added validator for async_execution true in tasks whenever in hierarchical run

.github/workflows/tests.yml

@@ -19,7 +19,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.11.9"
+          python-version: "3.10"
 
       - name: Install Requirements
         run: |
@@ -28,4 +28,4 @@ jobs:
           poetry install
 
       - name: Run tests
-        run: poetry run pytest
+        run: poetry run pytest tests

.gitignore

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

docs/core-concepts/Crews.md

@@ -4,38 +4,36 @@ description: Understanding and utilizing crews in the crewAI framework with comp
 ---
 
 ## What is a Crew?
-
 A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
 
 ## Crew Attributes
 
-| Attribute                             | Parameters             | Description                                                                                                                                                                                                                                               |
-| :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Tasks**                             | `tasks`                | A list of tasks assigned to the crew.                                                                                                                                                                                                                     |
-| **Agents**                            | `agents`               | A list of agents that are part of the crew.                                                                                                                                                                                                               |
-| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows.                                                                                                                                                                                       |
-| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution.                                                                                                                                                                                                         |
-| **Manager LLM** _(optional)_          | `manager_llm`          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.**                                                                                                                                   |
-| **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
-| **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
-| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution.                                                                                                                                                                                         |
-| **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
-| **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
-| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
-| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution.                                                                                                                                                                             |
-| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                                                                                                                                                                     |
-| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output.                                                                                                                                                           |
-| **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
-| **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
-| **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
-| **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
-| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
-| **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
-| **Planning** *(optional)*             | `planning`             |  Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.
+| Attribute                   | Parameters          | Description                                                                                              |
+| :-------------------------- | :------------------ | :------------------------------------------------------------------------------------------------------- |
+| **Tasks**                   | `tasks`               | A list of tasks assigned to the crew.                                                                    |
+| **Agents**                  | `agents`              | A list of agents that are part of the crew.                                                              |
+| **Process** *(optional)*    | `process`             | The process flow (e.g., sequential, hierarchical) the crew follows.                                      |
+| **Verbose** *(optional)*    | `verbose`             | The verbosity level for logging during execution.                                                        |
+| **Manager LLM** *(optional)*| `manager_llm`         | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
+| **Function Calling LLM** *(optional)* | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
+| **Config** *(optional)*     | `config`              | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                       |
+| **Max RPM** *(optional)*    | `max_rpm`             | Maximum requests per minute the crew adheres to during execution.                                         |
+| **Language**  *(optional)*  | `language`            | Language used for the crew, defaults to English.                                                          |
+| **Language File** *(optional)* | `language_file`       | Path to the language file to be used for the crew.                                                        |
+| **Memory** *(optional)*     | `memory`              | Utilized for storing execution memories (short-term, long-term, entity memory).                           |
+| **Cache** *(optional)*      | `cache`               | Specifies whether to use a cache for storing the results of tools' execution.                             |
+| **Embedder** *(optional)*   | `embedder`            | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                     |
+| **Full Output** *(optional)*| `full_output`         | Whether the crew should return the full output with all tasks outputs or just the final output.           |
+| **Step Callback** *(optional)* | `step_callback`       | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
+| **Task Callback** *(optional)* | `task_callback`       | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
+| **Share Crew** *(optional)* | `share_crew`          | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
+| **Output Log File** *(optional)* | `output_log_file`    | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
+| **Manager Agent** *(optional)* | `manager_agent`      | `manager` sets a custom agent that will be used as a manager.                                             |
+| **Manager Callbacks** *(optional)* | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
+| **Prompt File** *(optional)* | `prompt_file`         | Path to the prompt JSON file to be used for the crew.                                                     |
 
 !!! note "Crew Max RPM"
-The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
+    The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
 
 ## Creating a Crew
 
@@ -46,12 +44,6 @@ When assembling a crew, you combine agents with complementary roles and tools, a
 ```python
 from crewai import Crew, Agent, Task, Process
 from langchain_community.tools import DuckDuckGoSearchRun
-from crewai_tools import tool
-
-@tool('DuckDuckGoSearch')
-def search(search_query: str):
-    """Search the web for information on a given topic"""
-    return DuckDuckGoSearchRun().run(search_query)
 
 # Define agents with specific roles and tools
 researcher = Agent(
@@ -62,7 +54,7 @@ researcher = Agent(
         to the business.
         You're currently working on a project to analyze the
         trends and innovations in the space of artificial intelligence.""",
-    tools=[search]
+    tools=[DuckDuckGoSearchRun()]
 )
 
 writer = Agent(
@@ -97,57 +89,6 @@ my_crew = Crew(
 )
 ```
 
-## Crew Output
-
-!!! note "Understanding Crew Outputs"
-The output of a crew in the crewAI framework is encapsulated within the `CrewOutput` class.
-This class provides a structured way to access results of the crew's execution, including various formats such as raw strings, JSON, and Pydantic models.
-The `CrewOutput` includes the results from the final task output, token usage, and individual task outputs.
-
-### Crew Output Attributes
-
-| Attribute        | Parameters     | Type                       | Description                                                                                          |
-| :--------------- | :------------- | :------------------------- | :--------------------------------------------------------------------------------------------------- |
-| **Raw**          | `raw`          | `str`                      | The raw output of the crew. This is the default format for the output.                               |
-| **Pydantic**     | `pydantic`     | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the crew.                              |
-| **JSON Dict**    | `json_dict`    | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the crew.                                               |
-| **Tasks Output** | `tasks_output` | `List[TaskOutput]`         | A list of `TaskOutput` objects, each representing the output of a task in the crew.                  |
-| **Token Usage**  | `token_usage`  | `Dict[str, Any]`           | A summary of token usage, providing insights into the language model's performance during execution. |
-
-### Crew Output Methods and Properties
-
-| Method/Property | Description                                                                                       |
-| :-------------- | :------------------------------------------------------------------------------------------------ |
-| **json**        | Returns the JSON string representation of the crew output if the output format is JSON.           |
-| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
-| \***\*str\*\*** | Returns the string representation of the crew output, prioritizing Pydantic, then JSON, then raw. |
-
-### Accessing Crew Outputs
-
-Once a crew has been executed, its output can be accessed through the `output` attribute of the `Crew` object. The `CrewOutput` class provides various ways to interact with and present this output.
-
-#### Example
-
-```python
-# Example crew execution
-crew = Crew(
-    agents=[research_agent, writer_agent],
-    tasks=[research_task, write_article_task],
-    verbose=2
-)
-
-result = crew.kickoff()
-
-# Accessing the crew output
-print(f"Raw Output: {crew_output.raw}")
-if crew_output.json_dict:
-    print(f"JSON Output: {json.dumps(crew_output.json_dict, indent=2)}")
-if crew_output.pydantic:
-    print(f"Pydantic Output: {crew_output.pydantic}")
-print(f"Tasks Output: {crew_output.tasks_output}")
-print(f"Token Usage: {crew_output.token_usage}")
-```
-
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
@@ -182,7 +123,7 @@ result = my_crew.kickoff()
 print(result)
 ```
 
-### Different ways to Kicking Off a Crew
+### Different wayt to Kicking Off a Crew
 
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 
@@ -215,32 +156,3 @@ for async_result in async_results:
 ```
 
 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
-
-
-### Replaying from specific task:
-You can now replay from a specific task using our cli command replay.
-
-The replay 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_id` for the replay process.
-
-Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
-
-
-### 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:
-
-To view latest kickoff task_ids use:
-
-```shell
-crewai log-tasks-outputs
-```
-
-
-```shell
-crewai replay -t <task_id>
-```
-
-These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

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. |
 
@@ -29,11 +29,6 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
 When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
 By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
 
-The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.  
-The **Long-Term Memory** uses SQLLite3 to store task results.  Currently, there is no way to override these storage implementations.
-The data storage files are saved into a platform specific location found using the appdirs package 
-and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
-
 ### Example: Configuring Memory for a Crew
 
 ```python
@@ -166,43 +161,10 @@ my_crew = Crew(
 )
 ```
 
-### Resetting Memory
-```sh
-crewai reset_memories [OPTIONS]
-```
-
-#### Resetting Memory Options
-- **`-l, --long`**
-  - **Description:** Reset LONG TERM memory.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-- **`-s, --short`**
-  - **Description:** Reset SHORT TERM memory.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-- **`-e, --entities`**
-  - **Description:** Reset ENTITIES memory.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-- **`-k, --kickoff-outputs`**
-  - **Description:** Reset LATEST KICKOFF TASK OUTPUTS.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-- **`-a, --all`**
-  - **Description:** Reset ALL memories.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-
-
 ## Benefits of Using crewAI's Memory System
 - **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
 - **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
 - **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
 
 ## Getting Started
-Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.
+Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/core-concepts/Planning.md

@@ -1,119 +0,0 @@
----
-title: crewAI Planning
-description: Learn how to add planning to your crewAI Crew and improve their performance.
----
-
-## Introduction
-The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
-
-### Using the Planning Feature
-Getting started with the planning feature is very easy, the only step required is to add `planning=True` to your Crew:
-
-```python
-from crewai import Crew, Agent, Task, Process
-
-# Assemble your crew with planning capabilities
-my_crew = Crew(
-    agents=self.agents,
-    tasks=self.tasks,
-    process=Process.sequential,
-    planning=True,
-)
-```
-
-From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
-
-### Example
-
-When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
-
-```bash
-
-[2024-07-15 16:49:11][INFO]: Planning the crew execution
-**Step-by-Step Plan for Task Execution**
-
-**Task Number 1: Conduct a thorough research about AI LLMs**
-
-**Agent:** AI LLMs Senior Data Researcher
-
-**Agent Goal:** Uncover cutting-edge developments in AI LLMs
-
-**Task Expected Output:** A list with 10 bullet points of the most relevant information about AI LLMs
-
-**Task Tools:** None specified
-
-**Agent Tools:** None specified
-
-**Step-by-Step Plan:**
-
-1. **Define Research Scope:**
-   - Determine the specific areas of AI LLMs to focus on, such as advancements in architecture, use cases, ethical considerations, and performance metrics.
-
-2. **Identify Reliable Sources:**
-   - List reputable sources for AI research, including academic journals, industry reports, conferences (e.g., NeurIPS, ACL), AI research labs (e.g., OpenAI, Google AI), and online databases (e.g., IEEE Xplore, arXiv).
-
-3. **Collect Data:**
-   - Search for the latest papers, articles, and reports published in 2023 and early 2024.
-   - Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
-
-4. **Analyze Findings:**
-   - Read and summarize the key points from each source.
-   - Highlight new techniques, models, and applications introduced in the past year.
-
-5. **Organize Information:**
-   - Categorize the information into relevant topics (e.g., new architectures, ethical implications, real-world applications).
-   - Ensure each bullet point is concise but informative.
-
-6. **Create the List:**
-   - Compile the 10 most relevant pieces of information into a bullet point list.
-   - Review the list to ensure clarity and relevance.
-
-**Expected Output:**
-A list with 10 bullet points of the most relevant information about AI LLMs.
-
----
-
-**Task Number 2: Review the context you got and expand each topic into a full section for a report**
-
-**Agent:** AI LLMs Reporting Analyst
-
-**Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings
-
-**Task Expected Output:** A fully fledge report with the main topics, each with a full section of information. Formatted as markdown without '```'
-
-**Task Tools:** None specified
-
-**Agent Tools:** None specified
-
-**Step-by-Step Plan:**
-
-1. **Review the Bullet Points:**
-   - Carefully read through the list of 10 bullet points provided by the AI LLMs Senior Data Researcher.
-
-2. **Outline the Report:**
-   - Create an outline with each bullet point as a main section heading.
-   - Plan sub-sections under each main heading to cover different aspects of the topic.
-
-3. **Research Further Details:**
-   - For each bullet point, conduct additional research if necessary to gather more detailed information.
-   - Look for case studies, examples, and statistical data to support each section.
-
-4. **Write Detailed Sections:**
-   - Expand each bullet point into a comprehensive section.
-   - Ensure each section includes an introduction, detailed explanation, examples, and a conclusion.
-   - Use markdown formatting for headings, subheadings, lists, and emphasis.
-
-5. **Review and Edit:**
-   - Proofread the report for clarity, coherence, and correctness.
-   - Make sure the report flows logically from one section to the next.
-   - Format the report according to markdown standards.
-
-6. **Finalize the Report:**
-   - Ensure the report is complete with all sections expanded and detailed.
-   - Double-check formatting and make any necessary adjustments.
-
-**Expected Output:**
-A fully-fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
-
----
-```

docs/core-concepts/Tasks.md

@@ -4,29 +4,27 @@ description: Detailed guide on managing and creating tasks within the crewAI fra
 ---
 
 ## Overview of a Task
-
 !!! note "What is a Task?"
-In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
+    In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
 
 Tasks within crewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
 ## Task Attributes
 
-| Attribute                        | Parameters        | Description                                                                                                          |
-| :------------------------------- | :---------------- | :------------------------------------------------------------------------------------------------------------------- |
-| **Description**                  | `description`     | A clear, concise statement of what the task entails.                                                                 |
-| **Agent**                        | `agent`           | The agent responsible for the task, assigned either directly or by the crew's process.                               |
-| **Expected Output**              | `expected_output` | A detailed description of what the task's completion looks like.                                                     |
-| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task.                                             |
-| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion.                       |
-| **Context** _(optional)_         | `context`         | Specifies tasks whose outputs are used as context for this task.                                                     |
-| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization.                   |
-| **Output JSON** _(optional)_     | `output_json`     | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
-| **Output Pydantic** _(optional)_ | `output_pydantic` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
-| **Output File** _(optional)_     | `output_file`     | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Output** _(optional)_          | `output`          | The output of the task, containing the raw, JSON, and Pydantic output plus additional details.                       |
-| **Callback** _(optional)_        | `callback`        | A Python callable that is executed with the task's output upon completion.                                           |
-| **Human Input** _(optional)_     | `human_input`     | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight.                  |
+| Attribute              | Parameters           | Description                                                                                   |
+| :----------------------| :------------------- | :-------------------------------------------------------------------------------------------- |
+| **Description**        | `description`          | A clear, concise statement of what the task entails.                                          |
+| **Agent**              | `agent`                | The agent responsible for the task, assigned either directly or by the crew's process.        |
+| **Expected Output**    | `expected_output`      | A detailed description of what the task's completion looks like.                              |
+| **Tools** *(optional)* | `tools`                | The functions or capabilities the agent can utilize to perform the task.                      |
+| **Async Execution** *(optional)* | `async_execution`      | If set, the task executes asynchronously, allowing progression without waiting for completion.|
+| **Context**  *(optional)* | `context`              | Specifies tasks whose outputs are used as context for this task.                              |
+| **Config** *(optional)* | `config`               | Additional configuration details for the agent executing the task, allowing further customization. |
+| **Output JSON**  *(optional)* | `output_json`          | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.         |
+| **Output Pydantic**  *(optional)* | `output_pydantic`      | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
+| **Output File**  *(optional)* | `output_file`          | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
+| **Callback**  *(optional)* | `callback`             | A Python callable that is executed with the task's output upon completion.                    |
+| **Human Input** *(optional)* | `human_input`         | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
 
 ## Creating a Task
 
@@ -37,75 +35,12 @@ from crewai import Task
 
 task = Task(
     description='Find and summarize the latest and most relevant news on AI',
-    agent=sales_agent,
-    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=sales_agent
 )
 ```
 
 !!! note "Task Assignment"
-Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
-
-## Task Output
-
-!!! note "Understanding Task Outputs"
-The output of a task in the crewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw strings, JSON, and Pydantic models.
-By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
-
-### Task Output Attributes
-
-| Attribute         | Parameters      | Type                       | Description                                                                                        |
-| :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
-| **Description**   | `description`   | `str`                      | A brief description of the task.                                                                   |
-| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the description.                                  |
-| **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
-| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
-| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
-| **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
-| **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
-
-### Task Output Methods and Properties
-
-| Method/Property | Description                                                                                       |
-| :-------------- | :------------------------------------------------------------------------------------------------ |
-| **json**        | Returns the JSON string representation of the task output if the output format is JSON.           |
-| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
-| \***\*str\*\*** | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
-
-### Accessing Task Outputs
-
-Once a task has been executed, its output can be accessed through the `output` attribute of the `Task` object. The `TaskOutput` class provides various ways to interact with and present this output.
-
-#### Example
-
-```python
-# Example task
-task = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-# Execute the crew
-crew = Crew(
-    agents=[research_agent],
-    tasks=[task],
-    verbose=2
-)
-
-result = crew.kickoff()
-
-# Accessing the task output
-task_output = task.output
-
-print(f"Task Description: {task_output.description}")
-print(f"Task Summary: {task_output.summary}")
-print(f"Raw Output: {task_output.raw}")
-if task_output.json_dict:
-    print(f"JSON Output: {json.dumps(task_output.json_dict, indent=2)}")
-if task_output.pydantic:
-    print(f"Pydantic Output: {task_output.pydantic}")
-```
+    Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
 
 ## Integrating Tools with Tasks
 

docs/core-concepts/Testing.md

@@ -1,41 +0,0 @@
----
-title: crewAI Testing
-description: Learn how to test your crewAI Crew and evaluate their performance.
----
-
-## Introduction
-
-Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. And with crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
-
-### Using the Testing Feature
-
-We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics.
-The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now the only provider available is OpenAI.
-
-```bash
-crewai test
-```
-
-If you want to run more iterations or use a different model, you can specify the parameters like this:
-
-```bash
-crewai test --n_iterations 5 --model gpt-4o
-```
-
-What happens when you run the `crewai test` command is that the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
-
-A table of scores at the end will show the performance of the crew in terms of the following metrics:
-```
-                Task Scores
-          (1-10 Higher is better)
-┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
-┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
-┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
-│ Task 1     │ 10.0  │ 9.0   │ 9.5        │
-│ Task 2     │ 9.0   │ 9.0   │ 9.0        │
-│ Crew       │ 9.5   │ 9.0   │ 9.2        │
-└────────────┴───────┴───────┴────────────┘
-```
-
-The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.
-

docs/core-concepts/Tools.md

@@ -100,24 +100,16 @@ Here is a list of the available tools and their descriptions:
 
 | Tool                        | Description                                                                                   |
 | :-------------------------- | :-------------------------------------------------------------------------------------------- |
-| **BrowserbaseLoadTool**     | A tool for interacting with and extracting data from web browsers.                            |
 | **CodeDocsSearchTool**      | A RAG tool optimized for searching through code documentation and related technical documents. |
-| **CodeInterpreterTool**     | A tool for interpreting python code.                                                          |
-| **ComposioTool**            | Enables use of Composio tools.                                                                |
 | **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
 | **DirectorySearchTool**     | A RAG tool for searching within directories, useful for navigating through file systems.      |
 | **DOCXSearchTool**          | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.         |
 | **DirectoryReadTool**       | Facilitates reading and processing of directory structures and their contents.                |
-| **EXASearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
 | **FileReadTool**            | Enables reading and extracting data from files, supporting various file formats.              |
-| **FirecrawlSearchTool**     | A tool to search webpages using Firecrawl and return the results.                             |
-| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl.                                               |
-| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages url using Firecrawl and returning its contents.               |
 | **GithubSearchTool**        | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
 | **SerperDevTool**           | A specialized tool for development purposes, with specific functionalities under development. |
 | **TXTSearchTool**           | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.     |
 | **JSONSearchTool**          | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
-| **LlamaIndexTool**          | Enables the use of LlamaIndex tools.                                                          |
 | **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
 | **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
 | **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
@@ -128,6 +120,8 @@ Here is a list of the available tools and their descriptions:
 | **XMLSearchTool**           | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
 | **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis.           |
 | **YoutubeVideoSearchTool**  | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
+| **BrowserbaseTool**         | A tool for interacting with and extracting data from web browsers.                            |
+| **ExaSearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
 
 ## Creating your own Tools
 

docs/how-to/Conditional-Tasks.md

@@ -1,87 +0,0 @@
----
-title: Conditional Tasks
-description: Learn how to use conditional tasks in a crewAI kickoff
----
-
-## Introduction
-
-Conditional Tasks in crewAI allow for dynamic workflow adaptation based on the outcomes of previous tasks. This powerful feature enables crews to make decisions and execute tasks selectively, enhancing the flexibility and efficiency of your AI-driven processes.
-
-```python
-from typing import List
-
-from pydantic import BaseModel
-from crewai import Agent, Crew
-from crewai.tasks.conditional_task import ConditionalTask
-from crewai.tasks.task_output import TaskOutput
-from crewai.task import Task
-from crewai_tools import SerperDevTool
-
-
-# Define a condition function for the conditional task
-# if false task will be skipped, true, then execute task
-def is_data_missing(output: TaskOutput) -> bool:
-    return len(output.pydantic.events) < 10: # this will skip this task
-
-# Define the agents
-data_fetcher_agent = Agent(
-    role="Data Fetcher",
-    goal="Fetch data online using Serper tool",
-    backstory="Backstory 1",
-    verbose=True,
-    tools=[SerperDevTool()],
-)
-
-data_processor_agent = Agent(
-    role="Data Processor",
-    goal="Process fetched data",
-    backstory="Backstory 2",
-    verbose=True,
-)
-
-summary_generator_agent = Agent(
-    role="Summary Generator",
-    goal="Generate summary from fetched data",
-    backstory="Backstory 3",
-    verbose=True,
-)
-
-
-class EventOutput(BaseModel):
-    events: List[str]
-
-
-task1 = Task(
-    description="Fetch data about events in San Francisco using Serper tool",
-    expected_output="List of 10 things to do in SF this week",
-    agent=data_fetcher_agent,
-    output_pydantic=EventOutput,
-)
-
-conditional_task = ConditionalTask(
-    description="""
-        Check if data is missing. If we have less than 10 events,
-        fetch more events using Serper tool so that
-        we have a total of 10 events in SF this week..
-        """,
-    expected_output="List of 10 Things to do in SF this week ",
-    condition=is_data_missing,
-    agent=data_processor_agent,
-)
-
-task3 = Task(
-    description="Generate summary of events in San Francisco from fetched data",
-    expected_output="summary_generated",
-    agent=summary_generator_agent,
-)
-
-# Create a crew with the tasks
-crew = Crew(
-    agents=[data_fetcher_agent, data_processor_agent, summary_generator_agent],
-    tasks=[task1, conditional_task, task3],
-    verbose=2,
-)
-
-result = crew.kickoff()
-print("results", result)
-```

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

@@ -21,16 +21,14 @@ Define your agents with distinct roles, backstories, and enhanced capabilities.
 import os
 from langchain.llms import OpenAI
 from crewai import Agent
-from crewai_tools import SerperDevTool, BrowserbaseLoadTool, EXASearchTool
+from crewai_tools import SerperDevTool, BrowserbaseTool, ExaSearchTool
 
 os.environ["OPENAI_API_KEY"] = "Your OpenAI Key"
 os.environ["SERPER_API_KEY"] = "Your Serper Key"
-os.environ["BROWSERBASE_API_KEY"] = "Your BrowserBase Key"
-os.environ["BROWSERBASE_PROJECT_ID"] = "Your BrowserBase Project Id"
 
 search_tool = SerperDevTool()
-browser_tool = BrowserbaseLoadTool()
-exa_search_tool = EXASearchTool()
+browser_tool = BrowserbaseTool()
+exa_search_tool = ExaSearchTool()
 
 # Creating a senior researcher agent with advanced configurations
 researcher = Agent(
@@ -81,4 +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/LLM-Connections.md

@@ -127,7 +127,7 @@ llm = HuggingFaceHub(
 ```
 
 ## OpenAI Compatible API Endpoints
-Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, Groq, and Mistral AI.
+Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, and Mistral AI.
 
 ### Configuration Examples
 #### FastChat
@@ -144,13 +144,6 @@ OPENAI_API_BASE="http://localhost:1234/v1"
 OPENAI_API_KEY="lm-studio"
 ```
 
-#### Groq API
-```sh
-OPENAI_API_KEY=your-groq-api-key
-OPENAI_MODEL_NAME='llama3-8b-8192'
-OPENAI_API_BASE=https://api.groq.com/openai/v1
-```
-
 #### Mistral API
 ```sh
 OPENAI_API_KEY=your-mistral-api-key
@@ -218,4 +211,4 @@ azure_agent = Agent(
 ```
 
 ## Conclusion
-Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.
+Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.

docs/how-to/Replay-tasks-from-latest-Crew-Kickoff.md

@@ -1,49 +0,0 @@
----
-title: Replay Tasks from Latest Crew Kickoff
-description: Replay tasks from the latest crew.kickoff(...)
----
-
-## Introduction
-CrewAI provides the ability to replay from a task specified from the latest crew kickoff. This feature is particularly useful when you've finished a kickoff and may want to retry certain tasks or don't need to refetch data over and your agents already have the context saved from the kickoff execution so you just need to replay the tasks you want to.
-
-## Note:
-You must run `crew.kickoff()` before you can replay a task. Currently, only the latest kickoff is supported, so if you use `kickoff_for_each`, it will only allow you to replay from the most recent crew run.
-
-Here's an example of how to replay from a task:
-
-### 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:
-
-To view latest kickoff task_ids use:
-```shell
-crewai log-tasks-outputs
-```
-
-Once you have your task_id to replay from use:
-```shell
-crewai replay -t <task_id>
-```
-
-
-### Replaying from a task Programmatically
-To replay from a task programmatically, use the following steps:
-
-1. Specify the task_id and input parameters for the replay process.
-2. Execute the replay command within a try-except block to handle potential errors.
-
-```python
-   def replay():
-    """
-    Replay the crew execution from a specific task.
-    """
-    task_id = '<task_id>'
-    inputs = {"topic": "CrewAI Training"} # this is optional, you can pass in the inputs you want to replay otherwise uses the previous kickoffs inputs
-    try:
-        YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
-
-    except Exception as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")

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](https://docs.crewai.com/how-to/Installing-CrewAI/) 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

@@ -43,21 +43,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Memory
                 </a>
             </li>
-            <li>
-                <a href="./core-concepts/Planning">
-                    Planning
-                </a>
-            </li>
         </ul>
     </div>
     <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
@@ -98,11 +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
@@ -118,16 +103,6 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Kickoff a Crew for a List
                 </a>
             </li>
-            <li>
-                <a href="./how-to/Replay-tasks-from-latest-Crew-Kickoff">
-                    Replay from a Task
-                </a>
-            </li>
-            <li>
-                <a href="./how-to/Conditional-Tasks">
-                    Conditional Tasks
-                </a>
-            </li>
             <li>
                 <a href="./how-to/AgentOps-Observability">
                     Agent Monitoring with AgentOps

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

@@ -128,12 +128,9 @@ nav:
     - Collaboration: 'core-concepts/Collaboration.md'
     - Training: 'core-concepts/Training-Crew.md'
     - Memory: 'core-concepts/Memory.md'
-    - Planning: 'core-concepts/Planning.md'
-    - Testing: 'core-concepts/Testing.md'
     - 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'
@@ -143,12 +140,9 @@ 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'
-    - Replay from a specific task from a kickoff: 'how-to/Replay-tasks-from-latest-Crew-Kickoff.md'
-    - Conditional Tasks: 'how-to/Conditional-Tasks.md'
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
   - Tools Docs:
@@ -184,7 +178,6 @@ nav:
     - Landing Page Generator: https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator"
     - Prepare for meetings: https://github.com/joaomdmoura/crewAI-examples/tree/main/prep-for-a-meeting"
   - Telemetry: 'telemetry/Telemetry.md'
-  - Change Log: 'https://github.com/crewAIInc/crewAI/releases'
 
 extra_css:
   - stylesheets/output.css

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.41.1"
+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"
@@ -14,21 +14,20 @@ Repository = "https://github.com/joaomdmoura/crewai"
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
 pydantic = "^2.4.2"
-langchain = ">0.2,<=0.3"
+langchain = ">=0.1.4,<0.2.0"
 openai = "^1.13.3"
 opentelemetry-api = "^1.22.0"
 opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
 regex = "^2023.12.25"
-crewai-tools = { version = "^0.4.26", optional = true }
+crewai-tools = { version = "^0.4.6", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
 jsonref = "^1.1.0"
-agentops = { version = "^0.3.0", optional = true }
-embedchain = "^0.1.114"
-json-repair = "^0.25.2"
+agentops = { version = "^0.1.9", optional = true }
+embedchain = "^0.1.113"
 
 [tool.poetry.extras]
 tools = ["crewai-tools"]
@@ -46,7 +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 = "^0.4.26"
+crewai-tools = "^0.4.6"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"

src/crewai/agent.py

@@ -1,14 +1,13 @@
 import os
-from inspect import signature
 from typing import Any, List, Optional, Tuple
 
 from langchain.agents.agent import RunnableAgent
-from langchain.agents.tools import BaseTool
 from langchain.agents.tools import tool as LangChainTool
+from langchain.tools.render import render_text_description
 from langchain_core.agents import AgentAction
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_openai import ChatOpenAI
-from pydantic import Field, InstanceOf, PrivateAttr, model_validator
+from pydantic import Field, InstanceOf, model_validator
 
 from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -21,7 +20,7 @@ from crewai.utilities.training_handler import CrewTrainingHandler
 
 agentops = None
 try:
-    import agentops  # type: ignore # Name "agentops" already defined on line 21
+    import agentops
     from agentops import track_agent
 except ImportError:
 
@@ -55,17 +54,14 @@ class Agent(BaseAgent):
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
             callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
-            allow_code_execution: Enable code execution for the agent.
-            max_retry_limit: Maximum number of retries for an agent to execute a task when an error occurs.
     """
 
-    _times_executed: int = PrivateAttr(default=0)
     max_execution_time: Optional[int] = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
     )
-    agent_ops_agent_name: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
-    agent_ops_agent_id: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
+    agent_ops_agent_name: str = None
+    agent_ops_agent_id: str = None
     cache_handler: InstanceOf[CacheHandler] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
@@ -94,16 +90,9 @@ class Agent(BaseAgent):
     response_template: Optional[str] = Field(
         default=None, description="Response format for the agent."
     )
-    tools_results: Optional[List[Any]] = Field(
-        default=[], description="Results of the tools used by the agent."
-    )
     allow_code_execution: Optional[bool] = Field(
         default=False, description="Enable code execution for the agent."
     )
-    max_retry_limit: int = Field(
-        default=2,
-        description="Maximum number of retries for an agent to execute a task when an error occurs.",
-    )
 
     def __init__(__pydantic_self__, **data):
         config = data.pop("config", {})
@@ -127,8 +116,7 @@ class Agent(BaseAgent):
                 self.llm.callbacks.append(token_handler)
 
             if agentops and not any(
-                isinstance(handler, agentops.LangchainCallbackHandler)
-                for handler in self.llm.callbacks
+                isinstance(handler, agentops.LangchainCallbackHandler) for handler in self.llm.callbacks
             ):
                 agentops.stop_instrumenting()
                 self.llm.callbacks.append(agentops.LangchainCallbackHandler())
@@ -156,7 +144,8 @@ class Agent(BaseAgent):
             Output of the agent
         """
         if self.tools_handler:
-            self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
+            # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
+            self.tools_handler.last_used_tool = {}
 
         task_prompt = task.prompt()
 
@@ -175,15 +164,14 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        tools = tools or self.tools or []
-        parsed_tools = self._parse_tools(tools)
+        tools = tools or self.tools
+        # type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]"
+        parsed_tools = self._parse_tools(tools or [])
         self.create_agent_executor(tools=tools)
         self.agent_executor.tools = parsed_tools
         self.agent_executor.task = task
 
-        self.agent_executor.tools_description = self._render_text_description_and_args(
-            parsed_tools
-        )
+        self.agent_executor.tools_description = render_text_description(parsed_tools)
         self.agent_executor.tools_names = self.__tools_names(parsed_tools)
 
         if self.crew and self.crew._train:
@@ -191,30 +179,15 @@ class Agent(BaseAgent):
         else:
             task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
-        try:
-            result = self.agent_executor.invoke(
-                {
-                    "input": task_prompt,
-                    "tool_names": self.agent_executor.tools_names,
-                    "tools": self.agent_executor.tools_description,
-                }
-            )["output"]
-        except Exception as e:
-            self._times_executed += 1
-            if self._times_executed > self.max_retry_limit:
-                raise e
-            result = self.execute_task(task, context, tools)
-
+        result = self.agent_executor.invoke(
+            {
+                "input": task_prompt,
+                "tool_names": self.agent_executor.tools_names,
+                "tools": self.agent_executor.tools_description,
+            }
+        )["output"]
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
-
-        # If there was any tool in self.tools_results that had result_as_answer
-        # set to True, return the results of the last tool that had
-        # result_as_answer set to True
-        for tool_result in self.tools_results:  # type: ignore # Item "None" of "list[Any] | None" has no attribute "__iter__" (not iterable)
-            if tool_result.get("result_as_answer", False):
-                result = tool_result["result"]
-
         return result
 
     def format_log_to_str(
@@ -236,7 +209,7 @@ class Agent(BaseAgent):
         Returns:
             An instance of the CrewAgentExecutor class.
         """
-        tools = tools or self.tools or []
+        tools = tools or self.tools
 
         agent_args = {
             "input": lambda x: x["input"],
@@ -315,7 +288,7 @@ class Agent(BaseAgent):
     def get_output_converter(self, llm, text, model, instructions):
         return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
-    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:  # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
+    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:
         """Parse tools to be used for the task."""
         tools_list = []
         try:
@@ -331,7 +304,6 @@ class Agent(BaseAgent):
             tools_list = []
             for tool in tools:
                 tools_list.append(tool)
-
         return tools_list
 
     def _training_handler(self, task_prompt: str) -> str:
@@ -358,52 +330,6 @@ class Agent(BaseAgent):
                 )
         return task_prompt
 
-    def _render_text_description(self, tools: List[BaseTool]) -> str:
-        """Render the tool name and description in plain text.
-
-        Output will be in the format of:
-
-        .. code-block:: markdown
-
-            search: This tool is used for search
-            calculator: This tool is used for math
-        """
-        description = "\n".join(
-            [
-                f"Tool name: {tool.name}\nTool description:\n{tool.description}"
-                for tool in tools
-            ]
-        )
-
-        return description
-
-    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
-        """Render the tool name, description, and args in plain text.
-
-        Output will be in the format of:
-
-        .. code-block:: markdown
-
-            search: This tool is used for search, args: {"query": {"type": "string"}}
-            calculator: This tool is used for math, \
-    args: {"expression": {"type": "string"}}
-        """
-        tool_strings = []
-        for tool in tools:
-            args_schema = str(tool.args)
-            if hasattr(tool, "func") and tool.func:
-                sig = signature(tool.func)
-                description = (
-                    f"Tool Name: {tool.name}{sig}\nTool Description: {tool.description}"
-                )
-            else:
-                description = (
-                    f"Tool Name: {tool.name}\nTool Description: {tool.description}"
-                )
-            tool_strings.append(f"{description}\nTool Arguments: {args_schema}")
-
-        return "\n".join(tool_strings)
-
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])

src/crewai/agents/agent_builder/base_agent.py

@@ -1,7 +1,6 @@
 import uuid
 from abc import ABC, abstractmethod
 from copy import copy as shallow_copy
-from hashlib import md5
 from typing import Any, Dict, List, Optional, TypeVar
 
 from pydantic import (
@@ -163,11 +162,6 @@ class BaseAgent(ABC, BaseModel):
             self._token_process = TokenProcess()
         return self
 
-    @property
-    def key(self):
-        source = [self.role, self.goal, self.backstory]
-        return md5("|".join(source).encode()).hexdigest()
-
     @abstractmethod
     def execute_task(
         self,
@@ -186,7 +180,7 @@ class BaseAgent(ABC, BaseModel):
         pass
 
     @abstractmethod
-    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[Any]:
+    def get_delegation_tools(self, agents: List["BaseAgent"]):
         """Set the task tools that init BaseAgenTools class."""
         pass
 
@@ -197,7 +191,7 @@ class BaseAgent(ABC, BaseModel):
         """Get the converter class for the agent to create json/pydantic outputs."""
         pass
 
-    def copy(self: T) -> T:  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"
+    def copy(self: T) -> T:
         """Create a deep copy of the Agent."""
         exclude = {
             "id",

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

@@ -1,8 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import List, Optional, Union
-
 from pydantic import BaseModel, Field
-
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
 from crewai.utilities import I18N
@@ -24,7 +22,6 @@ class BaseAgentTools(BaseModel, ABC):
             is_list = coworker.startswith("[") and coworker.endswith("]")
             if is_list:
                 coworker = coworker[1:-1].split(",")[0]
-
         return coworker
 
     def delegate_work(
@@ -41,13 +38,11 @@ class BaseAgentTools(BaseModel, ABC):
         coworker = self._get_coworker(coworker, **kwargs)
         return self._execute(coworker, question, context)
 
-    def _execute(
-        self, agent_name: Union[str, None], task: str, context: Union[str, None]
-    ):
+    def _execute(self, agent: Union[str, None], task: str, context: Union[str, None]):
         """Execute the command."""
         try:
-            if agent_name is None:
-                agent_name = ""
+            if agent is None:
+                agent = ""
 
             # It is important to remove the quotes from the agent name.
             # The reason we have to do this is because less-powerful LLM's
@@ -56,9 +51,9 @@ class BaseAgentTools(BaseModel, ABC):
             # {"task": "....", "coworker": "....
             # when it should look like this:
             # {"task": "....", "coworker": "...."}
-            agent_name = agent_name.casefold().replace('"', "").replace("\n", "")
+            agent_name = agent.casefold().replace('"', "").replace("\n", "")
 
-            agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
+            agent = [
                 available_agent
                 for available_agent in self.agents
                 if available_agent.role.casefold().replace("\n", "") == agent_name
@@ -78,9 +73,9 @@ class BaseAgentTools(BaseModel, ABC):
             )
 
         agent = agent[0]
-        task_with_assigned_agent = Task(  # type: ignore # Incompatible types in assignment (expression has type "Task", variable has type "str")
+        task = Task(
             description=task,
             agent=agent,
             expected_output="Your best answer to your coworker asking you this, accounting for the context shared.",
         )
-        return agent.execute_task(task_with_assigned_agent, context)
+        return agent.execute_task(task, context)

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

@@ -1,7 +1,8 @@
 from abc import ABC, abstractmethod
 from typing import Any, Optional
 
-from pydantic import BaseModel, Field
+
+from pydantic import BaseModel, Field, PrivateAttr
 
 
 class OutputConverter(BaseModel, ABC):
@@ -21,12 +22,13 @@ class OutputConverter(BaseModel, ABC):
         max_attempts (int): Maximum number of conversion attempts (default: 3).
     """
 
+    _is_gpt: bool = PrivateAttr(default=True)
     text: str = Field(description="Text to be converted.")
     llm: Any = Field(description="The language model to be used to convert the text.")
     model: Any = Field(description="The model to be used to convert the text.")
     instructions: str = Field(description="Conversion instructions to the LLM.")
-    max_attempts: Optional[int] = Field(
-        description="Max number of attempts to try to get the output formatted.",
+    max_attemps: Optional[int] = Field(
+        description="Max number of attemps to try to get the output formated.",
         default=3,
     )
 
@@ -40,8 +42,7 @@ class OutputConverter(BaseModel, ABC):
         """Convert text to json."""
         pass
 
-    @property
     @abstractmethod
-    def is_gpt(self) -> bool:
+    def _is_gpt(self, llm):
         """Return if llm provided is of gpt from openai."""
         pass

src/crewai/agents/executor.py

@@ -1,22 +1,33 @@
 import threading
 import time
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+from typing import (
+    Any,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Union,
+)
 
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain_core.agents import AgentAction, AgentFinish, AgentStep
 from langchain_core.exceptions import OutputParserException
+
 from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
 from pydantic import InstanceOf
+from crewai.agents.agent_builder.base_agent_executor_mixin import (
+    CrewAgentExecutorMixin,
+)
 
-from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
-from crewai.utilities import I18N
 from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.training_handler import CrewTrainingHandler
+from crewai.utilities import I18N
 
 
 class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
@@ -35,7 +46,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
     tools_handler: Optional[InstanceOf[ToolsHandler]] = None
     max_iterations: Optional[int] = 15
     have_forced_answer: bool = False
-    force_answer_max_iterations: Optional[int] = None  # type: ignore # Incompatible types in assignment (expression has type "int | None", base class "CrewAgentExecutorMixin" defined the type as "int")
+    force_answer_max_iterations: Optional[int] = None
     step_callback: Optional[Any] = None
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
@@ -232,7 +243,6 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                 tools_names=self.tools_names,
                 function_calling_llm=self.function_calling_llm,
                 task=self.task,
-                agent=self.crew_agent,
                 action=agent_action,
             )
             tool_calling = tool_usage.parse(agent_action.log)
@@ -242,8 +252,6 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             else:
                 if tool_calling.tool_name.casefold().strip() in [
                     name.casefold().strip() for name in name_to_tool_map
-                ] or tool_calling.tool_name.casefold().replace("_", " ") in [
-                    name.casefold().strip() for name in name_to_tool_map
                 ]:
                     observation = tool_usage.use(tool_calling, agent_action.log)
                 else:

src/crewai/agents/parser.py

@@ -1,7 +1,6 @@
 import re
 from typing import Any, Union
 
-from json_repair import repair_json
 from langchain.agents.output_parsers import ReActSingleInputOutputParser
 from langchain_core.agents import AgentAction, AgentFinish
 from langchain_core.exceptions import OutputParserException
@@ -49,15 +48,11 @@ class CrewAgentParser(ReActSingleInputOutputParser):
                 raise OutputParserException(
                     f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
                 )
-            action = action_match.group(1)
-            clean_action = self._clean_action(action)
-
-            action_input = action_match.group(2).strip()
-
-            tool_input = action_input.strip(" ").strip('"')
-            safe_tool_input = self._safe_repair_json(tool_input)
-
-            return AgentAction(clean_action, safe_tool_input, text)
+            action = action_match.group(1).strip()
+            action_input = action_match.group(2)
+            tool_input = action_input.strip(" ")
+            tool_input = tool_input.strip('"')
+            return AgentAction(action, tool_input, text)
 
         elif includes_answer:
             return AgentFinish(
@@ -92,30 +87,3 @@ class CrewAgentParser(ReActSingleInputOutputParser):
                 llm_output=text,
                 send_to_llm=True,
             )
-
-    def _clean_action(self, text: str) -> str:
-        """Clean action string by removing non-essential formatting characters."""
-        return re.sub(r"^\s*\*+\s*|\s*\*+\s*$", "", text).strip()
-
-    def _safe_repair_json(self, tool_input: str) -> str:
-        UNABLE_TO_REPAIR_JSON_RESULTS = ['""', "{}"]
-
-        # Skip repair if the input starts and ends with square brackets
-        # Explanation: The JSON parser has issues handling inputs that are enclosed in square brackets ('[]').
-        # These are typically valid JSON arrays or strings that do not require repair. Attempting to repair such inputs
-        # might lead to unintended alterations, such as wrapping the entire input in additional layers or modifying
-        # the structure in a way that changes its meaning. By skipping the repair for inputs that start and end with
-        # square brackets, we preserve the integrity of these valid JSON structures and avoid unnecessary modifications.
-        if tool_input.startswith("[") and tool_input.endswith("]"):
-            return tool_input
-
-        # Before repair, handle common LLM issues:
-        # 1. Replace """ with " to avoid JSON parser errors
-
-        tool_input = tool_input.replace('"""', '"')
-
-        result = repair_json(tool_input)
-        if result in UNABLE_TO_REPAIR_JSON_RESULTS:
-            return tool_input
-
-        return str(result)

src/crewai/cli/cli.py

@@ -1,14 +1,7 @@
 import click
 import pkg_resources
 
-from crewai.memory.storage.kickoff_task_outputs_storage import (
-    KickoffTaskOutputsSQLiteStorage,
-)
-
 from .create_crew import create_crew
-from .replay_from_task import replay_task_command
-from .reset_memories_command import reset_memories_command
-from .test_crew import test_crew
 from .train_crew import train_crew
 
 
@@ -55,97 +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 log_tasks_outputs() -> None:
-    """
-    Retrieve your latest crew.kickoff() task outputs.
-    """
-    try:
-        storage = KickoffTaskOutputsSQLiteStorage()
-        tasks = storage.load()
-
-        if not tasks:
-            click.echo(
-                "No task outputs found. Only crew kickoff task outputs are logged."
-            )
-            return
-
-        for index, task in enumerate(tasks, 1):
-            click.echo(f"Task {index}: {task['task_id']}")
-            click.echo(f"Description: {task['expected_output']}")
-            click.echo("------")
-
-    except Exception as e:
-        click.echo(f"An error occurred while logging task outputs: {e}", err=True)
-
-
-@crewai.command()
-@click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory")
-@click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
-@click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
-@click.option(
-    "-k",
-    "--kickoff-outputs",
-    is_flag=True,
-    help="Reset LATEST KICKOFF TASK OUTPUTS",
-)
-@click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
-def reset_memories(long, short, entities, kickoff_outputs, all):
-    """
-    Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
-    """
-    try:
-        if not all and not (long or short or entities or kickoff_outputs):
-            click.echo(
-                "Please specify at least one memory type to reset using the appropriate flags."
-            )
-            return
-        reset_memories_command(long, short, entities, kickoff_outputs, all)
-    except Exception as e:
-        click.echo(f"An error occurred while resetting memories: {e}", err=True)
-
-
-@crewai.command()
-@click.option(
-    "-n",
-    "--n_iterations",
-    type=int,
-    default=3,
-    help="Number of iterations to Test the crew",
-)
-@click.option(
-    "-m",
-    "--model",
-    type=str,
-    default="gpt-4o-mini",
-    help="LLM Model to run the tests on the Crew. For now only accepting only OpenAI models.",
-)
-def test(n_iterations: int, model: str):
-    """Test the crew and evaluate the results."""
-    click.echo(f"Testing the crew for {n_iterations} iterations with model {model}")
-    test_crew(n_iterations, model)
-
-
 if __name__ == "__main__":
     crewai()

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", 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/reset_memories_command.py

@@ -1,45 +0,0 @@
-import subprocess
-import click
-
-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.utilities.task_output_storage_handler import TaskOutputStorageHandler
-
-
-def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
-    """
-    Replay the crew execution from a specific task.
-
-    Args:
-      task_id (str): The ID of the task to replay from.
-    """
-
-    try:
-        if all:
-            ShortTermMemory().reset()
-            EntityMemory().reset()
-            LongTermMemory().reset()
-            TaskOutputStorageHandler().reset()
-            click.echo("All memories have been reset.")
-        else:
-            if long:
-                LongTermMemory().reset()
-                click.echo("Long term memory has been reset.")
-
-            if short:
-                ShortTermMemory().reset()
-                click.echo("Short term memory has been reset.")
-            if entity:
-                EntityMemory().reset()
-                click.echo("Entity memory has been reset.")
-            if kickoff_outputs:
-                TaskOutputStorageHandler().reset()
-                click.echo("Latest Kickoff outputs stored has been reset.")
-
-    except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while resetting the memories: {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

@@ -2,15 +2,9 @@
 import sys
 from {{folder_name}}.crew import {{crew_name}}Crew
 
-# This main file is intended to be a way for your to run your
-# crew locally, so refrain from adding necessary logic into this file.
-# Replace with inputs you want to test with, it will automatically
-# interpolate any tasks and agents information
 
 def run():
-    """
-    Run the crew.
-    """
+    # Replace with your inputs, it will automatically interpolate any tasks and agents information
     inputs = {
         'topic': 'AI LLMs'
     }
@@ -21,34 +15,9 @@ def train():
     """
     Train the crew for a given number of iterations.
     """
-    inputs = {
-        "topic": "AI LLMs"
-    }
+    inputs = {"topic": "AI LLMs"}
     try:
         {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
-
-def replay():
-    """
-    Replay the crew execution from a specific task.
-    """
-    try:
-        {{crew_name}}Crew().crew().replay(task_id=sys.argv[1])
-
-    except Exception as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")
-
-def test():
-    """
-    Test the crew execution and returns the results.
-    """
-    inputs = {
-        "topic": "AI LLMs"
-    }
-    try:
-        {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
-
-    except Exception as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")

src/crewai/cli/templates/pyproject.toml

@@ -6,13 +6,11 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.41.1" }
+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"
-test = "{{folder_name}}.main:test"
 
 [build-system]
 requires = ["poetry-core"]

src/crewai/cli/test_crew.py

@@ -1,30 +0,0 @@
-import subprocess
-
-import click
-
-
-def test_crew(n_iterations: int, model: str) -> None:
-    """
-    Test the crew by running a command in the Poetry environment.
-
-    Args:
-        n_iterations (int): The number of iterations to test the crew.
-        model (str): The model to test the crew with.
-    """
-    command = ["poetry", "run", "test", str(n_iterations), model]
-
-    try:
-        if n_iterations <= 0:
-            raise ValueError("The number of iterations must be a positive integer.")
-
-        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 testing the crew: {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/crew.py

@@ -1,9 +1,7 @@
 import asyncio
 import json
 import uuid
-from concurrent.futures import Future
-from hashlib import md5
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Union, Tuple
 
 from langchain_core.callbacks import BaseCallbackHandler
 from pydantic import (
@@ -22,29 +20,15 @@ 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.conditional_task import ConditionalTask
-from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools import AgentTools
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import (
-    TRAINED_AGENTS_DATA_FILE,
-    TRAINING_DATA_FILE,
-)
-from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-from crewai.utilities.formatter import (
-    aggregate_raw_outputs_from_task_outputs,
-    aggregate_raw_outputs_from_tasks,
-)
-from crewai.utilities.planning_handler import CrewPlanner
-from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 try:
@@ -72,10 +56,10 @@ 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.
-        planning: Plan the crew execution and add the plan to the crew.
     """
 
     __hash__ = object.__hash__  # type: ignore
@@ -89,13 +73,6 @@ 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",
-    )
-    _task_output_handler: TaskOutputStorageHandler = PrivateAttr(
-        default_factory=TaskOutputStorageHandler
-    )
 
     cache: bool = Field(default=True)
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -115,6 +92,10 @@ 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
     )
@@ -151,18 +132,6 @@ class Crew(BaseModel):
         default=False,
         description="output_log_file",
     )
-    planning: Optional[bool] = Field(
-        default=False,
-        description="Plan the crew execution and add the plan to the crew.",
-    )
-    task_execution_output_json_files: Optional[List[str]] = Field(
-        default=None,
-        description="List of file paths for task execution JSON files.",
-    )
-    execution_logs: List[Dict[str, Any]] = Field(
-        default=[],
-        description="List of execution logs for tasks",
-    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -198,6 +167,7 @@ class Crew(BaseModel):
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
         self._telemetry = Telemetry()
         self._telemetry.set_tracer()
+        self._telemetry.crew_creation(self)
         return self
 
     @model_validator(mode="after")
@@ -261,7 +231,7 @@ class Crew(BaseModel):
                 if task.agent is None:
                     raise PydanticCustomError(
                         "missing_agent_in_task",
-                        f"Sequential process error: Agent is missing in the task with the following description: {task.description}",  # type: ignore # Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
+                        f"Sequential process error: Agent is missing in the task with the following description: {task.description}",  # type: ignore Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
                         {},
                     )
 
@@ -281,93 +251,6 @@ 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_first_task(self) -> "Crew":
-        """Ensure the first task is not a ConditionalTask."""
-        if self.tasks and isinstance(self.tasks[0], ConditionalTask):
-            raise PydanticCustomError(
-                "invalid_first_task",
-                "The first task cannot be a ConditionalTask.",
-                {},
-            )
-        return self
-
-    @model_validator(mode="after")
-    def validate_async_tasks_not_async(self) -> "Crew":
-        """Ensure that ConditionalTask is not async."""
-        for task in self.tasks:
-            if task.async_execution and isinstance(task, ConditionalTask):
-                raise PydanticCustomError(
-                    "invalid_async_conditional_task",
-                    f"Conditional Task: {task.description} , cannot be executed asynchronously.",  # type: ignore # Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
-                    {},
-                )
-        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
-
-    @property
-    def key(self) -> str:
-        source = [agent.key for agent in self.agents] + [
-            task.key for task in self.tasks
-        ]
-        return md5("|".join(source).encode()).hexdigest()
-
     def _setup_from_config(self):
         assert self.config is not None, "Config should not be None."
 
@@ -406,9 +289,6 @@ class Crew(BaseModel):
         for agent in self.agents:
             agent.allow_delegation = False
 
-        CrewTrainingHandler(TRAINING_DATA_FILE).initialize_file()
-        CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).initialize_file()
-
     def train(self, n_iterations: int, inputs: Optional[Dict[str, Any]] = {}) -> None:
         """Trains the crew for a given number of iterations."""
         self._setup_for_training()
@@ -417,58 +297,53 @@ class Crew(BaseModel):
             self._train_iteration = n_iteration
             self.kickoff(inputs=inputs)
 
-        training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
+        training_data = CrewTrainingHandler("training_data.pkl").load()
 
         for agent in self.agents:
             result = TaskEvaluator(agent).evaluate_training_data(
                 training_data=training_data, agent_id=str(agent.id)
             )
 
-            CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).save_trained_data(
+            CrewTrainingHandler("trained_agents_data.pkl").save_trained_data(
                 agent_id=str(agent.role), trained_data=result.model_dump()
             )
 
     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)
-        self._task_output_handler.reset()
-        self._logging_color = "bold_purple"
-
-        if inputs is not None:
-            self._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._interpolate_inputs(inputs)
         self._set_tasks_callbacks()
 
         i18n = I18N(prompt_file=self.prompt_file)
 
         for agent in self.agents:
+            # type: ignore # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
             agent.i18n = i18n
             # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
             agent.crew = self  # type: ignore[attr-defined]
             # TODO: Create an AgentFunctionCalling protocol for future refactoring
-            if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
-                agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
+            if not agent.function_calling_llm:
+                agent.function_calling_llm = self.function_calling_llm
 
-            if agent.allow_code_execution:  # type: ignore # BaseAgent" has no attribute "allow_code_execution"
-                agent.tools += agent.get_code_execution_tools()  # type: ignore # "BaseAgent" has no attribute "get_code_execution_tools"; maybe "get_delegation_tools"?
+            if agent.allow_code_execution:
+                agent.tools += agent.get_code_execution_tools()
 
-            if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
-                agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
+            if not agent.step_callback:
+                agent.step_callback = self.step_callback
 
             agent.create_agent_executor()
 
-        if self.planning:
-            self._handle_crew_planning()
-
         metrics = []
 
         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()
+            metrics.append(manager_metrics)
         else:
             raise NotImplementedError(
                 f"The process '{self.process}' is not implemented yet."
@@ -481,9 +356,11 @@ 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 = {
@@ -505,14 +382,15 @@ class Crew(BaseModel):
             results.append(output)
 
         self.usage_metrics = total_usage_metrics
-        self._task_output_handler.reset()
         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):
@@ -522,10 +400,6 @@ 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)
 
@@ -542,73 +416,53 @@ 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
-        self._task_output_handler.reset()
         return results
 
-    def _handle_crew_planning(self):
-        """Handles the Crew planning."""
-        self._logger.log("info", "Planning the crew execution")
-        result = CrewPlanner(self.tasks)._handle_crew_planning()
+    def _run_sequential_process(self) -> str:
+        """Executes tasks sequentially and returns the final output."""
+        task_output = ""
 
-        if result is not None and hasattr(result, "list_of_plans_per_task"):
-            for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
-                task.description += step_plan
-        else:
+        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)
+
+            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", "Something went wrong with the planning process of the Crew"
+                "info", f"== Starting Task: {task.description}", color="bold_purple"
             )
 
-    def _store_execution_log(
+            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()
+
+        # type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
+        return self._format_output(task_output, token_usage)
+
+    def _run_hierarchical_process(
         self,
-        task: Task,
-        output: TaskOutput,
-        task_index: int,
-        was_replayed: bool = False,
-    ):
-        if self._inputs:
-            inputs = self._inputs
-        else:
-            inputs = {}
-
-        log = {
-            "task": task,
-            "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,
-        }
-        self._task_output_handler.update(task_index, log)
-
-    def _run_sequential_process(self) -> CrewOutput:
-        """Executes tasks sequentially and returns the final output."""
-        return self._execute_tasks(self.tasks)
-
-    def _run_hierarchical_process(self) -> CrewOutput:
+    ) -> 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
@@ -627,263 +481,37 @@ 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
-        """
-
-        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
-
-            agent_to_use = self._get_agent_to_use(task, manager)
-            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."
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=manager.role, task=task.description, status="started"
                 )
 
-            self._prepare_agent_tools(task, manager)
-            self._log_task_start(task, agent_to_use.role)
-
-            if isinstance(task, ConditionalTask):
-                skipped_task_output = self._handle_conditional_task(
-                    task, task_outputs, futures, task_index, was_replayed
-                )
-                if skipped_task_output:
-                    continue
-
-            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))
+            if task.agent:
+                manager.tools = task.agent.get_delegation_tools([task.agent])
             else:
-                if futures:
-                    task_outputs = 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 _handle_conditional_task(
-        self,
-        task: ConditionalTask,
-        task_outputs: List[TaskOutput],
-        futures: List[Tuple[Task, Future[TaskOutput], int]],
-        task_index: int,
-        was_replayed: bool,
-    ) -> Optional[TaskOutput]:
-        if futures:
-            task_outputs = self._process_async_tasks(futures, was_replayed)
-            futures.clear()
-
-        previous_output = task_outputs[task_index - 1] if task_outputs else None
-        if previous_output is not None and not task.should_execute(previous_output):
-            self._logger.log(
-                "debug",
-                f"Skipping conditional task: {task.description}",
-                color="yellow",
+                manager.tools = manager.get_delegation_tools(self.agents)
+            task_output = task.execute(
+                agent=manager, context=task_output, tools=manager.tools
             )
-            skipped_task_output = task.get_skipped_task_output()
 
-            if not was_replayed:
-                self._store_execution_log(task, skipped_task_output, task_index)
-            return skipped_task_output
-        return None
-
-    def _prepare_agent_tools(self, task: Task, manager: Optional[BaseAgent]):
-        if self.process == Process.hierarchical:
-            if manager:
-                self._update_manager_tools(task, manager)
-            else:
-                raise ValueError("Manager agent is required for hierarchical process.")
-        elif task.agent and task.agent.allow_delegation:
-            self._add_delegation_tools(task)
-
-    def _get_agent_to_use(
-        self, task: Task, manager: Optional[BaseAgent]
-    ) -> Optional[BaseAgent]:
-        if self.process == Process.hierarchical:
-            return manager
-        return task.agent
-
-    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 len(agents_for_delegation) > 0 and task.agent:
-            delegation_tools = task.agent.get_delegation_tools(agents_for_delegation)
-
-            # Add tools if they are not already in task.tools
-            for new_tool in delegation_tools:
-                # Find the index of the tool with the same name
-                existing_tool_index = next(
-                    (
-                        index
-                        for index, tool in enumerate(task.tools or [])
-                        if tool.name == new_tool.name
-                    ),
-                    None,
+            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"
                 )
-                if not task.tools:
-                    task.tools = []
 
-                if existing_tool_index is not None:
-                    # Replace the existing tool
-                    task.tools[existing_tool_index] = new_tool
-                else:
-                    # Add the new tool
-                    task.tools.append(new_tool)
+        self._finish_execution(task_output)
 
-    def _log_task_start(self, task: Task, role: str = "None"):
-        color = self._logging_color
-        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: BaseAgent):
-        if task.agent:
-            manager.tools = task.agent.get_delegation_tools([task.agent])
-        else:
-            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."
-            )
-        final_task_output = task_outputs[0]
-        final_string_output = final_task_output.raw
-        self._finish_execution(final_string_output)
+        # type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
         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: List[TaskOutput] = []
-        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(
-        self, task_id: str, inputs: Optional[Dict[str, Any]] = None
-    ) -> CrewOutput:
-        stored_outputs = self._task_output_handler.load()
-        if not stored_outputs:
-            raise ValueError(f"Task with id {task_id} not found in the crew's tasks.")
-
-        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"
-            ]  # for adding context to the task
-            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."""
@@ -935,15 +563,33 @@ 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",
+                is_auto_end=True,
             )
-        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."""
@@ -967,20 +613,5 @@ class Crew(BaseModel):
 
         return total_usage_metrics
 
-    def test(
-        self,
-        n_iterations: int,
-        openai_model_name: str,
-        inputs: Optional[Dict[str, Any]] = None,
-    ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations."""
-        evaluator = CrewEvaluator(self, openai_model_name)
-
-        for i in range(1, n_iterations + 1):
-            evaluator.set_iteration(i)
-            self.kickoff(inputs=inputs)
-
-        evaluator.print_crew_evaluation_result()
-
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"

src/crewai/crews/__init__.py

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

src/crewai/crews/crew_output.py

@@ -1,50 +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 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]:
-        """Convert json_output and pydantic_output to a dictionary."""
-        output_dict = {}
-        if self.json_dict:
-            output_dict.update(self.json_dict)
-        elif self.pydantic:
-            output_dict.update(self.pydantic.model_dump())
-        return output_dict
-
-    def __str__(self):
-        if self.pydantic:
-            return str(self.pydantic)
-        if self.json_dict:
-            return str(self.json_dict)
-        return self.raw

src/crewai/memory/entity/entity_memory.py

@@ -23,9 +23,3 @@ class EntityMemory(Memory):
         """Saves an entity item into the SQLite storage."""
         data = f"{item.name}({item.type}): {item.description}"
         super().save(data, item.metadata)
-
-    def reset(self) -> None:
-        try:
-            self.storage.reset()
-        except Exception as e:
-            raise Exception(f"An error occurred while resetting the entity memory: {e}")

src/crewai/memory/long_term/long_term_memory.py

@@ -30,6 +30,3 @@ class LongTermMemory(Memory):
 
     def search(self, task: str, latest_n: int = 3) -> Dict[str, Any]:
         return self.storage.load(task, latest_n)  # type: ignore # BUG?: "Storage" has no attribute "load"
-
-    def reset(self) -> None:
-        self.storage.reset()

src/crewai/memory/short_term/short_term_memory.py

@@ -18,16 +18,8 @@ class ShortTermMemory(Memory):
         )
         super().__init__(storage)
 
-    def save(self, item: ShortTermMemoryItem) -> None:
+    def save(self, item: ShortTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         super().save(item.data, item.metadata, item.agent)
 
     def search(self, query: str, score_threshold: float = 0.35):
         return self.storage.search(query=query, score_threshold=score_threshold)  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
-
-    def reset(self) -> None:
-        try:
-            self.storage.reset()
-        except Exception as e:
-            raise Exception(
-                f"An error occurred while resetting the short-term memory: {e}"
-            )

src/crewai/memory/storage/interface.py

@@ -9,6 +9,3 @@ class Storage:
 
     def search(self, key: str) -> Dict[str, Any]:  # type: ignore
         pass
-
-    def reset(self) -> None:
-        pass

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -1,166 +0,0 @@
-import json
-import sqlite3
-from typing import Any, Dict, List, Optional
-
-from crewai.task import Task
-from crewai.utilities import Printer
-from crewai.utilities.crew_json_encoder import CrewJSONEncoder
-from crewai.utilities.paths import db_storage_path
-
-
-class KickoffTaskOutputsSQLiteStorage:
-    """
-    An updated SQLite storage class for kickoff task outputs storage.
-    """
-
-    def __init__(
-        self, db_path: str = f"{db_storage_path()}/latest_kickoff_task_outputs.db"
-    ) -> None:
-        self.db_path = db_path
-        self._printer: Printer = Printer()
-        self._initialize_db()
-
-    def _initialize_db(self):
-        """
-        Initializes the SQLite database and creates LTM table
-        """
-        try:
-            with sqlite3.connect(self.db_path) as conn:
-                cursor = conn.cursor()
-                cursor.execute(
-                    """
-                    CREATE TABLE IF NOT EXISTS latest_kickoff_task_outputs (
-                        task_id TEXT PRIMARY KEY,
-                        expected_output TEXT,
-                        output JSON,
-                        task_index INTEGER,
-                        inputs JSON,
-                        was_replayed BOOLEAN,
-                        timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
-                    )
-                """
-                )
-
-                conn.commit()
-        except sqlite3.Error as e:
-            self._printer.print(
-                content=f"SAVING KICKOFF TASK OUTPUTS ERROR: An error occurred during database initialization: {e}",
-                color="red",
-            )
-
-    def add(
-        self,
-        task: Task,
-        output: Dict[str, Any],
-        task_index: int,
-        was_replayed: bool = False,
-        inputs: Dict[str, Any] = {},
-    ):
-        try:
-            with sqlite3.connect(self.db_path) as conn:
-                cursor = conn.cursor()
-                cursor.execute(
-                    """
-                INSERT OR REPLACE INTO latest_kickoff_task_outputs
-                (task_id, expected_output, output, task_index, inputs, was_replayed)
-                VALUES (?, ?, ?, ?, ?, ?)
-            """,
-                    (
-                        str(task.id),
-                        task.expected_output,
-                        json.dumps(output, cls=CrewJSONEncoder),
-                        task_index,
-                        json.dumps(inputs),
-                        was_replayed,
-                    ),
-                )
-                conn.commit()
-        except sqlite3.Error as e:
-            self._printer.print(
-                content=f"SAVING KICKOFF TASK OUTPUTS ERROR: An error occurred during database initialization: {e}",
-                color="red",
-            )
-
-    def update(
-        self,
-        task_index: int,
-        **kwargs,
-    ):
-        """
-        Updates an existing row in the latest_kickoff_task_outputs table based on task_index.
-        """
-        try:
-            with sqlite3.connect(self.db_path) as conn:
-                cursor = conn.cursor()
-
-                fields = []
-                values = []
-                for key, value in kwargs.items():
-                    fields.append(f"{key} = ?")
-                    values.append(
-                        json.dumps(value, cls=CrewJSONEncoder)
-                        if isinstance(value, dict)
-                        else value
-                    )
-
-                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"
-                values.append(task_index)
-
-                cursor.execute(query, tuple(values))
-                conn.commit()
-
-                if cursor.rowcount == 0:
-                    self._printer.print(
-                        f"No row found with task_index {task_index}. No update performed.",
-                        color="red",
-                    )
-        except sqlite3.Error as e:
-            self._printer.print(f"UPDATE KICKOFF TASK OUTPUTS ERROR: {e}", color="red")
-
-    def load(self) -> Optional[List[Dict[str, Any]]]:
-        try:
-            with sqlite3.connect(self.db_path) as conn:
-                cursor = conn.cursor()
-                cursor.execute("""
-                SELECT *
-                FROM latest_kickoff_task_outputs
-                ORDER BY task_index
-                """)
-
-                rows = cursor.fetchall()
-                results = []
-                for row in rows:
-                    result = {
-                        "task_id": row[0],
-                        "expected_output": row[1],
-                        "output": json.loads(row[2]),
-                        "task_index": row[3],
-                        "inputs": json.loads(row[4]),
-                        "was_replayed": row[5],
-                        "timestamp": row[6],
-                    }
-                    results.append(result)
-
-                return results
-
-        except sqlite3.Error as e:
-            self._printer.print(
-                content=f"LOADING KICKOFF TASK OUTPUTS ERROR: An error occurred while querying kickoff task outputs: {e}",
-                color="red",
-            )
-            return None
-
-    def delete_all(self):
-        """
-        Deletes all rows from the latest_kickoff_task_outputs table.
-        """
-        try:
-            with sqlite3.connect(self.db_path) as conn:
-                cursor = conn.cursor()
-                cursor.execute("DELETE FROM latest_kickoff_task_outputs")
-                conn.commit()
-        except sqlite3.Error as e:
-            self._printer.print(
-                content=f"ERROR: Failed to delete all kickoff task outputs: {e}",
-                color="red",
-            )

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -103,20 +103,3 @@ class LTMSQLiteStorage:
                 color="red",
             )
         return None
-
-    def reset(
-        self,
-    ) -> None:
-        """Resets the LTM table with error handling."""
-        try:
-            with sqlite3.connect(self.db_path) as conn:
-                cursor = conn.cursor()
-                cursor.execute("DELETE FROM long_term_memories")
-                conn.commit()
-
-        except sqlite3.Error as e:
-            self._printer.print(
-                content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
-                color="red",
-            )
-        return None

src/crewai/memory/storage/rag_storage.py

@@ -2,7 +2,6 @@ import contextlib
 import io
 import logging
 import os
-import shutil
 from typing import Any, Dict, List, Optional
 
 from embedchain import App
@@ -72,13 +71,13 @@ class RAGStorage(Storage):
 
         if embedder_config:
             config["embedder"] = embedder_config
-        self.type = type
+
         self.app = App.from_config(config=config)
         self.app.llm = FakeLLM()
         if allow_reset:
             self.app.reset()
 
-    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+    def save(self, value: Any, metadata: Dict[str, Any]) -> None:  # type: ignore # BUG?: Should be save(key, value, metadata)  Signature of "save" incompatible with supertype "Storage"
         self._generate_embedding(value, metadata)
 
     def search(  # type: ignore # BUG?: Signature of "search" incompatible with supertype "Storage"
@@ -103,11 +102,3 @@ class RAGStorage(Storage):
     def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> Any:
         with suppress_logging():
             self.app.add(text, data_type="text", metadata=metadata)
-
-    def reset(self) -> None:
-        try:
-            shutil.rmtree(f"{db_storage_path()}/{self.type}")
-        except Exception as e:
-            raise Exception(
-                f"An error occurred while resetting the {self.type} memory: {e}"
-            )

src/crewai/task.py

@@ -1,12 +1,9 @@
-import json
 import os
 import re
 import threading
 import uuid
-from concurrent.futures import Future
 from copy import copy
-from hashlib import md5
-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
@@ -14,10 +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
@@ -101,10 +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
@@ -165,102 +157,91 @@ 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."
 
-    @property
-    def key(self) -> str:
-        description = self._original_description or self.description
-        expected_output = self._original_expected_output or self.expected_output
-        source = [description, expected_output]
+        if self._thread:
+            self._thread.join()
+            self._thread = None
 
-        return md5("|".join(source).encode()).hexdigest()
+        assert self.output, "Task output is not set."
 
-    def execute_async(
+        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."""
         agent = agent or self.agent
-        self.agent = 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:
+            # type: ignore # Incompatible types in assignment (expression has type "list[Never]", variable has type "str | None")
+            context = []
+            for task in self.context:
+                if task.async_execution:
+                    task.wait_for_completion()
+                if task.output:
+                    # type: ignore # Item "str" of "str | None" has no attribute "append"
+                    context.append(task.output.raw_output)
+            # type: ignore # Argument 1 to "join" of "str" has incompatible type "str | None"; expected "Iterable[str]"
+            context = "\n".join(context)
 
         self.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(
+        # type: ignore # the responses are usually str but need to figure out a more elegant solution here
+        self.output = TaskOutput(
             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)
 
         if self._execution_span:
-            self._telemetry.task_ended(self._execution_span, self, agent.crew)
+            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.
@@ -295,7 +276,7 @@ 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":
         """Create a deep copy of the Task."""
         exclude = {
             "id",
@@ -308,7 +289,7 @@ class Task(BaseModel):
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
 
         cloned_context = (
-            [task.copy(agents) for task in self.context] if self.context else None
+            [task.copy() for task in self.context] if self.context else None
         )
 
         def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
@@ -326,128 +307,81 @@ class Task(BaseModel):
 
         return copied_task
 
-    def _create_converter(self, *args, **kwargs) -> Converter:
-        """Create a converter instance."""
-        if self.agent and not self.converter_cls:
-            converter = self.agent.get_output_converter(*args, **kwargs)
-        elif self.converter_cls:
-            converter = self.converter_cls(*args, **kwargs)
-
-        if not converter:
-            raise Exception("No output converter found or set.")
-
-        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))
+                # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
+                exported_result = model.model_validate_json(result)
                 if self.output_json:
+                    # type: ignore # "str" has no attribute "model_dump"
                     return exported_result.model_dump()
                 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:
+                        # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
+                        exported_result = model.model_validate_json(match.group(0))
+                        if self.output_json:
+                            # type: ignore # "str" has no attribute "model_dump"
+                            return exported_result.model_dump()
+                        return exported_result
+                    except Exception:
+                        pass
 
-        return self._convert_with_instructions(result, model)
+            # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
+            llm = getattr(self.agent, "function_calling_llm", None) or self.agent.llm
+            if not self._is_gpt(llm):
+                # type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
+                model_schema = PydanticSchemaParser(model=model).get_schema()
+                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(
+                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 = (
+                # type: ignore # "str" has no attribute "json"
+                exported_result
+                if not self.output_pydantic
+                else exported_result.model_dump_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
 
     def _save_file(self, result: Any) -> None:
-        directory = os.path.dirname(self.output_file)  # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
+        # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
+        directory = os.path.dirname(self.output_file)
 
         if directory and not os.path.exists(directory):
             os.makedirs(directory)
 
-        with open(self.output_file, "w", encoding="utf-8") as file:  # type: ignore # Argument 1 to "open" has incompatible type "str | None"; expected "int | str | bytes | PathLike[str] | PathLike[bytes]"
+        # type: ignore # Argument 1 to "open" has incompatible type "str | None"; expected "int | str | bytes | PathLike[str] | PathLike[bytes]"
+        with open(self.output_file, "w", encoding="utf-8") as file:
             file.write(result)
         return None
 

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/conditional_task.py

@@ -1,47 +0,0 @@
-from typing import Any, Callable
-
-from pydantic import Field
-
-from crewai.task import Task
-from crewai.tasks.output_format import OutputFormat
-from crewai.tasks.task_output import TaskOutput
-
-
-class ConditionalTask(Task):
-    """
-    A task that can be conditionally executed based on the output of another task.
-    Note: This cannot be the only task you have in your crew and cannot be the first since its needs context from the previous task.
-    """
-
-    condition: Callable[[TaskOutput], bool] = Field(
-        default=None,
-        description="Maximum number of retries for an agent to execute a task when an error occurs.",
-    )
-
-    def __init__(
-        self,
-        condition: Callable[[Any], bool],
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.condition = condition
-
-    def should_execute(self, context: TaskOutput) -> bool:
-        """
-        Determines whether the conditional task should be executed based on the provided context.
-
-        Args:
-            context (Any): The context or output from the previous task that will be evaluated by the condition.
-
-        Returns:
-            bool: True if the task should be executed, False otherwise.
-        """
-        return self.condition(context)
-
-    def get_skipped_task_output(self):
-        return TaskOutput(
-            description=self.description,
-            raw="",
-            agent=self.agent.role if self.agent else "",
-            output_format=OutputFormat.RAW,
-        )

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

@@ -1,60 +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 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)
-        elif 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

@@ -80,7 +80,7 @@ class Telemetry:
                 self.ready = False
                 self.trace_set = False
 
-    def crew_creation(self, crew: Crew, inputs: dict[str, Any] | None):
+    def crew_creation(self, crew):
         """Records the creation of a crew."""
         if self.ready:
             try:
@@ -92,7 +92,6 @@ class Telemetry:
                     pkg_resources.get_distribution("crewai").version,
                 )
                 self._add_attribute(span, "python_version", platform.python_version())
-                self._add_attribute(span, "crew_key", crew.key)
                 self._add_attribute(span, "crew_id", str(crew.id))
                 self._add_attribute(span, "crew_process", crew.process)
                 self._add_attribute(span, "crew_memory", crew.memory)
@@ -104,7 +103,6 @@ class Telemetry:
                     json.dumps(
                         [
                             {
-                                "key": agent.key,
                                 "id": str(agent.id),
                                 "role": agent.role,
                                 "goal": agent.goal,
@@ -116,7 +114,7 @@ class Telemetry:
                                 "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
                                 "delegation_enabled?": agent.allow_delegation,
                                 "tools_names": [
-                                    tool.name.casefold() for tool in agent.tools or []
+                                    tool.name.casefold() for tool in agent.tools
                                 ],
                             }
                             for agent in crew.agents
@@ -129,21 +127,19 @@ class Telemetry:
                     json.dumps(
                         [
                             {
-                                "key": task.key,
                                 "id": str(task.id),
                                 "description": task.description,
                                 "expected_output": task.expected_output,
                                 "async_execution?": task.async_execution,
                                 "human_input?": task.human_input,
                                 "agent_role": task.agent.role if task.agent else "None",
-                                "agent_key": task.agent.key if task.agent else None,
                                 "context": (
                                     [task.description for task in task.context]
                                     if task.context
                                     else None
                                 ),
                                 "tools_names": [
-                                    tool.name.casefold() for tool in task.tools or []
+                                    tool.name.casefold() for tool in task.tools
                                 ],
                             }
                             for task in crew.tasks
@@ -155,53 +151,23 @@ class Telemetry:
                 self._add_attribute(span, "platform_system", platform.system())
                 self._add_attribute(span, "platform_version", platform.version())
                 self._add_attribute(span, "cpus", os.cpu_count())
-
-                if crew.share_crew:
-                    self._add_attribute(
-                        span, "crew_inputs", json.dumps(inputs) if inputs else None
-                    )
-
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             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")
-
-                created_span = tracer.start_span("Task Created")
-
-                self._add_attribute(created_span, "crew_key", crew.key)
-                self._add_attribute(created_span, "crew_id", str(crew.id))
-                self._add_attribute(created_span, "task_key", task.key)
-                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()
-
                 span = tracer.start_span("Task Execution")
 
-                self._add_attribute(span, "crew_key", crew.key)
-                self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(span, "task_key", task.key)
                 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:
@@ -209,16 +175,13 @@ class Telemetry:
 
         return None
 
-    def task_ended(self, span: Span, task: Task, crew: Crew):
+    def task_ended(self, span: Span, task: Task):
         """Records task execution in a crew."""
         if self.ready:
             try:
-                if crew.share_crew:
-                    self._add_attribute(
-                        span,
-                        "task_output",
-                        task.output.raw if task.output else "",
-                    )
+                self._add_attribute(
+                    span, "output", task.output.raw_output if task.output else ""
+                )
 
                 span.set_status(Status(StatusCode.OK))
                 span.end()
@@ -293,8 +256,6 @@ class Telemetry:
         """Records the complete execution of a crew.
         This is only collected if the user has opted-in to share the crew.
         """
-        self.crew_creation(crew, inputs)
-
         if (self.ready) and (crew.share_crew):
             try:
                 tracer = trace.get_tracer("crewai.telemetry")
@@ -304,18 +265,14 @@ class Telemetry:
                     "crewai_version",
                     pkg_resources.get_distribution("crewai").version,
                 )
-                self._add_attribute(span, "crew_key", crew.key)
                 self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(
-                    span, "crew_inputs", json.dumps(inputs) if inputs else None
-                )
+                self._add_attribute(span, "inputs", json.dumps(inputs))
                 self._add_attribute(
                     span,
                     "crew_agents",
                     json.dumps(
                         [
                             {
-                                "key": agent.key,
                                 "id": str(agent.id),
                                 "role": agent.role,
                                 "goal": agent.goal,
@@ -346,7 +303,6 @@ class Telemetry:
                                 "async_execution?": task.async_execution,
                                 "human_input?": task.human_input,
                                 "agent_role": task.agent.role if task.agent else "None",
-                                "agent_key": task.agent.key if task.agent else None,
                                 "context": (
                                     [task.description for task in task.context]
                                     if task.context
@@ -364,7 +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(
@@ -372,9 +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/agent_tools.py

@@ -7,7 +7,7 @@ class AgentTools(BaseAgentTools):
     """Default tools around agent delegation"""
 
     def tools(self):
-        coworkers = ", ".join([f"{agent.role}" for agent in self.agents])
+        coworkers = f"[{', '.join([f'{agent.role}' for agent in self.agents])}]"
         tools = [
             StructuredTool.from_function(
                 func=self.delegate_work,

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

@@ -11,10 +11,11 @@ from crewai.telemetry import Telemetry
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.utilities import I18N, Converter, ConverterError, Printer
 
+agentops = None
 try:
     import agentops
 except ImportError:
-    agentops = None
+    pass
 
 OPENAI_BIGGER_MODELS = ["gpt-4"]
 
@@ -50,7 +51,6 @@ class ToolUsage:
         tools_names: str,
         task: Any,
         function_calling_llm: Any,
-        agent: Any,
         action: Any,
     ) -> None:
         self._i18n: I18N = I18N()
@@ -59,7 +59,6 @@ class ToolUsage:
         self._run_attempts: int = 1
         self._max_parsing_attempts: int = 3
         self._remember_format_after_usages: int = 3
-        self.agent = agent
         self.tools_description = tools_description
         self.tools_names = tools_names
         self.tools_handler = tools_handler
@@ -98,7 +97,7 @@ class ToolUsage:
             self.task.increment_tools_errors()
             self._printer.print(content=f"\n\n{error}\n", color="red")
             return error
-        return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}"  # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
+        return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}" # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
 
     def _use(
         self,
@@ -106,8 +105,8 @@ class ToolUsage:
         tool: BaseTool,
         calling: Union[ToolCalling, InstructorToolCalling],
     ) -> str:  # TODO: Fix this return type
-        tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None
-        if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
+        tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None        
+        if self._check_tool_repeated_usage(calling=calling): # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
@@ -118,24 +117,20 @@ class ToolUsage:
                     tool_name=tool.name,
                     attempts=self._run_attempts,
                 )
-                result = self._format_result(result=result)  # type: ignore #  "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
-                return result  # type: ignore # Fix the return type of this function
+                result = self._format_result(result=result) # type: ignore #  "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
+                return result  # type: ignore # Fix the reutrn type of this function
 
             except Exception:
                 self.task.increment_tools_errors()
 
-        result = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
+        result = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
 
         if self.tools_handler.cache:
             result = self.tools_handler.cache.read(  # type: ignore # Incompatible types in assignment (expression has type "str | None", variable has type "str")
                 tool=calling.tool_name, input=calling.arguments
             )
 
-        original_tool = next(
-            (ot for ot in self.original_tools if ot.name == tool.name), None
-        )
-
-        if result is None:  #! finecwg: if not result --> if result is None
+        if result is None: #! finecwg: if not result --> if result is None
             try:
                 if calling.tool_name in [
                     "Delegate work to coworker",
@@ -145,18 +140,22 @@ class ToolUsage:
 
                 if calling.arguments:
                     try:
-                        acceptable_args = tool.args_schema.schema()["properties"].keys()  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
+                        acceptable_args = tool.args_schema.schema()["properties"].keys() # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
                         arguments = {
                             k: v
                             for k, v in calling.arguments.items()
                             if k in acceptable_args
                         }
-                        result = tool.invoke(input=arguments)
+                        result = tool._run(**arguments)
                     except Exception:
-                        arguments = calling.arguments
-                        result = tool.invoke(input=arguments)
+                        if tool.args_schema:
+                            arguments = calling.arguments
+                            result = tool._run(**arguments)
+                        else:
+                            arguments = calling.arguments.values() # type: ignore # Incompatible types in assignment (expression has type "dict_values[str, Any]", variable has type "dict[str, Any]")
+                            result = tool._run(*arguments)
                 else:
-                    result = tool.invoke(input={})
+                    result = tool._run()
             except Exception as e:
                 self._run_attempts += 1
                 if self._run_attempts > self._max_parsing_attempts:
@@ -180,6 +179,9 @@ class ToolUsage:
 
             if self.tools_handler:
                 should_cache = True
+                original_tool = next(
+                    (ot for ot in self.original_tools if ot.name == tool.name), None
+                )
                 if (
                     hasattr(original_tool, "cache_function")
                     and original_tool.cache_function  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
@@ -199,29 +201,14 @@ class ToolUsage:
             llm=self.function_calling_llm,
             tool_name=tool.name,
             attempts=self._run_attempts,
-        )
-        result = self._format_result(result=result)  # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
-        data = {
-            "result": result,
-            "tool_name": tool.name,
-            "tool_args": calling.arguments,
-        }
-
-        if (
-            hasattr(original_tool, "result_as_answer")
-            and original_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
-        ):
-            result_as_answer = original_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
-            data["result_as_answer"] = result_as_answer
-
-        self.agent.tools_results.append(data)
-
+        )        
+        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 # No return value expected
 
     def _format_result(self, result: Any) -> None:
         self.task.used_tools += 1
         if self._should_remember_format():  # type: ignore # "_should_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
-            result = self._remember_format(result=result)  # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
+            result = self._remember_format(result=result) # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
         return result
 
     def _should_remember_format(self) -> None:
@@ -316,7 +303,7 @@ class ToolUsage:
               Example:
               {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
                     ),
-                    max_attempts=1,
+                    max_attemps=1,
                 )
                 calling = converter.to_pydantic()
 

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/converter.py

@@ -2,8 +2,10 @@ import json
 
 from langchain.schema import HumanMessage, SystemMessage
 from langchain_openai import ChatOpenAI
-
-from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
+from pydantic import model_validator
+from crewai.agents.agent_builder.utilities.base_output_converter_base import (
+    OutputConverter,
+)
 
 
 class ConverterError(Exception):
@@ -17,15 +19,20 @@ class ConverterError(Exception):
 class Converter(OutputConverter):
     """Class that converts text into either pydantic or json."""
 
+    @model_validator(mode="after")
+    def check_llm_provider(self):
+        if not self._is_gpt(self.llm):
+            self._is_gpt = False
+
     def to_pydantic(self, current_attempt=1):
         """Convert text to pydantic."""
         try:
-            if self.is_gpt:
+            if self._is_gpt:
                 return self._create_instructor().to_pydantic()
             else:
                 return self._create_chain().invoke({})
         except Exception as e:
-            if current_attempt < self.max_attempts:
+            if current_attempt < self.max_attemps:
                 return self.to_pydantic(current_attempt + 1)
             return ConverterError(
                 f"Failed to convert text into a pydantic model due to the following error: {e}"
@@ -34,14 +41,14 @@ class Converter(OutputConverter):
     def to_json(self, current_attempt=1):
         """Convert text to json."""
         try:
-            if self.is_gpt:
+            if self._is_gpt:
                 return self._create_instructor().to_json()
             else:
                 return json.dumps(self._create_chain().invoke({}).model_dump())
-        except Exception as e:
-            if current_attempt < self.max_attempts:
+        except Exception:
+            if current_attempt < self.max_attemps:
                 return self.to_json(current_attempt + 1)
-            return ConverterError(f"Failed to convert text into JSON, error: {e}.")
+            return ConverterError("Failed to convert text into JSON.")
 
     def _create_instructor(self):
         """Create an instructor."""
@@ -49,7 +56,7 @@ class Converter(OutputConverter):
 
         inst = Instructor(
             llm=self.llm,
-            max_attempts=self.max_attempts,
+            max_attemps=self.max_attemps,
             model=self.model,
             content=self.text,
             instructions=self.instructions,
@@ -68,7 +75,5 @@ class Converter(OutputConverter):
         )
         return new_prompt | self.llm | parser
 
-    @property
-    def is_gpt(self) -> bool:
-        """Return if llm provided is of gpt from openai."""
-        return isinstance(self.llm, ChatOpenAI) and self.llm.openai_api_base is None
+    def _is_gpt(self, llm) -> bool:  # type: ignore # BUG? Name "_is_gpt" defined on line 20 hides name from outer scope
+        return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None

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/crew_pydantic_output_parser.py

@@ -17,16 +17,6 @@ class CrewPydanticOutputParser(PydanticOutputParser):
 
     def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
         result[0].text = self._transform_in_valid_json(result[0].text)
-
-        # Treating edge case of function calling llm returning the name instead of tool_name
-        json_object = json.loads(result[0].text)
-        json_object["tool_name"] = (
-            json_object["name"]
-            if "tool_name" not in json_object
-            else json_object["tool_name"]
-        )
-        result[0].text = json.dumps(json_object)
-
         json_object = super().parse_result(result)
         try:
             return self.pydantic_object.parse_obj(json_object)

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,149 +0,0 @@
-from collections import defaultdict
-
-from langchain_openai import ChatOpenAI
-from pydantic import BaseModel, Field
-from rich.console import Console
-from rich.table import Table
-
-from crewai.agent import Agent
-from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
-
-
-class TaskEvaluationPydanticOutput(BaseModel):
-    quality: float = Field(
-        description="A score from 1 to 10 evaluating on completion, quality, and overall performance from the task_description and task_expected_output to the actual Task Output."
-    )
-
-
-class CrewEvaluator:
-    """
-    A class to evaluate the performance of the agents in the crew based on the tasks they have performed.
-
-    Attributes:
-        crew (Crew): The crew of agents to evaluate.
-        openai_model_name (str): The model to use for evaluating the performance of the agents (for now ONLY OpenAI accepted).
-        tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task.
-        iteration (int): The current iteration of the evaluation.
-    """
-
-    tasks_scores: defaultdict = defaultdict(list)
-    iteration: int = 0
-
-    def __init__(self, crew, openai_model_name: str):
-        self.crew = crew
-        self.openai_model_name = openai_model_name
-        self._setup_for_evaluating()
-
-    def _setup_for_evaluating(self) -> None:
-        """Sets up the crew for evaluating."""
-        for task in self.crew.tasks:
-            task.callback = self.evaluate
-
-    def set_iteration(self, iteration: int) -> None:
-        self.iteration = iteration
-
-    def _evaluator_agent(self):
-        return Agent(
-            role="Task Execution Evaluator",
-            goal=(
-                "Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance."
-            ),
-            backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
-            verbose=False,
-            llm=ChatOpenAI(model=self.openai_model_name),
-        )
-
-    def _evaluation_task(
-        self, evaluator_agent: Agent, task_to_evaluate: Task, task_output: str
-    ) -> Task:
-        return Task(
-            description=(
-                "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance."
-                f"task_description: {task_to_evaluate.description} "
-                f"task_expected_output: {task_to_evaluate.expected_output} "
-                f"agent: {task_to_evaluate.agent.role if task_to_evaluate.agent else None} "
-                f"agent_goal: {task_to_evaluate.agent.goal if task_to_evaluate.agent else None} "
-                f"Task Output: {task_output}"
-            ),
-            expected_output="Evaluation Score from 1 to 10 based on the performance of the agents on the tasks",
-            agent=evaluator_agent,
-            output_pydantic=TaskEvaluationPydanticOutput,
-        )
-
-    def print_crew_evaluation_result(self) -> None:
-        """
-        Prints the evaluation result of the crew in a table.
-        A Crew with 2 tasks using the command crewai test -n 2
-        will output the following table:
-
-                        Task Scores
-                    (1-10 Higher is better)
-            ┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
-            ┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
-            ┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
-            │ Task 1     │ 10.0  │ 9.0   │ 9.5        │
-            │ Task 2     │ 9.0   │ 9.0   │ 9.0        │
-            │ Crew       │ 9.5   │ 9.0   │ 9.2        │
-            └────────────┴───────┴───────┴────────────┘
-        """
-        task_averages = [
-            sum(scores) / len(scores) for scores in zip(*self.tasks_scores.values())
-        ]
-        crew_average = sum(task_averages) / len(task_averages)
-
-        # Create a table
-        table = Table(title="Tasks Scores \n (1-10 Higher is better)")
-
-        # Add columns for the table
-        table.add_column("Tasks/Crew")
-        for run in range(1, len(self.tasks_scores) + 1):
-            table.add_column(f"Run {run}")
-        table.add_column("Avg. Total")
-
-        # Add rows for each task
-        for task_index in range(len(task_averages)):
-            task_scores = [
-                self.tasks_scores[run][task_index]
-                for run in range(1, len(self.tasks_scores) + 1)
-            ]
-            avg_score = task_averages[task_index]
-            table.add_row(
-                f"Task {task_index + 1}", *map(str, task_scores), f"{avg_score:.1f}"
-            )
-
-        # Add a row for the crew average
-        crew_scores = [
-            sum(self.tasks_scores[run]) / len(self.tasks_scores[run])
-            for run in range(1, len(self.tasks_scores) + 1)
-        ]
-        table.add_row("Crew", *map(str, crew_scores), f"{crew_average:.1f}")
-
-        # Display the table in the terminal
-        console = Console()
-        console.print(table)
-
-    def evaluate(self, task_output: TaskOutput):
-        """Evaluates the performance of the agents in the crew based on the tasks they have performed."""
-        current_task = None
-        for task in self.crew.tasks:
-            if task.description == task_output.description:
-                current_task = task
-                break
-
-        if not current_task or not task_output:
-            raise ValueError(
-                "Task to evaluate and task output are required for evaluation"
-            )
-
-        evaluator_agent = self._evaluator_agent()
-        evaluation_task = self._evaluation_task(
-            evaluator_agent, current_task, task_output.raw
-        )
-
-        evaluation_result = evaluation_task.execute_sync()
-
-        if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput):
-            self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
-        else:
-            raise ValueError("Evaluation result is not in the expected format")

src/crewai/utilities/file_handler.py

@@ -1,7 +1,5 @@
 import os
 import pickle
-
-
 from datetime import datetime
 
 
@@ -33,8 +31,9 @@ class PickleHandler:
         - file_name (str): The name of the file for saving and loading data.
         """
         self.file_path = os.path.join(os.getcwd(), file_name)
+        self._initialize_file()
 
-    def initialize_file(self) -> None:
+    def _initialize_file(self) -> None:
         """
         Initialize the file with an empty dictionary if it does not exist or is empty.
         """

src/crewai/utilities/formatter.py

@@ -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/planning_handler.py

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

src/crewai/utilities/printer.py

@@ -8,10 +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)
-        elif color == "yellow":
-            self._print_yellow(content)
         else:
             print(content)
 
@@ -26,9 +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))
-
-    def _print_yellow(self, content):
-        print("\033[93m {}\033[00m".format(content))

src/crewai/utilities/task_output_storage_handler.py

@@ -1,61 +0,0 @@
-from pydantic import BaseModel, Field
-from datetime import datetime
-from typing import Dict, Any, Optional, List
-from crewai.memory.storage.kickoff_task_outputs_storage import (
-    KickoffTaskOutputsSQLiteStorage,
-)
-from crewai.task import Task
-
-
-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 TaskOutputStorageHandler:
-    def __init__(self) -> None:
-        self.storage = KickoffTaskOutputsSQLiteStorage()
-
-    def update(self, task_index: int, log: Dict[str, Any]):
-        saved_outputs = self.load()
-        if saved_outputs is None:
-            raise ValueError("Logs cannot be None")
-
-        if log.get("was_replayed", False):
-            replayed = {
-                "task_id": str(log["task"].id),
-                "expected_output": log["task"].expected_output,
-                "output": log["output"],
-                "was_replayed": log["was_replayed"],
-                "inputs": log["inputs"],
-            }
-            self.storage.update(
-                task_index,
-                **replayed,
-            )
-        else:
-            self.storage.add(**log)
-
-    def add(
-        self,
-        task: Task,
-        output: Dict[str, Any],
-        task_index: int,
-        inputs: Dict[str, Any] = {},
-        was_replayed: bool = False,
-    ):
-        self.storage.add(task, output, task_index, was_replayed, inputs)
-
-    def reset(self):
-        self.storage.delete_all()
-
-    def load(self) -> Optional[List[Dict[str, Any]]]:
-        return self.storage.load()

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."
 
@@ -724,74 +723,6 @@ def test_agent_count_formatting_error():
         mock_count_errors.assert_called_once()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
-    from crewai_tools import BaseTool
-
-    class MyCustomTool(BaseTool):
-        name: str = "Get Greetings"
-        description: str = "Get a random greeting back"
-
-        def _run(self) -> str:
-            return "Howdy!"
-
-    agent1 = Agent(
-        role="Data Scientist",
-        goal="Product amazing resports on AI",
-        backstory="You work with data and AI",
-        tools=[MyCustomTool(result_as_answer=True)],
-    )
-
-    essay = Task(
-        description="Write and then review an small paragraph on AI until it's AMAZING. But first use the `Get Greetings` tool to get a greeting.",
-        expected_output="The final paragraph with the full review on AI and no greeting.",
-        agent=agent1,
-    )
-    tasks = [essay]
-    crew = Crew(agents=[agent1], tasks=tasks)
-
-    result = crew.kickoff()
-    print("RESULT: ", result.raw)
-    assert result.raw == "Howdy!"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_tool_usage_information_is_appended_to_agent():
-    from crewai_tools import BaseTool
-
-    class MyCustomTool(BaseTool):
-        name: str = "Decide Greetings"
-        description: str = "Decide what is the appropriate greeting to use"
-
-        def _run(self) -> str:
-            return "Howdy!"
-
-    agent1 = Agent(
-        role="Friendly Neighbor",
-        goal="Make everyone feel welcome",
-        backstory="You are the friendly neighbor",
-        tools=[MyCustomTool(result_as_answer=True)],
-    )
-
-    greeting = Task(
-        description="Say an appropriate greeting.",
-        expected_output="The greeting.",
-        agent=agent1,
-    )
-    tasks = [greeting]
-    crew = Crew(agents=[agent1], tasks=tasks)
-
-    crew.kickoff()
-    assert agent1.tools_results == [
-        {
-            "result": "Howdy!",
-            "tool_name": "Decide Greetings",
-            "tool_args": {},
-            "result_as_answer": True,
-        }
-    ]
-
-
 def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
     agent1 = Agent(
         role="test role",
@@ -963,54 +894,3 @@ def test_agent_use_trained_data(crew_training_handler):
     crew_training_handler.assert_has_calls(
         [mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
     )
-
-
-def test_agent_max_retry_limit():
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        max_retry_limit=1,
-    )
-
-    task = Task(
-        agent=agent,
-        description="Say the word: Hi",
-        expected_output="The word: Hi",
-        human_input=True,
-    )
-
-    error_message = "Error happening while sending prompt to model."
-    with patch.object(
-        CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
-    ) as invoke_mock:
-        invoke_mock.side_effect = Exception(error_message)
-
-        assert agent._times_executed == 0
-        assert agent.max_retry_limit == 1
-
-        with pytest.raises(Exception) as e:
-            agent.execute_task(
-                task=task,
-            )
-        assert e.value.args[0] == error_message
-        assert agent._times_executed == 2
-
-        invoke_mock.assert_has_calls(
-            [
-                mock.call(
-                    {
-                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi \n you MUST return the actual complete content as the final answer, not a summary.",
-                        "tool_names": "",
-                        "tools": "",
-                    }
-                ),
-                mock.call(
-                    {
-                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi \n you MUST return the actual complete content as the final answer, not a summary.",
-                        "tool_names": "",
-                        "tools": "",
-                    }
-                ),
-            ]
-        )

tests/agents/agent_builder/base_agent_test.py

@@ -1,36 +0,0 @@
-import hashlib
-from typing import Any, List, Optional
-
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from pydantic import BaseModel
-
-
-class TestAgent(BaseAgent):
-    def execute_task(
-        self,
-        task: Any,
-        context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
-    ) -> str:
-        return ""
-
-    def create_agent_executor(self, tools=None) -> None: ...
-
-    def _parse_tools(self, tools: List[Any]) -> List[Any]:
-        return []
-
-    def get_delegation_tools(self, agents: List["BaseAgent"]): ...
-
-    def get_output_converter(
-        self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
-    ): ...
-
-
-def test_key():
-    agent = TestAgent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-    )
-    hash = hashlib.md5("test role|test goal|test backstory".encode()).hexdigest()
-    assert agent.key == hash

tests/agents/test_crew_agent_parser.py

@@ -1,378 +0,0 @@
-import pytest
-from crewai.agents.parser import CrewAgentParser
-from langchain_core.agents import AgentAction, AgentFinish
-from langchain_core.exceptions import OutputParserException
-
-
-@pytest.fixture
-def parser():
-    p = CrewAgentParser()
-    p.agent = MockAgent()
-    return p
-
-
-def test_valid_action_parsing_special_characters(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: what's the temperature in SF?"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what's the temperature in SF?"
-
-
-def test_valid_action_parsing_with_json_tool_input(parser):
-    text = """
-    Thought: Let's find the information
-    Action: query
-    Action Input: ** {"task": "What are some common challenges or barriers that you have observed or experienced when implementing AI-powered solutions in healthcare settings?", "context": "As we've discussed recent advancements in AI applications in healthcare, it's crucial to acknowledge the potential hurdles. Some possible obstacles include...", "coworker": "Senior Researcher"}
-    """
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    expected_tool_input = '{"task": "What are some common challenges or barriers that you have observed or experienced when implementing AI-powered solutions in healthcare settings?", "context": "As we\'ve discussed recent advancements in AI applications in healthcare, it\'s crucial to acknowledge the potential hurdles. Some possible obstacles include...", "coworker": "Senior Researcher"}'
-    assert result.tool == "query"
-    assert result.tool_input == expected_tool_input
-
-
-def test_valid_action_parsing_with_quotes(parser):
-    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "temperature in SF"'
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "temperature in SF"
-
-
-def test_valid_action_parsing_with_curly_braces(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: {temperature in SF}"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "{temperature in SF}"
-
-
-def test_valid_action_parsing_with_angle_brackets(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: <temperature in SF>"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "<temperature in SF>"
-
-
-def test_valid_action_parsing_with_parentheses(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: (temperature in SF)"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "(temperature in SF)"
-
-
-def test_valid_action_parsing_with_mixed_brackets(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: [temperature in {SF}]"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "[temperature in {SF}]"
-
-
-def test_valid_action_parsing_with_nested_quotes(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what's the temperature in 'SF'?\""
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what's the temperature in 'SF'?"
-
-
-def test_valid_action_parsing_with_incomplete_json(parser):
-    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: {"query": "temperature in SF"'
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == '{"query": "temperature in SF"}'
-
-
-def test_valid_action_parsing_with_special_characters(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is the temperature in SF? @$%^&*"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what is the temperature in SF? @$%^&*"
-
-
-def test_valid_action_parsing_with_combination(parser):
-    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "[what is the temperature in SF?]"'
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "[what is the temperature in SF?]"
-
-
-def test_valid_action_parsing_with_mixed_quotes(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what's the temperature in SF?\""
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what's the temperature in SF?"
-
-
-def test_valid_action_parsing_with_newlines(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is\nthe temperature in SF?"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what is\nthe temperature in SF?"
-
-
-def test_valid_action_parsing_with_escaped_characters(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is the temperature in SF? \\n"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what is the temperature in SF? \\n"
-
-
-def test_valid_action_parsing_with_json_string(parser):
-    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: {"query": "temperature in SF"}'
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == '{"query": "temperature in SF"}'
-
-
-def test_valid_action_parsing_with_unbalanced_quotes(parser):
-    text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what is the temperature in SF?"
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what is the temperature in SF?"
-
-
-def test_clean_action_no_formatting(parser):
-    action = "Ask question to senior researcher"
-    cleaned_action = parser._clean_action(action)
-    assert cleaned_action == "Ask question to senior researcher"
-
-
-def test_clean_action_with_leading_asterisks(parser):
-    action = "** Ask question to senior researcher"
-    cleaned_action = parser._clean_action(action)
-    assert cleaned_action == "Ask question to senior researcher"
-
-
-def test_clean_action_with_trailing_asterisks(parser):
-    action = "Ask question to senior researcher **"
-    cleaned_action = parser._clean_action(action)
-    assert cleaned_action == "Ask question to senior researcher"
-
-
-def test_clean_action_with_leading_and_trailing_asterisks(parser):
-    action = "** Ask question to senior researcher **"
-    cleaned_action = parser._clean_action(action)
-    assert cleaned_action == "Ask question to senior researcher"
-
-
-def test_clean_action_with_multiple_leading_asterisks(parser):
-    action = "**** Ask question to senior researcher"
-    cleaned_action = parser._clean_action(action)
-    assert cleaned_action == "Ask question to senior researcher"
-
-
-def test_clean_action_with_multiple_trailing_asterisks(parser):
-    action = "Ask question to senior researcher ****"
-    cleaned_action = parser._clean_action(action)
-    assert cleaned_action == "Ask question to senior researcher"
-
-
-def test_clean_action_with_spaces_and_asterisks(parser):
-    action = "  **  Ask question to senior researcher  **  "
-    cleaned_action = parser._clean_action(action)
-    print(f"Original action: '{action}'")
-    print(f"Cleaned action: '{cleaned_action}'")
-    assert cleaned_action == "Ask question to senior researcher"
-
-
-def test_clean_action_with_only_asterisks(parser):
-    action = "****"
-    cleaned_action = parser._clean_action(action)
-    assert cleaned_action == ""
-
-
-def test_clean_action_with_empty_string(parser):
-    action = ""
-    cleaned_action = parser._clean_action(action)
-    assert cleaned_action == ""
-
-
-def test_valid_final_answer_parsing(parser):
-    text = (
-        "Thought: I found the information\nFinal Answer: The temperature is 100 degrees"
-    )
-    result = parser.parse(text)
-    assert isinstance(result, AgentFinish)
-    assert result.return_values["output"] == "The temperature is 100 degrees"
-
-
-def test_missing_action_error(parser):
-    text = "Thought: Let's find the temperature\nAction Input: what is the temperature in SF?"
-    with pytest.raises(OutputParserException) as exc_info:
-        parser.parse(text)
-    assert "Could not parse LLM output" in str(exc_info.value)
-
-
-def test_missing_action_input_error(parser):
-    text = "Thought: Let's find the temperature\nAction: search"
-    with pytest.raises(OutputParserException) as exc_info:
-        parser.parse(text)
-    assert "Could not parse LLM output" in str(exc_info.value)
-
-
-def test_action_and_final_answer_error(parser):
-    text = "Thought: I found the information\nAction: search\nAction Input: what is the temperature in SF?\nFinal Answer: The temperature is 100 degrees"
-    with pytest.raises(OutputParserException) as exc_info:
-        parser.parse(text)
-    assert "both perform Action and give a Final Answer" in str(exc_info.value)
-
-
-def test_safe_repair_json(parser):
-    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": Senior Researcher'
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_unrepairable(parser):
-    invalid_json = "{invalid_json"
-    result = parser._safe_repair_json(invalid_json)
-    print("result:", invalid_json)
-    assert result == invalid_json  # Should return the original if unrepairable
-
-
-def test_safe_repair_json_missing_quotes(parser):
-    invalid_json = (
-        '{task: "Research XAI", context: "Explainable AI", coworker: Senior Researcher}'
-    )
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_unclosed_brackets(parser):
-    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"'
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_extra_commas(parser):
-    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher",}'
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_trailing_commas(parser):
-    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher",}'
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_single_quotes(parser):
-    invalid_json = "{'task': 'Research XAI', 'context': 'Explainable AI', 'coworker': 'Senior Researcher'}"
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_mixed_quotes(parser):
-    invalid_json = "{'task': \"Research XAI\", 'context': \"Explainable AI\", 'coworker': 'Senior Researcher'}"
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_unescaped_characters(parser):
-    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher\n"}'
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    print("result:", result)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_missing_colon(parser):
-    invalid_json = '{"task" "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_missing_comma(parser):
-    invalid_json = '{"task": "Research XAI" "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_unexpected_trailing_characters(parser):
-    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"} random text'
-    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_safe_repair_json_special_characters_key(parser):
-    invalid_json = '{"task!@#": "Research XAI", "context$%^": "Explainable AI", "coworker&*()": "Senior Researcher"}'
-    expected_repaired_json = '{"task!@#": "Research XAI", "context$%^": "Explainable AI", "coworker&*()": "Senior Researcher"}'
-    result = parser._safe_repair_json(invalid_json)
-    assert result == expected_repaired_json
-
-
-def test_parsing_with_whitespace(parser):
-    text = " Thought: Let's find the temperature \n Action: search \n Action Input: what is the temperature in SF? "
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what is the temperature in SF?"
-
-
-def test_parsing_with_special_characters(parser):
-    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "what is the temperature in SF?"'
-    result = parser.parse(text)
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what is the temperature in SF?"
-
-
-def test_integration_valid_and_invalid(parser):
-    text = """
-    Thought: Let's find the temperature
-    Action: search
-    Action Input: what is the temperature in SF?
-
-    Thought: I found the information
-    Final Answer: The temperature is 100 degrees
-
-    Thought: Missing action
-    Action Input: invalid
-
-    Thought: Missing action input
-    Action: invalid
-    """
-    parts = text.strip().split("\n\n")
-    results = []
-    for part in parts:
-        try:
-            result = parser.parse(part.strip())
-            results.append(result)
-        except OutputParserException as e:
-            results.append(e)
-
-    assert isinstance(results[0], AgentAction)
-    assert isinstance(results[1], AgentFinish)
-    assert isinstance(results[2], OutputParserException)
-    assert isinstance(results[3], OutputParserException)
-
-
-class MockAgent:
-    def increment_formatting_errors(self):
-        pass
-
-
-# TODO: ADD TEST TO MAKE SURE ** REMOVAL DOESN'T MESS UP ANYTHING

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