fix: fix and add comments for all type checking errors

.gitignore

@@ -14,5 +14,4 @@ test.py
 rc-tests/*
 *.pkl
 temp/*
-.vscode/*
-crew_tasks_output.json
+.vscode/*

docs/core-concepts/Crews.md

@@ -155,23 +155,4 @@ for async_result in async_results:
     print(async_result)
 ```
 
-### Replaying from specific task:
-You can now replay from a specific task using our cli command replay.
-
-The replay_from_tasks feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the task name for the replay process.
-
-Kickoffs will now create a `crew_tasks_ouput.json` file with the output of the tasks which you use to retrieve the task id to replay.
-
-
-### Replaying from specific task Using the CLI
-To use the replay feature, follow these steps:
-
-1. Open your terminal or command prompt.
-2. Navigate to the directory where your CrewAI project is located.
-3. Run the following command:
-
-```shell
-crewai replay -t <task_id>
-```
-
 These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs

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/cli.py

@@ -1,11 +1,8 @@
 import click
 import pkg_resources
 
-
 from .create_crew import create_crew
 from .train_crew import train_crew
-from .replay_from_task import replay_task_command
-from .list_task_outputs import show_task_outputs
 
 
 @click.group()
@@ -51,32 +48,5 @@ def train(n_iterations: int):
     train_crew(n_iterations)
 
 
-@crewai.command()
-@click.option(
-    "-t",
-    "--task_id",
-    type=str,
-    help="Replay the crew from this task ID, including all subsequent tasks.",
-)
-def replay(task_id: str) -> None:
-    """
-    Replay the crew execution from a specific task.
-
-    Args:
-        task_id (str): The ID of the task to replay from.
-    """
-    try:
-        click.echo(f"Replaying the crew from task {task_id}")
-        replay_task_command(task_id)
-    except Exception as e:
-        click.echo(f"An error occurred while replaying: {e}", err=True)
-
-
-@crewai.command()
-def list_completed_tasks_ids():
-    """List all task outputs saved from crew_tasks_output.json."""
-    show_task_outputs()
-
-
 if __name__ == "__main__":
     crewai()

src/crewai/cli/list_task_outputs.py

@@ -1,34 +0,0 @@
-import subprocess
-import click
-from pathlib import Path
-import json
-
-
-def show_task_outputs() -> None:
-    """
-    Replay the crew execution from a specific task.
-
-    Args:
-      task_id (str): The ID of the task to replay from.
-    """
-
-    try:
-        file_path = Path("crew_tasks_output.json")
-        if not file_path.exists():
-            click.echo("crew_tasks_output.json not found.")
-            return
-
-        with open(file_path, "r") as f:
-            tasks = json.load(f)
-
-        for index, task in enumerate(tasks):
-            click.echo(f"Task {index + 1}: {task['task_id']}")
-            click.echo(f"Description: {task['output']['description']}")
-            click.echo("---")
-
-    except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while replaying the task: {e}", err=True)
-        click.echo(e.output, err=True)
-
-    except Exception as e:
-        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/replay_from_task.py

@@ -1,24 +0,0 @@
-import subprocess
-import click
-
-
-def replay_task_command(task_id: str) -> None:
-    """
-    Replay the crew execution from a specific task.
-
-    Args:
-      task_id (str): The ID of the task to replay from.
-    """
-    command = ["poetry", "run", "replay_from_task", task_id]
-
-    try:
-        result = subprocess.run(command, capture_output=False, text=True, check=True)
-        if result.stderr:
-            click.echo(result.stderr, err=True)
-
-    except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while replaying the task: {e}", err=True)
-        click.echo(e.output, err=True)
-
-    except Exception as e:
-        click.echo(f"An unexpected error occurred: {e}", err=True)

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/cli/templates/main.py

@@ -21,13 +21,3 @@ def train():
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
-
-def replay_from_task():
-    """
-    Replay the crew execution from a specific task.
-    """
-    try:
-        {{crew_name}}Crew().crew().replay_from_task(task_id=sys.argv[1])
-
-    except Exception as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")

src/crewai/cli/templates/pyproject.toml

@@ -11,7 +11,6 @@ crewai = { extras = ["tools"], version = "^0.35.8" }
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"
 train = "{{folder_name}}.main:train"
-replay = "{{folder_name}}.main:replay_from_task"
 
 [build-system]
 requires = ["poetry-core"]

src/crewai/crew.py

@@ -1,7 +1,6 @@
 import asyncio
 import json
 import uuid
-from concurrent.futures import Future
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from langchain_core.callbacks import BaseCallbackHandler
@@ -21,30 +20,16 @@ from pydantic_core import PydanticCustomError
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
-from crewai.crews.crew_output import CrewOutput
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.process import Process
 from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools import AgentTools
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import (
-    CREW_TASKS_OUTPUT_FILE,
-    TRAINED_AGENTS_DATA_FILE,
-    TRAINING_DATA_FILE,
-)
+from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-from crewai.utilities.task_output_handler import (
-    ExecutionLog,
-    TaskOutputJsonHandler,
-)
-from crewai.utilities.formatter import (
-    aggregate_raw_outputs_from_task_outputs,
-    aggregate_raw_outputs_from_tasks,
-)
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 try:
@@ -72,6 +57,7 @@ class Crew(BaseModel):
         max_rpm: Maximum number of requests per minute for the crew execution to be respected.
         prompt_file: Path to the prompt json file to be used for the crew.
         id: A unique identifier for the crew instance.
+        full_output: Whether the crew should return the full output with all tasks outputs and token usage metrics or just the final output.
         task_callback: Callback to be executed after each task for every agents execution.
         step_callback: Callback to be executed after each step for every agents execution.
         share_crew: Whether you want to share the complete crew information and execution with crewAI to make the library better, and allow us to train models.
@@ -88,10 +74,6 @@ class Crew(BaseModel):
     _entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
     _train: Optional[bool] = PrivateAttr(default=False)
     _train_iteration: Optional[int] = PrivateAttr()
-    _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
-    _logging_color: str = PrivateAttr(
-        default="bold_purple",
-    )
 
     cache: bool = Field(default=True)
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -111,6 +93,10 @@ class Crew(BaseModel):
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
+    full_output: Optional[bool] = Field(
+        default=False,
+        description="Whether the crew should return the full output with all tasks outputs and token usage metrics or just the final output.",
+    )
     manager_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
@@ -147,14 +133,6 @@ class Crew(BaseModel):
         default=False,
         description="output_log_file",
     )
-    task_execution_output_json_files: Optional[List[str]] = Field(
-        default=None,
-        description="List of file paths for task execution JSON files.",
-    )
-    execution_logs: List[ExecutionLog] = Field(
-        default=[],
-        description="List of execution logs for tasks",
-    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -274,63 +252,6 @@ class Crew(BaseModel):
 
         return self
 
-    @model_validator(mode="after")
-    def validate_end_with_at_most_one_async_task(self):
-        """Validates that the crew ends with at most one asynchronous task."""
-        final_async_task_count = 0
-
-        # Traverse tasks backward
-        for task in reversed(self.tasks):
-            if task.async_execution:
-                final_async_task_count += 1
-            else:
-                break  # Stop traversing as soon as a non-async task is encountered
-
-        if final_async_task_count > 1:
-            raise PydanticCustomError(
-                "async_task_count",
-                "The crew must end with at most one asynchronous task.",
-                {},
-            )
-
-        return self
-
-    @model_validator(mode="after")
-    def validate_async_task_cannot_include_sequential_async_tasks_in_context(self):
-        """
-        Validates that if a task is set to be executed asynchronously,
-        it cannot include other asynchronous tasks in its context unless
-        separated by a synchronous task.
-        """
-        for i, task in enumerate(self.tasks):
-            if task.async_execution and task.context:
-                for context_task in task.context:
-                    if context_task.async_execution:
-                        for j in range(i - 1, -1, -1):
-                            if self.tasks[j] == context_task:
-                                raise ValueError(
-                                    f"Task '{task.description}' is asynchronous and cannot include other sequential asynchronous tasks in its context."
-                                )
-                            if not self.tasks[j].async_execution:
-                                break
-        return self
-
-    @model_validator(mode="after")
-    def validate_context_no_future_tasks(self):
-        """Validates that a task's context does not include future tasks."""
-        task_indices = {id(task): i for i, task in enumerate(self.tasks)}
-
-        for task in self.tasks:
-            if task.context:
-                for context_task in task.context:
-                    if id(context_task) not in task_indices:
-                        continue  # Skip context tasks not in the main tasks list
-                    if task_indices[id(context_task)] > task_indices[id(task)]:
-                        raise ValueError(
-                            f"Task '{task.description}' has a context dependency on a future task '{context_task.description}', which is not allowed."
-                        )
-        return self
-
     def _setup_from_config(self):
         assert self.config is not None, "Config should not be None."
 
@@ -393,17 +314,12 @@ class Crew(BaseModel):
 
     def kickoff(
         self,
-        inputs: Optional[Dict[str, Any]] = None,
-    ) -> CrewOutput:
+        inputs: Optional[Dict[str, Any]] = {},
+    ) -> Union[str, Dict[str, Any]]:
         """Starts the crew to work on its assigned tasks."""
         self._execution_span = self._telemetry.crew_execution_span(self, inputs)
-        TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).initialize_file()
-        TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).reset()
-        self._logging_color = "bold_purple"
 
-        if inputs is not None:
-            self._inputs = inputs
-            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)
@@ -429,7 +345,8 @@ class Crew(BaseModel):
         if self.process == Process.sequential:
             result = self._run_sequential_process()
         elif self.process == Process.hierarchical:
-            result = self._run_hierarchical_process()
+            result, manager_metrics = self._run_hierarchical_process()  # type: ignore # Incompatible types in assignment (expression has type "str | dict[str, Any]", variable has type "str")
+            metrics.append(manager_metrics)
         else:
             raise NotImplementedError(
                 f"The process '{self.process}' is not implemented yet."
@@ -442,9 +359,11 @@ class Crew(BaseModel):
 
         return result
 
-    def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
+    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: List[CrewOutput] = []
+        results = []
 
         # Initialize the parent crew's usage metrics
         total_usage_metrics = {
@@ -468,11 +387,13 @@ class Crew(BaseModel):
         self.usage_metrics = total_usage_metrics
         return results
 
-    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = {}) -> CrewOutput:
+    async def kickoff_async(
+        self, inputs: Optional[Dict[str, Any]] = {}
+    ) -> Union[str, Dict]:
         """Asynchronous kickoff method to start the crew execution."""
         return await asyncio.to_thread(self.kickoff, inputs)
 
-    async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[CrewOutput]:
+    async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[Any]:
         crew_copies = [self.copy() for _ in inputs]
 
         async def run_crew(crew, input_data):
@@ -482,10 +403,6 @@ class Crew(BaseModel):
             asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
             for i in range(len(inputs))
         ]
-        tasks = [
-            asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
-            for i in range(len(inputs))
-        ]
 
         results = await asyncio.gather(*tasks)
 
@@ -502,66 +419,52 @@ class Crew(BaseModel):
 
         self.usage_metrics = total_usage_metrics
 
-        total_usage_metrics = {
-            "total_tokens": 0,
-            "prompt_tokens": 0,
-            "completion_tokens": 0,
-            "successful_requests": 0,
-        }
-        for crew in crew_copies:
-            if crew.usage_metrics:
-                for key in total_usage_metrics:
-                    total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
-
-        self.usage_metrics = total_usage_metrics
-
         return results
 
-    def _store_execution_log(
-        self,
-        task: Task,
-        output: TaskOutput,
-        task_index: int,
-        was_replayed: bool = False,
-    ):
-        if self._inputs:
-            inputs = self._inputs
-        else:
-            inputs = {}
-
-        log = ExecutionLog(
-            task_id=str(task.id),
-            expected_output=task.expected_output,
-            output={
-                "description": output.description,
-                "summary": output.summary,
-                "raw": output.raw,
-                "pydantic": output.pydantic,
-                "json_dict": output.json_dict,
-                "output_format": output.output_format,
-                "agent": output.agent,
-            },
-            task_index=task_index,
-            inputs=inputs,
-            was_replayed=was_replayed,
-        )
-        if task_index < len(self.execution_logs):
-            self.execution_logs[task_index] = log
-        else:
-            self.execution_logs.append(log)
-
-        TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).update(task_index, log)
-
-    def _run_sequential_process(self) -> CrewOutput:
+    def _run_sequential_process(self) -> str:
         """Executes tasks sequentially and returns the final output."""
-        return self._execute_tasks(self.tasks)
+        task_output = ""
 
-    def _run_hierarchical_process(self) -> CrewOutput:
+        for task in self.tasks:
+            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)  # 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")
+            self._logger.log(
+                "info", f"== Starting Task: {task.description}", color="bold_purple"
+            )
+
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=role, task=task.description, status="started"
+                )
+            output = task.execute(context=task_output)
+
+            if not task.async_execution:
+                task_output = output
+
+            role = task.agent.role if task.agent is not None else "None"
+            self._logger.log("debug", f"== [{role}] Task output: {task_output}\n\n")
+
+            if self.output_log_file:
+                self._file_handler.log(agent=role, task=task_output, status="completed")
+
+        self._finish_execution(task_output)
+
+        token_usage = self.calculate_usage_metrics()
+
+        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,
+    ) -> Tuple[Union[str, Dict[str, Any]], Dict[str, Any]]:
         """Creates and assigns a manager agent to make sure the crew completes the tasks."""
-        self._create_manager_agent()
-        return self._execute_tasks(self.tasks, self.manager_agent)
 
-    def _create_manager_agent(self):
         i18n = I18N(prompt_file=self.prompt_file)
         if self.manager_agent is not None:
             self.manager_agent.allow_delegation = True
@@ -580,199 +483,37 @@ class Crew(BaseModel):
             )
             self.manager_agent = manager
 
-    def _execute_tasks(
-        self,
-        tasks: List[Task],
-        manager: Optional[BaseAgent] = None,
-        start_index: Optional[int] = 0,
-        was_replayed: bool = False,
-    ) -> CrewOutput:
-        """Executes tasks sequentially and returns the final output.
+        task_output = ""
 
-        Args:
-            tasks (List[Task]): List of tasks to execute
-            manager (Optional[BaseAgent], optional): Manager agent to use for delegation. Defaults to None.
+        for task in self.tasks:
+            self._logger.log("debug", f"Working Agent: {manager.role}")
+            self._logger.log("info", f"Starting Task: {task.description}")
 
-        Returns:
-            CrewOutput: Final output of the crew
-        """
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=manager.role, task=task.description, status="started"
+                )
 
-        task_outputs: List[TaskOutput] = []
-        futures: List[Tuple[Task, Future[TaskOutput], int]] = []
-        last_sync_output: Optional[TaskOutput] = None
-
-        for task_index, task in enumerate(tasks):
-            if start_index is not None and task_index < start_index:
-                if task.output:
-                    if task.async_execution:
-                        task_outputs.append(task.output)
-                    else:
-                        task_outputs = [task.output]
-                        last_sync_output = task.output
-                continue
-
-            self._prepare_task(task, manager)
-            if self.process == Process.hierarchical:
-                agent_to_use = manager
+            if task.agent:
+                manager.tools = task.agent.get_delegation_tools([task.agent])
             else:
-                agent_to_use = task.agent
-            if agent_to_use is None:
-                raise ValueError(
-                    f"No agent available for task: {task.description}. Ensure that either the task has an assigned agent or a manager agent is provided."
-                )
-            self._log_task_start(task, agent_to_use)
-
-            if task.async_execution:
-                context = self._get_context(
-                    task, [last_sync_output] if last_sync_output else []
-                )
-                future = task.execute_async(
-                    agent=agent_to_use,
-                    context=context,
-                    tools=agent_to_use.tools,
-                )
-                futures.append((task, future, task_index))
-            else:
-                if futures:
-                    task_outputs.extend(
-                        self._process_async_tasks(futures, was_replayed)
-                    )
-                    futures.clear()
-
-                context = self._get_context(task, task_outputs)
-                task_output = task.execute_sync(
-                    agent=agent_to_use,
-                    context=context,
-                    tools=agent_to_use.tools,
-                )
-                task_outputs = [task_output]
-                self._process_task_result(task, task_output)
-                self._store_execution_log(task, task_output, task_index, was_replayed)
-
-        if futures:
-            task_outputs = self._process_async_tasks(futures, was_replayed)
-
-        return self._create_crew_output(task_outputs)
-
-    def _prepare_task(self, task: Task, manager: Optional[BaseAgent]):
-        if self.process == Process.hierarchical:
-            self._update_manager_tools(task, manager)
-        elif task.agent and task.agent.allow_delegation:
-            self._add_delegation_tools(task)
-
-    def _add_delegation_tools(self, task: Task):
-        agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
-        if len(self.agents) > 1 and agents_for_delegation:
-            task.tools += task.agent.get_delegation_tools(agents_for_delegation)  # type: ignore
-
-    def _log_task_start(self, task: Task, agent: Optional[BaseAgent]):
-        color = self._logging_color
-        role = agent.role if agent else "None"
-        self._logger.log("debug", f"== Working Agent: {role}", color=color)
-        self._logger.log("info", f"== Starting Task: {task.description}", color=color)
-        if self.output_log_file:
-            self._file_handler.log(agent=role, task=task.description, status="started")
-
-    def _update_manager_tools(self, task: Task, manager: Optional[BaseAgent]):
-        if task.agent and manager:
-            manager.tools = task.agent.get_delegation_tools([task.agent])
-        if manager:
-            manager.tools = manager.get_delegation_tools(self.agents)
-
-    def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
-        context = (
-            aggregate_raw_outputs_from_tasks(task.context)
-            if task.context
-            else aggregate_raw_outputs_from_task_outputs(task_outputs)
-        )
-        return context
-
-    def _process_task_result(self, task: Task, output: TaskOutput) -> None:
-        role = task.agent.role if task.agent is not None else "None"
-        self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
-        if self.output_log_file:
-            self._file_handler.log(agent=role, task=output, status="completed")
-
-    def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
-        if len(task_outputs) != 1:
-            raise ValueError(
-                "Something went wrong. Kickoff should return only one task output."
+                manager.tools = manager.get_delegation_tools(self.agents)
+            task_output = task.execute(
+                agent=manager, context=task_output, tools=manager.tools
             )
-        final_task_output = task_outputs[0]
-        final_string_output = final_task_output.raw
-        self._finish_execution(final_string_output)
+
+            self._logger.log("debug", f"[{manager.role}] Task output: {task_output}")
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=manager.role, task=task_output, status="completed"
+                )
+
+        self._finish_execution(task_output)
+
+        # type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
         token_usage = self.calculate_usage_metrics()
 
-        return CrewOutput(
-            raw=final_task_output.raw,
-            pydantic=final_task_output.pydantic,
-            json_dict=final_task_output.json_dict,
-            tasks_output=[task.output for task in self.tasks if task.output],
-            token_usage=token_usage,
-        )
-
-    def _process_async_tasks(
-        self,
-        futures: List[Tuple[Task, Future[TaskOutput], int]],
-        was_replayed: bool = False,
-    ) -> List[TaskOutput]:
-        task_outputs = []
-        for future_task, future, task_index in futures:
-            task_output = future.result()
-            task_outputs.append(task_output)
-            self._process_task_result(future_task, task_output)
-            self._store_execution_log(
-                future_task, task_output, task_index, was_replayed
-            )
-        return task_outputs
-
-    def _find_task_index(
-        self, task_id: str, stored_outputs: List[Any]
-    ) -> Optional[int]:
-        return next(
-            (
-                index
-                for (index, d) in enumerate(stored_outputs)
-                if d["task_id"] == str(task_id)
-            ),
-            None,
-        )
-
-    def replay_from_task(
-        self, task_id: str, inputs: Optional[Dict[str, Any]] = None
-    ) -> CrewOutput:
-        stored_outputs = TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).load()
-        start_index = self._find_task_index(task_id, stored_outputs)
-
-        if start_index is None:
-            raise ValueError(f"Task with id {task_id} not found in the crew's tasks.")
-
-        replay_inputs = (
-            inputs if inputs is not None else stored_outputs[start_index]["inputs"]
-        )
-        self._inputs = replay_inputs
-
-        if replay_inputs:
-            self._interpolate_inputs(replay_inputs)
-
-        if self.process == Process.hierarchical:
-            self._create_manager_agent()
-
-        for i in range(start_index):
-            stored_output = stored_outputs[i]["output"]
-            task_output = TaskOutput(
-                description=stored_output["description"],
-                agent=stored_output["agent"],
-                raw=stored_output["raw"],
-                pydantic=stored_output["pydantic"],
-                json_dict=stored_output["json_dict"],
-                output_format=stored_output["output_format"],
-            )
-            self.tasks[i].output = task_output
-
-        self._logging_color = "bold_blue"
-        result = self._execute_tasks(self.tasks, self.manager_agent, start_index, True)
-        return result
+        return self._format_output(task_output, token_usage), token_usage
 
     def copy(self):
         """Create a deep copy of the Crew."""
@@ -824,15 +565,31 @@ class Crew(BaseModel):
         for agent in self.agents:
             agent.interpolate_inputs(inputs)
 
-    def _finish_execution(self, final_string_output: str) -> None:
+    def _format_output(
+        self, output: str, token_usage: Optional[Dict[str, Any]] = None
+    ) -> Union[str, Dict[str, Any]]:
+        """
+        Formats the output of the crew execution.
+        If full_output is True, then returned data type will be a dictionary else returned outputs are string
+        """
+
+        if self.full_output:
+            return {  # type: ignore # Incompatible return value type (got "dict[str, Sequence[str | TaskOutput | None]]", expected "str")
+                "final_output": output,
+                "tasks_outputs": [task.output for task in self.tasks if task],
+                "usage_metrics": token_usage,
+            }
+        else:
+            return output
+
+    def _finish_execution(self, output) -> None:
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
         if agentops:
             agentops.end_session(
-                end_state="Success",
-                end_state_reason="Finished Execution",
+                end_state="Success", end_state_reason="Finished Execution"
             )
-        self._telemetry.end_crew(self, final_string_output)
+        self._telemetry.end_crew(self, output)
 
     def calculate_usage_metrics(self) -> Dict[str, int]:
         """Calculates and returns the usage metrics."""

src/crewai/crews/__init__.py

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

src/crewai/crews/crew_output.py

@@ -1,58 +0,0 @@
-import json
-from typing import Any, Dict, Optional
-
-from pydantic import BaseModel, Field
-
-from crewai.tasks.output_format import OutputFormat
-from crewai.tasks.task_output import TaskOutput
-
-
-class CrewOutput(BaseModel):
-    """Class that represents the result of a crew."""
-
-    raw: str = Field(description="Raw output of crew", default="")
-    pydantic: Optional[BaseModel] = Field(
-        description="Pydantic output of Crew", default=None
-    )
-    json_dict: Optional[Dict[str, Any]] = Field(
-        description="JSON dict output of Crew", default=None
-    )
-    tasks_output: list[TaskOutput] = Field(
-        description="Output of each task", default=[]
-    )
-    token_usage: Dict[str, Any] = Field(
-        description="Processed token summary", default={}
-    )
-
-    # @property
-    # def pydantic(self) -> Optional[BaseModel]:
-    #     # Check if the final task output included a pydantic model
-    #     if self.tasks_output[-1].output_format != OutputFormat.PYDANTIC:
-    #         raise ValueError(
-    #             "No pydantic model found in the final task. Please make sure to set the output_pydantic property in the final task in your crew."
-    #         )
-
-    #     return self._pydantic
-
-    @property
-    def json(self) -> Optional[str]:
-        if self.tasks_output[-1].output_format != OutputFormat.JSON:
-            raise ValueError(
-                "No JSON output found in the final task. Please make sure to set the output_json property in the final task in your crew."
-            )
-
-        return json.dumps(self.json_dict)
-
-    def to_dict(self) -> Dict[str, Any]:
-        if self.json_dict:
-            return self.json_dict
-        if self.pydantic:
-            return self.pydantic.model_dump()
-        raise ValueError("No output to convert to dictionary")
-
-    def __str__(self):
-        if self.pydantic:
-            return str(self.pydantic)
-        if self.json_dict:
-            return str(self.json_dict)
-        return self.raw

src/crewai/task.py

@@ -1,11 +1,9 @@
-import json
 import os
 import re
 import threading
 import uuid
-from concurrent.futures import Future
 from copy import copy
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, List, Optional, Type, Union
 
 from langchain_openai import ChatOpenAI
 from opentelemetry.trace import Span
@@ -13,10 +11,9 @@ from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
-from crewai.utilities.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
@@ -100,10 +97,6 @@ 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
@@ -164,75 +157,78 @@ class Task(BaseModel):
             )
         return self
 
-    def execute_sync(
-        self,
-        agent: Optional[BaseAgent] = None,
-        context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
-    ) -> TaskOutput:
-        """Execute the task synchronously."""
-        return self._execute_core(agent, context, tools)
+    def wait_for_completion(self) -> str | BaseModel:
+        """Wait for asynchronous task completion and return the output."""
+        assert self.async_execution, "Task is not set to be executed asynchronously."
 
-    def execute_async(
+        if self._thread:
+            self._thread.join()
+            self._thread = None
+
+        assert self.output, "Task output is not set."
+
+        return self.output.exported_output
+
+    def execute(  # type: ignore # Missing return statement
         self,
         agent: BaseAgent | None = None,
         context: Optional[str] = None,
         tools: Optional[List[Any]] = None,
-    ) -> Future[TaskOutput]:
-        """Execute the task asynchronously."""
-        future: Future[TaskOutput] = Future()
-        threading.Thread(
-            target=self._execute_task_async, args=(agent, context, tools, future)
-        ).start()
-        return future
+    ) -> str:
+        """Execute the task.
 
-    def _execute_task_async(
-        self,
-        agent: Optional[BaseAgent],
-        context: Optional[str],
-        tools: Optional[List[Any]],
-        future: Future[TaskOutput],
-    ) -> None:
-        """Execute the task asynchronously with context handling."""
-        result = self._execute_core(agent, context, tools)
-        future.set_result(result)
+        Returns:
+            Output of the task.
+        """
+
+        self._execution_span = self._telemetry.task_started(self)
 
-    def _execute_core(
-        self,
-        agent: Optional[BaseAgent],
-        context: Optional[str],
-        tools: Optional[List[Any]],
-    ) -> TaskOutput:
-        """Run the core execution logic of the task."""
-        self.agent = agent
         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."
             )
 
-        self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
+        if self.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:
+                    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 or []
+        tools = tools or self.tools
 
+        if self.async_execution:
+            self._thread = threading.Thread(
+                target=self._execute, args=(agent, self, context, tools)
+            )
+            self._thread.start()
+        else:
+            result = self._execute(
+                task=self,
+                agent=agent,
+                context=context,
+                tools=tools,
+            )
+            return result
+
+    def _execute(self, agent: "BaseAgent", task, context, tools):
         result = agent.execute_task(
-            task=self,
+            task=task,
             context=context,
             tools=tools,
         )
+        exported_output = self._export_output(result)
 
-        pydantic_output, json_output = self._export_output(result)
-
-        task_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,
-            raw=result,
-            pydantic=pydantic_output,
-            json_dict=json_output,
+            exported_output=exported_output,
+            raw_output=result,
             agent=agent.role,
-            output_format=self._get_output_format(),
         )
-        self.output = task_output
 
         if self.callback:
             self.callback(self.output)
@@ -241,17 +237,7 @@ class Task(BaseModel):
             self._telemetry.task_ended(self._execution_span, self)
             self._execution_span = None
 
-        if self.output_file:
-            content = (
-                json_output
-                if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
-            )
-            self._save_file(content)
-
-        return task_output
+        return exported_output
 
     def prompt(self) -> str:
         """Prompt the task.
@@ -286,7 +272,7 @@ class Task(BaseModel):
         """Increment the delegations counter."""
         self.delegations += 1
 
-    def copy(self, agents: List["BaseAgent"]) -> "Task":
+    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",
@@ -299,11 +285,11 @@ 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]:
-            return next((agent for agent in agents if agent.role == role), 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 []
@@ -317,113 +303,62 @@ class Task(BaseModel):
 
         return copied_task
 
-    def _create_converter(self, *args, **kwargs) -> Converter:
-        """Create a converter instance."""
-        converter = self.agent.get_output_converter(*args, **kwargs)
-        if self.converter_cls:
-            converter = self.converter_cls(*args, **kwargs)
-        return converter
-
-    def _export_output(
-        self, result: str
-    ) -> Tuple[Optional[BaseModel], Optional[Dict[str, Any]]]:
-        pydantic_output: Optional[BaseModel] = None
-        json_output: Optional[Dict[str, Any]] = None
+    def _export_output(self, result: str) -> Any:
+        exported_result = result
+        instructions = "I'm gonna convert this raw text into valid JSON."
 
         if self.output_pydantic or self.output_json:
-            model_output = self._convert_to_model(result)
-            pydantic_output = (
-                model_output if isinstance(model_output, BaseModel) else None
-            )
-            if isinstance(model_output, str):
-                try:
-                    json_output = json.loads(model_output)
-                except json.JSONDecodeError:
-                    json_output = None
-            else:
-                json_output = model_output if isinstance(model_output, dict) else None
+            model = self.output_pydantic or self.output_json
 
-        return pydantic_output, json_output
-
-    def _convert_to_model(self, result: str) -> Union[dict, BaseModel, str]:
-        model = self.output_pydantic or self.output_json
-        if model is None:
-            return result
-
-        try:
-            return self._validate_model(result, model)
-        except Exception:
-            return self._handle_partial_json(result, model)
-
-    def _validate_model(
-        self, result: str, model: Type[BaseModel]
-    ) -> Union[dict, BaseModel]:
-        exported_result = model.model_validate_json(result)
-        if self.output_json:
-            return exported_result.model_dump()
-        return exported_result
-
-    def _handle_partial_json(
-        self, result: str, model: Type[BaseModel]
-    ) -> Union[dict, BaseModel, str]:
-        match = re.search(r"({.*})", result, re.DOTALL)
-        if match:
+            # try to convert task_output directly to pydantic/json
             try:
-                exported_result = model.model_validate_json(match.group(0))
+                exported_result = model.model_validate_json(result)  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
                 if self.output_json:
-                    return exported_result.model_dump()
+                    return exported_result.model_dump()  # type: ignore # "str" has no attribute "model_dump"
                 return exported_result
             except Exception:
-                pass
+                # sometimes the response contains valid JSON in the middle of text
+                match = re.search(r"({.*})", result, re.DOTALL)
+                if match:
+                    try:
+                        exported_result = model.model_validate_json(match.group(0))  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
+                        if self.output_json:
+                            return exported_result.model_dump()  # type: ignore # "str" has no attribute "model_dump"
+                        return exported_result
+                    except Exception:
+                        pass
 
-        return self._convert_with_instructions(result, model)
+            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"
+            if not self._is_gpt(llm):
+                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}"
 
-    def _convert_with_instructions(
-        self, result: str, model: Type[BaseModel]
-    ) -> Union[dict, BaseModel, str]:
-        llm = self.agent.function_calling_llm or self.agent.llm  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
-        instructions = self._get_conversion_instructions(model, llm)
-
-        converter = self._create_converter(
-            llm=llm, text=result, model=model, instructions=instructions
-        )
-        exported_result = (
-            converter.to_pydantic() if self.output_pydantic else converter.to_json()
-        )
-
-        if isinstance(exported_result, ConverterError):
-            Printer().print(
-                content=f"{exported_result.message} Using raw output instead.",
-                color="red",
+            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
             )
-            return result
+
+            if self.output_pydantic:
+                exported_result = converter.to_pydantic()
+            elif self.output_json:
+                exported_result = converter.to_json()
+
+            if isinstance(exported_result, ConverterError):
+                Printer().print(
+                    content=f"{exported_result.message} Using raw output instead.",
+                    color="red",
+                )
+                exported_result = result
+
+        if self.output_file:
+            content = (
+                exported_result
+                if not self.output_pydantic
+                else exported_result.model_dump_json()  # type: ignore # "str" has no attribute "json"
+            )
+            self._save_file(content)
 
         return exported_result
 
-    def _get_output_format(self) -> OutputFormat:
-        if self.output_json:
-            return OutputFormat.JSON
-        if self.output_pydantic:
-            return OutputFormat.PYDANTIC
-        return OutputFormat.RAW
-
-    def _get_conversion_instructions(self, model: Type[BaseModel], llm: Any) -> str:
-        instructions = "I'm gonna convert this raw text into valid JSON."
-        if not self._is_gpt(llm):
-            model_schema = PydanticSchemaParser(model=model).get_schema()
-            instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
-        return instructions
-
-    def _save_output(self, content: str) -> None:
-        if not self.output_file:
-            raise Exception("Output file path is not set.")
-
-        directory = os.path.dirname(self.output_file)
-        if directory and not os.path.exists(directory):
-            os.makedirs(directory)
-        with open(self.output_file, "w", encoding="utf-8") as file:
-            file.write(content)
-
     def _is_gpt(self, llm) -> bool:
         return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
 

src/crewai/tasks/__init__.py

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

src/crewai/tasks/output_format.py

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

src/crewai/tasks/task_output.py

@@ -1,74 +1,24 @@
-import json
-from typing import Any, Dict, Optional
+from typing import Optional, Union
 
 from pydantic import BaseModel, Field, model_validator
 
-from crewai.tasks.output_format import OutputFormat
-
 
 class TaskOutput(BaseModel):
     """Class that represents the result of a task."""
 
     description: str = Field(description="Description of the task")
     summary: Optional[str] = Field(description="Summary of the task", default=None)
-    raw: str = Field(description="Raw output of the task", default="")
-    pydantic: Optional[BaseModel] = Field(
-        description="Pydantic output of task", default=None
-    )
-    json_dict: Optional[Dict[str, Any]] = Field(
-        description="JSON dictionary of task", default=None
+    exported_output: Union[str, BaseModel] = Field(
+        description="Output of the task", default=None
     )
     agent: str = Field(description="Agent that executed the task")
-    output_format: OutputFormat = Field(
-        description="Output format of the task", default=OutputFormat.RAW
-    )
+    raw_output: str = Field(description="Result of the task")
 
     @model_validator(mode="after")
     def set_summary(self):
-        """Set the summary field based on the description."""
         excerpt = " ".join(self.description.split(" ")[:10])
         self.summary = f"{excerpt}..."
         return self
 
-    # @property
-    # def pydantic(self) -> Optional[BaseModel]:
-    #     # Check if the final task output included a pydantic model
-    #     if self.output_format != OutputFormat.PYDANTIC:
-    #         raise ValueError(
-    #             """
-    #             Invalid output format requested.
-    #             If you would like to access the pydantic model,
-    #             please make sure to set the output_pydantic property for the task.
-    #             """
-    #         )
-
-    #     return self._pydantic
-
-    @property
-    def json(self) -> Optional[str]:
-        if self.output_format != OutputFormat.JSON:
-            raise ValueError(
-                """
-                Invalid output format requested.
-                If you would like to access the JSON output,
-                please make sure to set the output_json property for the task
-                """
-            )
-
-        return json.dumps(self.json_dict)
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert json_output and pydantic_output to a dictionary."""
-        output_dict = {}
-        if self.json_dict:
-            output_dict.update(self.json_dict)
-        if self.pydantic:
-            output_dict.update(self.pydantic.model_dump())
-        return output_dict
-
-    def __str__(self) -> str:
-        if self.pydantic:
-            return str(self.pydantic)
-        if self.json_dict:
-            return str(self.json_dict)
-        return self.raw
+    def result(self):
+        return self.exported_output

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:
@@ -337,7 +320,7 @@ class Telemetry:
             except Exception:
                 pass
 
-    def end_crew(self, crew, final_string_output):
+    def end_crew(self, crew, output):
         if (self.ready) and (crew.share_crew):
             try:
                 self._add_attribute(
@@ -345,9 +328,7 @@ class Telemetry:
                     "crewai_version",
                     pkg_resources.get_distribution("crewai").version,
                 )
-                self._add_attribute(
-                    crew._execution_span, "crew_output", final_string_output
-                )
+                self._add_attribute(crew._execution_span, "crew_output", output)
                 self._add_attribute(
                     crew._execution_span,
                     "crew_tasks_output",

src/crewai/tools/tool_calling.py

@@ -8,7 +8,7 @@ from pydantic.v1 import BaseModel, Field
 class ToolCalling(BaseModel):
     tool_name: str = Field(..., description="The name of the tool to be called.")
     arguments: Optional[Dict[str, Any]] = Field(
-        ..., description="A 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/constants.py

@@ -1,3 +1,2 @@
 TRAINING_DATA_FILE = "training_data.pkl"
 TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
-CREW_TASKS_OUTPUT_FILE = "crew_tasks_output.json"

src/crewai/utilities/crew_json_encoder.py

@@ -1,31 +0,0 @@
-from datetime import datetime
-import json
-from uuid import UUID
-from pydantic import BaseModel
-
-
-class CrewJSONEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, BaseModel):
-            return self._handle_pydantic_model(obj)
-        elif isinstance(obj, UUID):
-            return str(obj)
-
-        elif isinstance(obj, datetime):
-            return obj.isoformat()
-        return super().default(obj)
-
-    def _handle_pydantic_model(self, obj):
-        try:
-            data = obj.model_dump()
-            # Remove circular references
-            for key, value in data.items():
-                if isinstance(value, BaseModel):
-                    data[key] = str(
-                        value
-                    )  # Convert nested models to string representation
-            return data
-        except RecursionError:
-            return str(
-                obj
-            )  # Fall back to string representation if circular reference is detected

src/crewai/utilities/file_handler.py

@@ -1,7 +1,5 @@
 import os
 import pickle
-
-
 from datetime import datetime
 
 

src/crewai/utilities/formatter.py

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

src/crewai/utilities/printer.py

@@ -8,8 +8,6 @@ class Printer:
             self._print_bold_green(content)
         elif color == "bold_purple":
             self._print_bold_purple(content)
-        elif color == "bold_blue":
-            self._print_bold_blue(content)
         else:
             print(content)
 
@@ -24,6 +22,3 @@ class Printer:
 
     def _print_red(self, content):
         print("\033[91m {}\033[00m".format(content))
-
-    def _print_bold_blue(self, content):
-        print("\033[1m\033[94m {}\033[00m".format(content))

src/crewai/utilities/task_output_handler.py

@@ -1,69 +0,0 @@
-import json
-import os
-
-from pydantic import BaseModel, Field
-from datetime import datetime
-from typing import Dict, Any, Optional, List
-from crewai.utilities.crew_json_encoder import CrewJSONEncoder
-
-
-class ExecutionLog(BaseModel):
-    task_id: str
-    expected_output: Optional[str] = None
-    output: Dict[str, Any]
-    timestamp: datetime = Field(default_factory=datetime.now)
-    task_index: int
-    inputs: Dict[str, Any] = Field(default_factory=dict)
-    was_replayed: bool = False
-
-    def __getitem__(self, key: str) -> Any:
-        return getattr(self, key)
-
-
-class TaskOutputJsonHandler:
-    def __init__(self, file_name: str) -> None:
-        self.file_path = os.path.join(os.getcwd(), file_name)
-
-    def initialize_file(self) -> None:
-        if not os.path.exists(self.file_path) or os.path.getsize(self.file_path) == 0:
-            with open(self.file_path, "w") as file:
-                json.dump([], file)
-
-    def update(self, task_index: int, log: ExecutionLog):
-        logs = self.load()
-        if task_index < len(logs):
-            logs[task_index] = log
-        else:
-            logs.append(log)
-        self.save(logs)
-
-    def save(self, logs: List[ExecutionLog]):
-        with open(self.file_path, "w") as file:
-            json.dump(logs, file, indent=2, cls=CrewJSONEncoder)
-
-    def reset(self):
-        """Reset the JSON file by creating an empty file."""
-        with open(self.file_path, "w") as f:
-            json.dump([], f)
-
-    def load(self) -> List[ExecutionLog]:
-        try:
-            if (
-                not os.path.exists(self.file_path)
-                or os.path.getsize(self.file_path) == 0
-            ):
-                return []
-
-            with open(self.file_path, "r") as file:
-                return json.load(file)
-        except FileNotFoundError:
-            print(f"File {self.file_path} not found. Returning empty list.")
-            return []
-        except json.JSONDecodeError:
-            print(
-                f"Error decoding JSON from file {self.file_path}. Returning empty list."
-            )
-            return []
-        except Exception as e:
-            print(f"An unexpected error occurred: {e}")
-            return []

tests/agent_test.py

@@ -631,9 +631,8 @@ def test_agent_use_specific_tasks_output_as_context(capsys):
 
     crew = Crew(agents=[agent1, agent2], tasks=tasks)
     result = crew.kickoff()
-    print("LOWER RESULT", result.raw)
-    assert "bye" not in result.raw.lower()
-    assert "hi" in result.raw.lower() or "hello" in result.raw.lower()
+    assert "bye" not in result.lower()
+    assert "hi" in result.lower() or "hello" in result.lower()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -645,7 +644,7 @@ def test_agent_step_callback():
     with patch.object(StepCallback, "callback") as callback:
 
         @tool
-        def learn_about_AI(topic) -> str:
+        def learn_about_AI(topic) -> float:
             """Useful for when you need to learn about AI to write an paragraph about it."""
             return "AI is a very broad field."
 
@@ -679,7 +678,7 @@ def test_agent_function_calling_llm():
     with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
 
         @tool
-        def learn_about_AI(topic) -> str:
+        def learn_about_AI(topic) -> float:
             """Useful for when you need to learn about AI to write an paragraph about it."""
             return "AI is a very broad field."
 
@@ -751,8 +750,7 @@ def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
     crew = Crew(agents=[agent1], tasks=tasks)
 
     result = crew.kickoff()
-    print("RESULT: ", result.raw)
-    assert result.raw == "Howdy!"
+    assert result == "Howdy!"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

tests/cassettes/test_crew_async_kickoff.yaml

@@ -1,591 +0,0 @@
-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-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"Thought"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
-        I"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
-        now"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
-        can"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
-        give"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
-        a"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
-        great"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
-        answer"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"\n"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-9gdIXYXcf53VmxfiC6Q2NBDG2bPci","object":"chat.completion.chunk","created":1719947865,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"Final"},"logprobs":null,"finish_reason":null}]}
-
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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():
@@ -81,7 +80,7 @@ def test_task_prompt_includes_expected_output():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute_sync(agent=researcher)
+        task.execute()
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -104,13 +103,11 @@ def test_task_callback():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute_sync(agent=researcher)
+        task.execute()
         task_completed.assert_called_once_with(task.output)
 
 
 def test_task_callback_returns_task_ouput():
-    from crewai.tasks.output_format import OutputFormat
-
     researcher = Agent(
         role="Researcher",
         goal="Make the best research and analysis on content about AI and AI agents",
@@ -129,7 +126,7 @@ def test_task_callback_returns_task_ouput():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "exported_ok"
-        task.execute_sync(agent=researcher)
+        task.execute()
         # Ensure the callback is called with a TaskOutput object serialized to JSON
         task_completed.assert_called_once()
         callback_data = task_completed.call_args[0][0]
@@ -142,12 +139,10 @@ def test_task_callback_returns_task_ouput():
         output_dict = json.loads(callback_data)
         expected_output = {
             "description": task.description,
-            "raw": "exported_ok",
-            "pydantic": None,
-            "json_dict": None,
+            "exported_output": "exported_ok",
+            "raw_output": "exported_ok",
             "agent": researcher.role,
             "summary": "Give me a list of 5 interesting ideas to explore...",
-            "output_format": OutputFormat.RAW,
         }
         assert output_dict == expected_output
 
@@ -166,7 +161,7 @@ def test_execute_with_agent():
     )
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
-        task.execute_sync(agent=researcher)
+        task.execute(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -186,7 +181,7 @@ def test_async_execution():
     )
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
-        task.execute_async(agent=researcher)
+        task.execute(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -204,7 +199,7 @@ def test_multiple_output_type_error():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_pydantic_sequential():
+def test_output_pydantic():
     class ScoreOutput(BaseModel):
         score: int
 
@@ -222,46 +217,13 @@ def test_output_pydantic_sequential():
         agent=scorer,
     )
 
-    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
+    crew = Crew(agents=[scorer], tasks=[task])
     result = crew.kickoff()
-    assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
+    assert isinstance(result, ScoreOutput)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_pydantic_hierarchical():
-    from langchain_openai import ChatOpenAI
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_pydantic=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(
-        agents=[scorer],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4o"),
-    )
-    result = crew.kickoff()
-    assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_sequential():
+def test_output_json():
     class ScoreOutput(BaseModel):
         score: int
 
@@ -279,126 +241,9 @@ def test_output_json_sequential():
         agent=scorer,
     )
 
-    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
+    crew = Crew(agents=[scorer], tasks=[task])
     result = crew.kickoff()
-    assert '{"score": 4}' == result.json
-    assert result.to_dict() == {"score": 4}
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_hierarchical():
-    from langchain_openai import ChatOpenAI
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(
-        agents=[scorer],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4o"),
-    )
-    result = crew.kickoff()
-    assert '{"score": 4}' == result.json
-    assert result.to_dict() == {"score": 4}
-
-
-def test_json_property_without_output_json():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_pydantic=ScoreOutput,  # Using output_pydantic instead of output_json
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
-    result = crew.kickoff()
-
-    with pytest.raises(ValueError) as excinfo:
-        _ = result.json  # Attempt to access the json property
-
-    assert "No JSON output found in the final task." in str(excinfo.value)
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_dict_sequential():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
-    result = crew.kickoff()
-    assert {"score": 4} == result.json_dict
-    assert result.to_dict() == {"score": 4}
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_dict_hierarchical():
-    from langchain_openai import ChatOpenAI
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(
-        agents=[scorer],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4o"),
-    )
-    result = crew.kickoff()
-    assert {"score": 4} == result.json_dict
-    assert result.to_dict() == {"score": 4}
+    assert '{\n  "score": 4\n}' == result
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -434,11 +279,7 @@ def test_output_pydantic_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2], verbose=2)
     result = crew.kickoff()
-    pydantic_result = result.pydantic
-    assert isinstance(
-        pydantic_result, ScoreOutput
-    ), "Expected pydantic result to be of type ScoreOutput"
-    assert 5 == pydantic_result.score
+    assert 5 == result.score
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -469,7 +310,7 @@ def test_output_json_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2])
     result = crew.kickoff()
-    assert '{"score": 5}' == result.json
+    assert '{\n  "score": 5\n}' == result
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -521,7 +362,7 @@ def test_save_task_json_output():
     with patch.object(Task, "_save_file") as save_file:
         save_file.return_value = None
         crew.kickoff()
-        save_file.assert_called_once_with({"score": 4})
+        save_file.assert_called_once_with('{\n  "score": 4\n}')
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -552,38 +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
@@ -714,80 +523,3 @@ def test_interpolate_inputs():
         == "Give me a list of 5 interesting ideas about ML to explore for an article, what makes them unique and interesting."
     )
     assert task.expected_output == "Bullet point list of 5 interesting ideas about ML."
-
-
-def test_task_output_str_with_pydantic():
-    from crewai.tasks.output_format import OutputFormat
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    score_output = ScoreOutput(score=4)
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        pydantic=score_output,
-        output_format=OutputFormat.PYDANTIC,
-    )
-
-    assert str(task_output) == str(score_output)
-
-
-def test_task_output_str_with_json_dict():
-    from crewai.tasks.output_format import OutputFormat
-
-    json_dict = {"score": 4}
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        json_dict=json_dict,
-        output_format=OutputFormat.JSON,
-    )
-
-    assert str(task_output) == str(json_dict)
-
-
-def test_task_output_str_with_raw():
-    from crewai.tasks.output_format import OutputFormat
-
-    raw_output = "Raw task output"
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        raw=raw_output,
-        output_format=OutputFormat.RAW,
-    )
-
-    assert str(task_output) == raw_output
-
-
-def test_task_output_str_with_pydantic_and_json_dict():
-    from crewai.tasks.output_format import OutputFormat
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    score_output = ScoreOutput(score=4)
-    json_dict = {"score": 4}
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        pydantic=score_output,
-        json_dict=json_dict,
-        output_format=OutputFormat.PYDANTIC,
-    )
-
-    # When both pydantic and json_dict are present, pydantic should take precedence
-    assert str(task_output) == str(score_output)
-
-
-def test_task_output_str_with_none():
-    from crewai.tasks.output_format import OutputFormat
-
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        output_format=OutputFormat.RAW,
-    )
-
-    assert str(task_output) == ""