.agent.get_delegation_tools(agents_for_delegation)
+                    task.tools += task.agent.get_delegation_tools(agents_for_delegation)  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_delegation_tools"
 
             role = task.agent.role if task.agent is not None else "None"
             self._logger.log("debug", f"== Working Agent: {role}", color="bold_purple")
@@ -474,7 +458,7 @@ class Crew(BaseModel):
 
         token_usage = self.calculate_usage_metrics()
 
-        return self._format_output(task_output if task_output else "", token_usage)
+        return self._format_output(task_output, token_usage)  # type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
 
     def _run_hierarchical_process(
         self,
@@ -499,7 +483,7 @@ class Crew(BaseModel):
             )
             self.manager_agent = manager
 
-        task_output = None
+        task_output = ""
 
         for task in self.tasks:
             self._logger.log("debug", f"Working Agent: {manager.role}")
@@ -526,11 +510,10 @@ 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()
 
-        return self._format_output(
-            task_output if task_output else "", token_usage
-        ), token_usage
+        return self._format_output(task_output, token_usage), token_usage
 
     def copy(self):
         """Create a deep copy of the Crew."""
@@ -591,7 +574,7 @@ class Crew(BaseModel):
         """
 
         if self.full_output:
-            return {
+            return {  # type: ignore # Incompatible return value type (got "dict[str, Sequence[str | TaskOutput | None]]", expected "str")
                 "final_output": output,
                 "tasks_outputs": [task.output for task in self.tasks if task],
                 "usage_metrics": token_usage,

src/crewai/task.py

@@ -1,7 +1,7 @@
 import os
 import re
+import threading
 import uuid
-from concurrent.futures import Future, ThreadPoolExecutor
 from copy import copy
 from typing import Any, Dict, List, Optional, Type, Union
 
@@ -13,7 +13,7 @@ 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.converter import Converter, ConverterError
+from crewai.utilities.converter import ConverterError
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import Printer
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
@@ -97,16 +97,12 @@ 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
-    _future: Future | None = None
+    _thread: threading.Thread | None = None
 
     def __init__(__pydantic_self__, **data):
         config = data.pop("config", {})
@@ -165,53 +161,51 @@ 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._future:
-            self._future.result()  # Wait for the future to complete
-            self._future = None
+        if self._thread:
+            self._thread.join()
+            self._thread = None
 
         assert self.output, "Task output is not set."
 
         return self.output.exported_output
 
-    def execute(
+    def execute(  # type: ignore # Missing return statement
         self,
         agent: BaseAgent | None = None,
         context: Optional[str] = None,
         tools: Optional[List[Any]] = None,
-    ) -> Any:
+    ) -> str:
         """Execute the task.
 
         Returns:
             Output of the task.
         """
 
+        self._execution_span = self._telemetry.task_started(self)
+
         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."
             )
 
-        self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
-
         if self.context:
-            internal_context = []
+            context = []  # type: ignore # Incompatible types in assignment (expression has type "list[Never]", variable has type "str | None")
             for task in self.context:
                 if task.async_execution:
                     task.wait_for_completion()
                 if task.output:
-                    internal_context.append(task.output.raw_output)
-            context = "\n".join(internal_context)
+                    context.append(task.output.raw_output)  # type: ignore # Item "str" of "str | None" has no attribute "append"
+            context = "\n".join(context)  # type: ignore # Argument 1 to "join" of "str" has incompatible type "str | None"; expected "Iterable[str]"
 
         self.prompt_context = context
         tools = tools or self.tools
 
         if self.async_execution:
-            with ThreadPoolExecutor() as executor:
-                self._future = executor.submit(
-                    self._execute, agent, self, context, tools
-                )
-            return None
+            self._thread = threading.Thread(
+                target=self._execute, args=(agent, self, context, tools)
+            )
+            self._thread.start()
         else:
             result = self._execute(
                 task=self,
@@ -221,7 +215,7 @@ class Task(BaseModel):
             )
             return result
 
-    def _execute(self, agent: "BaseAgent", task, context, tools) -> str | None:
+    def _execute(self, agent: "BaseAgent", task, context, tools):
         result = agent.execute_task(
             task=task,
             context=context,
@@ -229,7 +223,7 @@ class Task(BaseModel):
         )
         exported_output = self._export_output(result)
 
-        self.output = TaskOutput(
+        self.output = TaskOutput(  # type: ignore # the responses are usually str but need to figure out a more elegant solution here
             description=self.description,
             exported_output=exported_output,
             raw_output=result,
@@ -291,7 +285,7 @@ class Task(BaseModel):
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
 
         cloned_context = (
-            [task.copy(agents) for task in self.context] if self.context else None
+            [task.copy() for task in self.context] if self.context else None
         )
 
         def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
@@ -309,17 +303,7 @@ class Task(BaseModel):
 
         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:  # TODO: Refactor and fix type hints
+    def _export_output(self, result: str) -> Any:
         exported_result = result
         instructions = "I'm gonna convert this raw text into valid JSON."
 
@@ -349,7 +333,7 @@ class Task(BaseModel):
                 model_schema = PydanticSchemaParser(model=model).get_schema()  # type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
                 instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
 
-            converter = self._create_converter(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
+            converter = self.agent.get_output_converter(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
                 llm=llm, text=result, model=model, instructions=instructions
             )
 

src/crewai/telemetry/telemetry.py

@@ -156,35 +156,18 @@ class Telemetry:
             except Exception:
                 pass
 
-    def task_started(self, crew: Crew, task: Task) -> Span | None:
+    def task_started(self, task: Task) -> Span | None:
         """Records task started in a crew."""
         if self.ready:
             try:
                 tracer = trace.get_tracer("crewai.telemetry")
                 span = tracer.start_span("Task Execution")
 
-                created_span = tracer.start_span("Task Created")
-
-                self._add_attribute(created_span, "task_id", str(task.id))
-
-                if crew.share_crew:
-                    self._add_attribute(
-                        created_span, "formatted_description", task.description
-                    )
-                    self._add_attribute(
-                        created_span, "formatted_expected_output", task.expected_output
-                    )
-
-                created_span.set_status(Status(StatusCode.OK))
-                created_span.end()
-
                 self._add_attribute(span, "task_id", str(task.id))
-
-                if crew.share_crew:
-                    self._add_attribute(span, "formatted_description", task.description)
-                    self._add_attribute(
-                        span, "formatted_expected_output", task.expected_output
-                    )
+                self._add_attribute(span, "formatted_description", task.description)
+                self._add_attribute(
+                    span, "formatted_expected_output", task.expected_output
+                )
 
                 return span
             except Exception:

src/crewai/tools/tool_calling.py

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

src/crewai/tools/tool_usage.py

@@ -119,7 +119,7 @@ class ToolUsage:
                     attempts=self._run_attempts,
                 )
                 result = self._format_result(result=result)  # type: ignore #  "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
-                return result  # type: ignore # Fix the return type of this function
+                return result  # type: ignore # Fix the reutrn type of this function
 
             except Exception:
                 self.task.increment_tools_errors()

src/crewai/translations/en.json

@@ -16,7 +16,7 @@
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task\nYour final answer must be the great and the most complete as possible, it must be outcome described\n\n",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
     "expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary.",
-    "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
+    "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's final answer: {final_answer}\nPlease provide feedback: "
   },
   "errors": {

src/crewai/utilities/planning_handler.py

@@ -1,64 +0,0 @@
-from typing import List
-
-from pydantic import BaseModel
-
-from crewai.agent import Agent
-from crewai.task import Task
-
-
-class PlannerTaskPydanticOutput(BaseModel):
-    list_of_plans_per_task: List[str]
-
-
-class CrewPlanner:
-    def __init__(self, tasks: List[Task]):
-        self.tasks = tasks
-
-    def _handle_crew_planning(self):
-        """Handles the Crew planning by creating detailed step-by-step plans for each task."""
-        planning_agent = self._create_planning_agent()
-        tasks_summary = self._create_tasks_summary()
-
-        planner_task = self._create_planner_task(planning_agent, tasks_summary)
-
-        return planner_task.execute()
-
-    def _create_planning_agent(self) -> Agent:
-        """Creates the planning agent for the crew planning."""
-        return Agent(
-            role="Task Execution Planner",
-            goal=(
-                "Your goal is to create an extremely detailed, step-by-step plan based on the tasks and tools "
-                "available to each agent so that they can perform the tasks in an exemplary manner"
-            ),
-            backstory="Planner agent for crew planning",
-        )
-
-    def _create_planner_task(self, planning_agent: Agent, tasks_summary: str) -> Task:
-        """Creates the planner task using the given agent and tasks summary."""
-        return Task(
-            description=(
-                f"Based on these tasks summary: {tasks_summary} \n Create the most descriptive plan based on the tasks "
-                "descriptions, tools available, and agents' goals for them to execute their goals with perfection."
-            ),
-            expected_output="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
-            agent=planning_agent,
-            output_pydantic=PlannerTaskPydanticOutput,
-        )
-
-    def _create_tasks_summary(self) -> str:
-        """Creates a summary of all tasks."""
-        tasks_summary = []
-        for idx, task in enumerate(self.tasks):
-            tasks_summary.append(
-                f"""
-                Task Number {idx + 1} - {task.description}
-                "task_description": {task.description}
-                "task_expected_output": {task.expected_output}
-                "agent": {task.agent.role if task.agent else "None"}
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tests/crew_test.py

@@ -501,13 +501,13 @@ def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
 
 
 def test_async_task_execution():
-    from concurrent.futures import ThreadPoolExecutor
-    from unittest.mock import MagicMock, patch
+    import threading
+    from unittest.mock import patch
 
     from crewai.tasks.task_output import TaskOutput
 
     list_ideas = Task(
-        description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
+        description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
         expected_output="Bullet point list of 5 important events.",
         agent=researcher,
         async_execution=True,
@@ -533,24 +533,23 @@ def test_async_task_execution():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        with patch.object(ThreadPoolExecutor, "submit") as submit:
-            future = MagicMock()
-            future.result.return_value = "ok"
-            submit.return_value = future
-
-            list_ideas.output = TaskOutput(
-                description="A 4 paragraph article about AI.",
-                raw_output="ok",
-                agent="writer",
-            )
-            list_important_history.output = TaskOutput(
-                description="A 4 paragraph article about AI.",
-                raw_output="ok",
-                agent="writer",
-            )
-            crew.kickoff()
-            submit.assert_called()
-            future.result.assert_called()
+        with patch.object(threading.Thread, "start") as start:
+            thread = threading.Thread(target=lambda: None, args=()).start()
+            start.return_value = thread
+            with patch.object(threading.Thread, "join", wraps=thread.join()) as join:
+                list_ideas.output = TaskOutput(
+                    description="A 4 paragraph article about AI.",
+                    raw_output="ok",
+                    agent="writer",
+                )
+                list_important_history.output = TaskOutput(
+                    description="A 4 paragraph article about AI.",
+                    raw_output="ok",
+                    agent="writer",
+                )
+                crew.kickoff()
+                start.assert_called()
+                join.assert_called()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

tests/task_test.py

@@ -9,7 +9,6 @@ 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():
@@ -394,38 +393,6 @@ 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