feat: fix test

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,35 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve CrewAI
-title: "[BUG]"
-labels: bug
-assignees: ''
-
----
-
-**Description**
-Provide a clear and concise description of what the bug is.
-
-**Steps to Reproduce**
-Provide a step-by-step process to reproduce the behavior:
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots/Code snippets**
-If applicable, add screenshots or code snippets to help explain your problem.
-
-**Environment Details:**
-- **Operating System**: [e.g., Ubuntu 20.04, macOS Catalina, Windows 10]
-- **Python Version**: [e.g., 3.8, 3.9, 3.10]
-- **crewAI Version**: [e.g., 0.30.11]
-- **crewAI Tools Version**: [e.g., 0.2.6]
-
-**Logs**
-Include relevant logs or error messages if applicable.
-
-**Possible Solution**
-Have a solution in mind? Please suggest it here, or write "None".
-
-**Additional context**
-Add any other context about the problem here.

.github/ISSUE_TEMPLATE/custom.md

@@ -1,24 +0,0 @@
----
-name: Custom issue template
-about: Describe this issue template's purpose here.
-title: "[DOCS]"
-labels: documentation
-assignees: ''
-
----
-
-## Documentation Page
-<!-- Provide a link to the documentation page that needs improvement -->
-
-## Description
-<!-- Describe what needs to be changed or improved in the documentation -->
-
-## Suggested Changes
-<!-- If possible, provide specific suggestions for how to improve the documentation -->
-
-## Additional Context
-<!-- Add any other context about the documentation issue here -->
-
-## Checklist
-- [ ] I have searched the existing issues to make sure this is not a duplicate
-- [ ] I have checked the latest version of the documentation to ensure this hasn't been addressed

.github/workflows/stale.yml

@@ -1,26 +0,0 @@
-name: Mark stale issues and pull requests
-
-on:
-  schedule:
-    - cron: '10 12 * * *'
-  workflow_dispatch:
-
-jobs:
-  stale:
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-    - uses: actions/stale@v9
-      with:
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        stale-issue-label: 'no-issue-activity'
-        stale-issue-message: 'This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
-        close-issue-message: 'This issue was closed because it has been stalled for 5 days with no activity.'
-        days-before-issue-stale: 30
-        days-before-issue-close: 5
-        stale-pr-label: 'no-pr-activity'
-        stale-pr-message: 'This PR is stale because it has been open for 45 days with no activity.'
-        days-before-pr-stale: 45
-        days-before-pr-close: -1

README.md

@@ -126,7 +126,7 @@ task2 = Task(
 crew = Crew(
   agents=[researcher, writer],
   tasks=[task1, task2],
-  verbose=True,
+  verbose=2, # You can set it to 1 or 2 to different logging levels
   process = Process.sequential
 )
 
@@ -254,7 +254,7 @@ pip install dist/*.tar.gz
 
 CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
 
-It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. We don't offer a way to disable it now, but we will in the future.
+There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
 
 Data collected includes:
 
@@ -279,7 +279,7 @@ Data collected includes:
 - Tools names available
   - Understand out of the publically available tools, which ones are being used the most so we can improve them
 
-Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
+Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.
 
 ## License
 

docs/core-concepts/Agents.md

@@ -114,7 +114,7 @@ from langchain.agents import load_tools
 langchain_tools = load_tools(["google-serper"], llm=llm)
 
 agent1 = CustomAgent(
-    role="agent role",
+    role="backstory agent",
     goal="who is {input}?",
     backstory="agent backstory",
     verbose=True,
@@ -127,7 +127,7 @@ task1 = Task(
 )
 
 agent2 = Agent(
-    role="agent role",
+    role="bio agent",
     goal="summarize the short bio for {input} and if needed do more research",
     backstory="agent backstory",
     verbose=True,

docs/core-concepts/Crews.md

@@ -33,7 +33,6 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **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.
-| **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process. |
 
 !!! 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.
@@ -134,10 +133,10 @@ Once a crew has been executed, its output can be accessed through the `output` a
 crew = Crew(
     agents=[research_agent, writer_agent],
     tasks=[research_task, write_article_task],
-    verbose=True
+    verbose=2
 )
 
-crew_output = crew.kickoff()
+result = crew.kickoff()
 
 # Accessing the crew output
 print(f"Raw Output: {crew_output.raw}")

docs/core-concepts/Pipeline.md

@@ -1,267 +0,0 @@
----
-title: crewAI Pipelines
-description: Understanding and utilizing pipelines in the crewAI framework for efficient multi-stage task processing.
----
-
-## What is a Pipeline?
-
-A pipeline in crewAI represents a structured workflow that allows for the sequential or parallel execution of multiple crews. It provides a way to organize complex processes involving multiple stages, where the output of one stage can serve as input for subsequent stages.
-
-## Key Terminology
-
-Understanding the following terms is crucial for working effectively with pipelines:
-
-- **Stage**: A distinct part of the pipeline, which can be either sequential (a single crew) or parallel (multiple crews executing concurrently).
-- **Run**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
-- **Branch**: Parallel executions within a stage (e.g., concurrent crew operations).
-- **Trace**: The journey of an individual input through the entire pipeline, capturing the path and transformations it undergoes.
-
-Example pipeline structure:
-
-```
-crew1 >> [crew2, crew3] >> crew4
-```
-
-This represents a pipeline with three stages:
-
-1. A sequential stage (crew1)
-2. A parallel stage with two branches (crew2 and crew3 executing concurrently)
-3. Another sequential stage (crew4)
-
-Each input creates its own run, flowing through all stages of the pipeline. Multiple runs can be processed concurrently, each following the defined pipeline structure.
-
-## Pipeline Attributes
-
-| Attribute  | Parameters | Description                                                                           |
-| :--------- | :--------- | :------------------------------------------------------------------------------------ |
-| **Stages** | `stages`   | A list of crews or lists of crews representing the stages to be executed in sequence. |
-
-## Creating a Pipeline
-
-When creating a pipeline, you define a series of stages, each consisting of either a single crew or a list of crews for parallel execution. The pipeline ensures that each stage is executed in order, with the output of one stage feeding into the next.
-
-### Example: Assembling a Pipeline
-
-```python
-from crewai import Crew, Agent, Task, Pipeline
-
-# Define your crews
-research_crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    process=Process.sequential
-)
-
-analysis_crew = Crew(
-    agents=[analyst],
-    tasks=[analysis_task],
-    process=Process.sequential
-)
-
-writing_crew = Crew(
-    agents=[writer],
-    tasks=[writing_task],
-    process=Process.sequential
-)
-
-# Assemble the pipeline
-my_pipeline = Pipeline(
-    stages=[research_crew, analysis_crew, writing_crew]
-)
-```
-
-## Pipeline Methods
-
-| Method           | Description                                                                                                                                                                    |
-| :--------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **process_runs** | Executes the pipeline, processing all stages and returning the results. This method initiates one or more runs through the pipeline, handling the flow of data between stages. |
-
-## Pipeline Output
-
-!!! note "Understanding Pipeline Outputs"
-The output of a pipeline in the crewAI framework is encapsulated within two main classes: `PipelineOutput` and `PipelineRunResult`. These classes provide a structured way to access the results of the pipeline's execution, including various formats such as raw strings, JSON, and Pydantic models.
-
-### Pipeline Output Attributes
-
-| Attribute       | Parameters    | Type                      | Description                                                                                               |
-| :-------------- | :------------ | :------------------------ | :-------------------------------------------------------------------------------------------------------- |
-| **ID**          | `id`          | `UUID4`                   | A unique identifier for the pipeline output.                                                              |
-| **Run Results** | `run_results` | `List[PipelineRunResult]` | A list of `PipelineRunResult` objects, each representing the output of a single run through the pipeline. |
-
-### Pipeline Output Methods
-
-| Method/Property    | Description                                            |
-| :----------------- | :----------------------------------------------------- |
-| **add_run_result** | Adds a `PipelineRunResult` to the list of run results. |
-
-### Pipeline Run Result Attributes
-
-| Attribute         | Parameters      | Type                       | Description                                                                                   |
-| :---------------- | :-------------- | :------------------------- | :-------------------------------------------------------------------------------------------- |
-| **ID**            | `id`            | `UUID4`                    | A unique identifier for the run result.                                                       |
-| **Raw**           | `raw`           | `str`                      | The raw output of the final stage in the pipeline run.                                        |
-| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the final stage, if applicable. |
-| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the final stage, if applicable.                  |
-| **Token Usage**   | `token_usage`   | `Dict[str, Any]`           | A summary of token usage across all stages of the pipeline run.                               |
-| **Trace**         | `trace`         | `List[Any]`                | A trace of the journey of inputs through the pipeline run.                                    |
-| **Crews Outputs** | `crews_outputs` | `List[CrewOutput]`         | A list of `CrewOutput` objects, representing the outputs from each crew in the pipeline run.  |
-
-### Pipeline Run Result Methods and Properties
-
-| Method/Property | Description                                                                                              |
-| :-------------- | :------------------------------------------------------------------------------------------------------- |
-| **json**        | Returns the JSON string representation of the run result if the output format of the final task is JSON. |
-| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                                  |
-| \***\*str\*\*** | Returns the string representation of the run result, prioritizing Pydantic, then JSON, then raw.         |
-
-### Accessing Pipeline Outputs
-
-Once a pipeline has been executed, its output can be accessed through the `PipelineOutput` object returned by the `process_runs` method. The `PipelineOutput` class provides access to individual `PipelineRunResult` objects, each representing a single run through the pipeline.
-
-#### Example
-
-```python
-# Define input data for the pipeline
-input_data = [{"initial_query": "Latest advancements in AI"}, {"initial_query": "Future of robotics"}]
-
-# Execute the pipeline
-pipeline_output = await my_pipeline.process_runs(input_data)
-
-# Access the results
-for run_result in pipeline_output.run_results:
-    print(f"Run ID: {run_result.id}")
-    print(f"Final Raw Output: {run_result.raw}")
-    if run_result.json_dict:
-        print(f"JSON Output: {json.dumps(run_result.json_dict, indent=2)}")
-    if run_result.pydantic:
-        print(f"Pydantic Output: {run_result.pydantic}")
-    print(f"Token Usage: {run_result.token_usage}")
-    print(f"Trace: {run_result.trace}")
-    print("Crew Outputs:")
-    for crew_output in run_result.crews_outputs:
-        print(f"  Crew: {crew_output.raw}")
-    print("\n")
-```
-
-This example demonstrates how to access and work with the pipeline output, including individual run results and their associated data.
-
-## Using Pipelines
-
-Pipelines are particularly useful for complex workflows that involve multiple stages of processing, analysis, or content generation. They allow you to:
-
-1. **Sequence Operations**: Execute crews in a specific order, ensuring that the output of one crew is available as input to the next.
-2. **Parallel Processing**: Run multiple crews concurrently within a stage for increased efficiency.
-3. **Manage Complex Workflows**: Break down large tasks into smaller, manageable steps executed by specialized crews.
-
-### Example: Running a Pipeline
-
-```python
-# Define input data for the pipeline
-input_data = [{"initial_query": "Latest advancements in AI"}]
-
-# Execute the pipeline, initiating a run for each input
-results = await my_pipeline.process_runs(input_data)
-
-# Access the results
-for result in results:
-    print(f"Final Output: {result.raw}")
-    print(f"Token Usage: {result.token_usage}")
-    print(f"Trace: {result.trace}")  # Shows the path of the input through all stages
-```
-
-## Advanced Features
-
-### Parallel Execution within Stages
-
-You can define parallel execution within a stage by providing a list of crews, creating multiple branches:
-
-```python
-parallel_analysis_crew = Crew(agents=[financial_analyst], tasks=[financial_analysis_task])
-market_analysis_crew = Crew(agents=[market_analyst], tasks=[market_analysis_task])
-
-my_pipeline = Pipeline(
-    stages=[
-        research_crew,
-        [parallel_analysis_crew, market_analysis_crew],  # Parallel execution (branching)
-        writing_crew
-    ]
-)
-```
-
-### Routers in Pipelines
-
-Routers are a powerful feature in crewAI pipelines that allow for dynamic decision-making and branching within your workflow. They enable you to direct the flow of execution based on specific conditions or criteria, making your pipelines more flexible and adaptive.
-
-#### What is a Router?
-
-A router in crewAI is a special component that can be included as a stage in your pipeline. It evaluates the input data and determines which path the execution should take next. This allows for conditional branching in your pipeline, where different crews or sub-pipelines can be executed based on the router's decision.
-
-#### Key Components of a Router
-
-1. **Routes**: A dictionary of named routes, each associated with a condition and a pipeline to execute if the condition is met.
-2. **Default Route**: A fallback pipeline that is executed if none of the defined route conditions are met.
-
-#### Creating a Router
-
-Here's an example of how to create a router:
-
-```python
-from crewai import Router, Route, Pipeline, Crew, Agent, Task
-
-# Define your agents
-classifier = Agent(name="Classifier", role="Email Classifier")
-urgent_handler = Agent(name="Urgent Handler", role="Urgent Email Processor")
-normal_handler = Agent(name="Normal Handler", role="Normal Email Processor")
-
-# Define your tasks
-classify_task = Task(description="Classify the email based on its content and metadata.")
-urgent_task = Task(description="Process and respond to urgent email quickly.")
-normal_task = Task(description="Process and respond to normal email thoroughly.")
-
-# Define your crews
-classification_crew = Crew(agents=[classifier], tasks=[classify_task]) # classify email between high and low urgency 1-10
-urgent_crew = Crew(agents=[urgent_handler], tasks=[urgent_task])
-normal_crew = Crew(agents=[normal_handler], tasks=[normal_task])
-
-# Create pipelines for different urgency levels
-urgent_pipeline = Pipeline(stages=[urgent_crew])
-normal_pipeline = Pipeline(stages=[normal_crew])
-
-# Create a router
-email_router = Router(
-    routes={
-        "high_urgency": Route(
-            condition=lambda x: x.get("urgency_score", 0) > 7,
-            pipeline=urgent_pipeline
-        ),
-        "low_urgency": Route(
-            condition=lambda x: x.get("urgency_score", 0) <= 7,
-            pipeline=normal_pipeline
-        )
-    },
-    default=Pipeline(stages=[normal_pipeline])  # Default to just classification if no urgency score
-)
-
-# Use the router in a main pipeline
-main_pipeline = Pipeline(stages=[classification_crew, email_router])
-
-inputs = [{"email": "..."}, {"email": "..."}]  # List of email data
-
-main_pipeline.kickoff(inputs=inputs)
-```
-
-In this example, the router decides between an urgent pipeline and a normal pipeline based on the urgency score of the email. If the urgency score is greater than 7, it routes to the urgent pipeline; otherwise, it uses the normal pipeline. If the input doesn't include an urgency score, it defaults to just the classification crew.
-
-#### Benefits of Using Routers
-
-1. **Dynamic Workflow**: Adapt your pipeline's behavior based on input characteristics or intermediate results.
-2. **Efficiency**: Route urgent tasks to quicker processes, reserving more thorough pipelines for less time-sensitive inputs.
-3. **Flexibility**: Easily modify or extend your pipeline's logic without changing the core structure.
-4. **Scalability**: Handle a wide range of email types and urgency levels with a single pipeline structure.
-
-### Error Handling and Validation
-
-The Pipeline class includes validation mechanisms to ensure the robustness of the pipeline structure:
-
-- Validates that stages contain only Crew instances or lists of Crew instances.
-- Prevents double nesting of stages to maintain a clear structure.

docs/core-concepts/Planning.md

@@ -23,25 +23,6 @@ my_crew = Crew(
 
 From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
 
-#### Planning LLM
-
-Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available.
-
-```python
-from crewai import Crew, Agent, Task, Process
-from langchain_openai import ChatOpenAI
-
-# Assemble your crew with planning capabilities and custom LLM
-my_crew = Crew(
-    agents=self.agents,
-    tasks=self.tasks,
-    process=Process.sequential,
-    planning=True,
-    planning_llm=ChatOpenAI(model="gpt-4o")
-)
-```
-
-
 ### 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.

docs/core-concepts/Tasks.md

@@ -90,7 +90,7 @@ task = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task],
-    verbose=True
+    verbose=2
 )
 
 result = crew.kickoff()
@@ -142,7 +142,7 @@ task = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task],
-    verbose=True
+    verbose=2
 )
 
 result = crew.kickoff()
@@ -264,7 +264,7 @@ task1 = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task1, task2, task3],
-    verbose=True
+    verbose=2
 )
 
 result = crew.kickoff()

docs/core-concepts/Tools.md

@@ -84,7 +84,7 @@ write = Task(
 crew = Crew(
     agents=[researcher, writer],
     tasks=[research, write],
-    verbose=True
+    verbose=2
 )
 
 # Execute tasks

docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md

@@ -1,259 +0,0 @@
----
-title: Starting a New CrewAI Project - Using Template
-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.
-
-Beforre we start there are a couple of things to note:
-
-1. CrewAI is a Python package and requires Python >=3.10 and <=3.13 to run.
-2. The preferred way of setting up CrewAI is using the `crewai create` command.This will create a new project folder and install a skeleton template for you to work on.
-
-## Prerequisites
-
-Before getting started with CrewAI, make sure that you have installed it via pip:
-
-```shell
-$ pip install crewai crewai-tools
-```
-
-### Virtual Environments
-It is highly recommended that you use virtual environments to ensure that your CrewAI project is isolated from other projects and dependencies. Virtual environments provide a clean, separate workspace for each project, preventing conflicts between different versions of packages and libraries. This isolation is crucial for maintaining consistency and reproducibility in your development process. You have multiple options for setting up virtual environments depending on your operating system and Python version:
-
-1. Use venv (Python's built-in virtual environment tool):
-   venv is included with Python 3.3 and later, making it a convenient choice for many developers. It's lightweight and easy to use, perfect for simple project setups.
-
-   To set up virtual environments with venv, refer to the official [Python documentation](https://docs.python.org/3/tutorial/venv.html).
-
-2. Use Conda (A Python virtual environment manager):
-   Conda is an open-source package manager and environment management system for Python. It's widely used by data scientists, developers, and researchers to manage dependencies and environments in a reproducible way.
-
-   To set up virtual environments with Conda, refer to the official [Conda documentation](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).
-
-3. Use Poetry (A Python package manager and dependency management tool):
-   Poetry is an open-source Python package manager that simplifies the installation of packages and their dependencies. Poetry offers a convenient way to manage virtual environments and dependencies.
-   Poetry is CrewAI's prefered tool for package / dependancy management in CrewAI.
-
-### Code IDEs
-
-Most users of CrewAI a Code Editor / Integrated Development Environment (IDE) for building there Crews. You can use any code IDE of your choice. Seee below for some popular options for Code Editors / Integrated Development Environments (IDE):
-
-- [Visual Studio Code](https://code.visualstudio.com/) - Most popular
-- [PyCharm](https://www.jetbrains.com/pycharm/)
-- [Cursor AI](https://cursor.com)
-
-Pick one that suits your style and needs.
-
-## Creating a New Project
-In this example we will be using Venv as our virtual environment manager.
-
-To setup a virtual environment, run the following CLI command:
-
-```shell
-$ python3 -m venv <venv-name>
-```
-
-Activate your virtual environment by running the following CLI command:
-
-```shell
-$ source <venv-name>/bin/activate
-```
-
-Now, to create a new CrewAI project, run the following CLI command:
-
-```shell
-$ crewai create <project_name>
-```
-
-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.
-  agent: researcher # THIS NEEDS TO MATCH THE AGENT NAME IN THE AGENTS.YAML FILE AND THE AGENT DEFINED IN THE Crew.PY FILE
-  context: # THESE NEED TO MATCH THE TASK NAMES DEFINED ABOVE AND THE TASKS.YAML FILE AND THE TASK DEFINED IN THE Crew.PY FILE
-    - researcher
-```
-
-### Referencing Variables:
-Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from task.yaml file. Ensure your annotated agent and function name is the same otherwise your task wont recognize the reference properly.
-
-#### Example References
-agent.yaml
-```yaml
-email_summarizer:
-    role: >
-      Email Summarizer
-    goal: >
-      Summarize emails into a concise and clear summary
-    backstory: >
-      You will create a 5 bullet point summary of the report
-    llm: mixtal_llm
-```
-
-task.yaml
-```yaml
-email_summarizer_task:
-    description: >
-      Summarize the email into a 5 bullet point summary
-    expected_output: >
-      A 5 bullet point summary of the email
-    agent: email_summarizer
-    context:
-      - reporting_task
-      - research_task
-```
-
-Use the annotations are used to properly reference the agent and task in the crew.py file.
-
-### Annotations include:
-* @agent
-* @task
-* @crew
-* @llm
-* @tool
-* @callback
-* @output_json
-* @output_pydantic
-* @cache_handler
-
-
-crew.py
-```py
-...
-    @llm
-    def mixtal_llm(self):
-        return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
-
-    @agent
-    def email_summarizer(self) -> Agent:
-        return Agent(
-            config=self.agents_config["email_summarizer"],
-        )
-    ## ...other tasks defined
-    @task
-    def email_summarizer_task(self) -> Task:
-        return Task(
-            config=self.tasks_config["email_summarizer_task"],
-        )
-...
-```
-
-
-
-## 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
-$ crewai run
-```
-or
-```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/how-to/Conditional-Tasks.md

@@ -79,7 +79,7 @@ task3 = Task(
 crew = Crew(
     agents=[data_fetcher_agent, data_processor_agent, summary_generator_agent],
     tasks=[task1, conditional_task, task3],
-    verbose=True,
+    verbose=2,
 )
 
 result = crew.kickoff()

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

@@ -7,7 +7,6 @@ description: Comprehensive guide on crafting, using, and managing custom tools w
 This guide provides detailed instructions on creating custom tools for the crewAI framework and how to efficiently manage and utilize these tools, incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, enabling agents to perform a wide range of actions.
 
 ### Prerequisites
-
 Before creating your own tools, ensure you have the crewAI extra tools package installed:
 
 ```bash
@@ -32,7 +31,7 @@ class MyCustomTool(BaseTool):
 
 ### Using the `tool` Decorator
 
-Alternatively, you can use the tool decorator `@tool`. This approach allows you to define the tool's attributes and functionality directly within a function, offering a concise and efficient way to create specialized tools tailored to your needs.
+Alternatively, use the `tool` decorator for a direct approach to create tools. This requires specifying attributes and the tool's logic within a function.
 
 ```python
 from crewai_tools import tool

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

@@ -0,0 +1,84 @@
+---
+title: Assembling and Activating Your CrewAI Team
+description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, asynchronous execution, output customization, language model configuration, code execution, integration with third-party agents, and improved task management.
+---
+
+## Introduction
+Embark on your CrewAI journey by setting up your environment and initiating your AI crew with the latest features. This guide ensures a smooth start, incorporating all recent updates for an enhanced experience, including code execution capabilities, integration with third-party agents, and advanced task management.
+
+## Step 0: Installation
+Install CrewAI and any necessary packages for your project. CrewAI is compatible with Python >=3.10,<=3.13.
+
+```shell
+pip install crewai
+pip install 'crewai[tools]'
+```
+
+## Step 1: Assemble Your Agents
+Define your agents with distinct roles, backstories, and enhanced capabilities. The Agent class now supports a wide range of attributes for fine-tuned control over agent behavior and interactions, including code execution and integration with third-party agents.
+
+```python
+import os
+from langchain.llms import OpenAI
+from crewai import Agent
+from crewai_tools import SerperDevTool, BrowserbaseLoadTool, 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()
+
+# Creating a senior researcher agent with advanced configurations
+researcher = Agent(
+    role='Senior Researcher',
+    goal='Uncover groundbreaking technologies in {topic}',
+    backstory=("Driven by curiosity, you're at the forefront of innovation, "
+               "eager to explore and share knowledge that could change the world."),
+    memory=True,
+    verbose=True,
+    allow_delegation=False,
+    tools=[search_tool, browser_tool],
+    allow_code_execution=False,  # New attribute for enabling code execution
+    max_iter=15,  # Maximum number of iterations for task execution
+    max_rpm=100,  # Maximum requests per minute
+    max_execution_time=3600,  # Maximum execution time in seconds
+    system_template="Your custom system template here",  # Custom system template
+    prompt_template="Your custom prompt template here",  # Custom prompt template
+    response_template="Your custom response template here",  # Custom response template
+)
+
+# Creating a writer agent with custom tools and specific configurations
+writer = Agent(
+    role='Writer',
+    goal='Narrate compelling tech stories about {topic}',
+    backstory=("With a flair for simplifying complex topics, you craft engaging "
+               "narratives that captivate and educate, bringing new discoveries to light."),
+    verbose=True,
+    allow_delegation=False,
+    memory=True,
+    tools=[exa_search_tool],
+    function_calling_llm=OpenAI(model_name="gpt-3.5-turbo"),  # Separate LLM for function calling
+)
+
+# Setting a specific manager agent
+manager = Agent(
+  role='Manager',
+  goal='Ensure the smooth operation and coordination of the team',
+  verbose=True,
+  backstory=(
+    "As a seasoned project manager, you excel in organizing "
+    "tasks, managing timelines, and ensuring the team stays on track."
+  ),
+  allow_code_execution=True,  # Enable code execution for the manager
+)
+```
+
+### New Agent Attributes and Features
+
+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.

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

@@ -7,7 +7,7 @@ description: Learn how to force tool output as the result in of an Agent's task
 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 `result_as_answer` parameter to `True` when creating the agent. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
+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:
 
@@ -16,7 +16,7 @@ Here's an example of how to force the tool output as the result of an agent's ta
 # Define a custom tool that returns the result as the answer
 coding_agent =Agent(
         role="Data Scientist",
-        goal="Product amazing reports on AI",
+        goal="Product amazing resports on AI",
         backstory="You work with data and AI",
         tools=[MyCustomTool(result_as_answer=True)],
     )

docs/how-to/Human-Input-on-Execution.md

@@ -81,7 +81,7 @@ task2 = Task(
 crew = Crew(
     agents=[researcher, writer],
     tasks=[task1, task2],
-    verbose=True,
+    verbose=2,
     memory=True,
 )
 

docs/how-to/Installing-CrewAI.md

@@ -18,7 +18,4 @@ pip install crewai
 # Install the main crewAI package and the tools package
 # that includes a series of helpful tools for your agents
 pip install 'crewai[tools]'
-
-# Alternatively, you can also use:
-pip install crewai crewai-tools
 ```

docs/how-to/LLM-Connections.md

@@ -6,25 +6,33 @@ description: Comprehensive guide on integrating CrewAI with various Large Langua
 ## Connect CrewAI to LLMs
 
 !!! note "Default LLM"
-    By default, CrewAI uses OpenAI's GPT-4o model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4o") for language processing. You can configure your agents to use a different model or API as described in this guide.
-    By default, CrewAI uses OpenAI's GPT-4 model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4") for language processing. You can configure your agents to use a different model or API as described in this guide.
+    By default, CrewAI uses OpenAI's GPT-4 model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4o") for language processing. You can configure your agents to use a different model or API as described in this guide.
 
-CrewAI provides extensive versatility in integrating with various Language Models (LLMs), including local options through Ollama such as  Llama and Mixtral to cloud-based solutions like Azure. Its compatibility extends to all [LangChain LLM components](https://python.langchain.com/v0.2/docs/integrations/llms/), offering a wide range of integration possibilities for customized AI applications.
+CrewAI offers flexibility in connecting to various LLMs, including local models via [Ollama](https://ollama.ai) and different APIs like Azure. It's compatible with all [LangChain LLM](https://python.langchain.com/docs/integrations/llms/) components, enabling diverse integrations for tailored AI solutions.
 
-The platform supports connections to an array of Generative AI models, including:
+## CrewAI Agent Overview
 
- - OpenAI's suite of advanced language models
- - Anthropic's cutting-edge AI offerings
- - Ollama's diverse range of locally-hosted generative model & embeddings
- - LM Studio's diverse range of locally hosted generative models & embeddings
- - Groq's Super Fast LLM offerings
- - Azures' generative AI offerings
- - HuggingFace's generative AI offerings
+The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. Here's a comprehensive overview of the Agent class attributes and methods:
 
-This broad spectrum of LLM options enables users to select the most suitable model for their specific needs, whether prioritizing local deployment, specialized capabilities, or cloud-based scalability.
+- **Attributes**:
+    - `role`: Defines the agent's role within the solution.
+    - `goal`: Specifies the agent's objective.
+    - `backstory`: Provides a background story to the agent.
+    - `cache` *Optional*: Determines whether the agent should use a cache for tool usage. Default is `True`.
+    - `max_rpm` *Optional*: Maximum number of requests per minute the agent's execution should respect. Optional.
+    - `verbose` *Optional*: Enables detailed logging of the agent's execution. Default is `False`.
+    - `allow_delegation` *Optional*: Allows the agent to delegate tasks to other agents, default is `True`.
+    - `tools`: Specifies the tools available to the agent for task execution. Optional.
+    - `max_iter` *Optional*: Maximum number of iterations for an agent to execute a task, default is 25.
+    - `max_execution_time` *Optional*: Maximum execution time for an agent to execute a task. Optional.
+    - `step_callback` *Optional*: Provides a callback function to be executed after each step. Optional.
+    - `llm` *Optional*: Indicates the Large Language Model the agent uses. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
+    - `function_calling_llm` *Optional* :  Will turn the ReAct CrewAI agent into a function-calling agent.
+    - `callbacks` *Optional*: A list of callback functions from the LangChain library that are triggered during the agent's execution process.
+    - `system_template` *Optional*: Optional string to define the system format for the agent.
+    - `prompt_template` *Optional*: Optional string to define the prompt format for the agent.
+    - `response_template` *Optional*: Optional string to define the response format for the agent.
 
-## Changing the default LLM
-The default LLM is provided through the `langchain openai` package, which is installed by default when you install CrewAI. You can change this default LLM to a different model or API by setting the `OPENAI_MODEL_NAME` environment variable. This straightforward process allows you to harness the power of different OpenAI models, enhancing the flexibility and capabilities of your CrewAI implementation.
 ```python
 # Required
 os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
@@ -37,27 +45,30 @@ example_agent = Agent(
   verbose=True
 )
 ```
-## Ollama Local Integration
-Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, you will need the `langchain-ollama` package. You can then set the following environment variables to connect to your Ollama instance running locally on port 11434.
 
+## Ollama Integration
+Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below.
+
+### Setting Up Ollama
+- **Environment Variables Configuration**: To integrate Ollama, set the following environment variables:
 ```sh
-os.environ[OPENAI_API_BASE]='http://localhost:11434'
-os.environ[OPENAI_MODEL_NAME]='llama2'  # Adjust based on available model
-os.environ[OPENAI_API_KEY]='' # No API Key required for Ollama
+OPENAI_API_BASE='http://localhost:11434'
+OPENAI_MODEL_NAME='llama2'  # Adjust based on available model
+OPENAI_API_KEY=''
 ```
 
-## Ollama Integration Step by Step (ex. for using Llama 3.1 8B locally)
-1. [Download and install Ollama](https://ollama.com/download).   
-2. After setting up the Ollama, Pull the Llama3.1 8B model by typing following lines into your terminal ```ollama run llama3.1```.   
-3. Llama3.1 should now be served locally on `http://localhost:11434`
+## Ollama Integration (ex. for using Llama 2 locally)
+1. [Download Ollama](https://ollama.com/download).   
+2. After setting up the Ollama, Pull the Llama2 by typing following lines into the terminal ```ollama pull llama2```.   
+3. Enjoy your free Llama2 model that powered up by excellent agents from crewai.   
 ```
 from crewai import Agent, Task, Crew
-from langchain_ollama import ChatOllama
+from langchain.llms import Ollama
 import os
 os.environ["OPENAI_API_KEY"] = "NA"
 
 llm = Ollama(
-    model = "llama3.1",
+    model = "llama2",
     base_url = "http://localhost:11434")
 
 general_agent = Agent(role = "Math Professor",
@@ -74,7 +85,7 @@ task = Task(description="""what is 3 + 5""",
 crew = Crew(
             agents=[general_agent],
             tasks=[task],
-            verbose=True
+            verbose=2
         )
 
 result = crew.kickoff()
@@ -87,14 +98,13 @@ There are a couple of different ways you can use HuggingFace to host your LLM.
 
 ### Your own HuggingFace endpoint
 ```python
-from langchain_huggingface import HuggingFaceEndpoint,
+from langchain_community.llms import HuggingFaceEndpoint
 
 llm = HuggingFaceEndpoint(
-    repo_id="microsoft/Phi-3-mini-4k-instruct",
+    endpoint_url="<YOUR_ENDPOINT_URL_HERE>",
+    huggingfacehub_api_token="<HF_TOKEN_HERE>",
     task="text-generation",
-    max_new_tokens=512,
-    do_sample=False,
-    repetition_penalty=1.03,
+    max_new_tokens=512
 )
 
 agent = Agent(
@@ -105,50 +115,66 @@ agent = Agent(
 )
 ```
 
+### From HuggingFaceHub endpoint
+```python
+from langchain_community.llms import HuggingFaceHub
+
+llm = HuggingFaceHub(
+    repo_id="HuggingFaceH4/zephyr-7b-beta",
+    huggingfacehub_api_token="<HF_TOKEN_HERE>",
+    task="text-generation",
+)
+```
+
 ## OpenAI Compatible API Endpoints
 Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, Groq, and Mistral AI.
 
 ### Configuration Examples
 #### FastChat
 ```sh
-os.environ[OPENAI_API_BASE]="http://localhost:8001/v1"
-os.environ[OPENAI_MODEL_NAME]='oh-2.5m7b-q51'
-os.environ[OPENAI_API_KEY]=NA
+OPENAI_API_BASE="http://localhost:8001/v1"
+OPENAI_MODEL_NAME='oh-2.5m7b-q51'
+OPENAI_API_KEY=NA
 ```
 
 #### LM Studio
 Launch [LM Studio](https://lmstudio.ai) and go to the Server tab. Then select a model from the dropdown menu and wait for it to load. Once it's loaded, click the green Start Server button and use the URL, port, and API key that's shown (you can modify them). Below is an example of the default settings as of LM Studio 0.2.19:
 ```sh
-os.environ[OPENAI_API_BASE]="http://localhost:1234/v1"
-os.environ[OPENAI_API_KEY]="lm-studio"
+OPENAI_API_BASE="http://localhost:1234/v1"
+OPENAI_API_KEY="lm-studio"
 ```
 
 #### Groq API
 ```sh
-os.environ[OPENAI_API_KEY]=your-groq-api-key
-os.environ[OPENAI_MODEL_NAME]='llama3-8b-8192'
-os.environ[OPENAI_API_BASE]=https://api.groq.com/openai/v1
+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
-os.environ[OPENAI_API_KEY]=your-mistral-api-key
-os.environ[OPENAI_API_BASE]=https://api.mistral.ai/v1
-os.environ[OPENAI_MODEL_NAME]="mistral-small"
+OPENAI_API_KEY=your-mistral-api-key
+OPENAI_API_BASE=https://api.mistral.ai/v1
+OPENAI_MODEL_NAME="mistral-small"
 ```
 
 ### Solar
-```sh
+```python
 from langchain_community.chat_models.solar import SolarChat
-```
-```sh
-os.environ[SOLAR_API_BASE]="https://api.upstage.ai/v1/solar"
-os.environ[SOLAR_API_KEY]="your-solar-api-key"
-```
+# Initialize language model
+os.environ["SOLAR_API_KEY"] = "your-solar-api-key"
+llm = SolarChat(max_tokens=1024)
 
 # Free developer API key available here: https://console.upstage.ai/services/solar
 # Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
+```
 
+### text-gen-web-ui
+```sh
+OPENAI_API_BASE=http://localhost:5000/v1
+OPENAI_MODEL_NAME=NA
+OPENAI_API_KEY=NA
+```
 
 ### Cohere
 ```python
@@ -164,11 +190,10 @@ llm = ChatCohere()
 ### Azure Open AI Configuration
 For Azure OpenAI API integration, set the following environment variables:
 ```sh
-
-os.environ[AZURE_OPENAI_DEPLOYMENT] = "You deployment"
-os.environ["OPENAI_API_VERSION"] = "2023-12-01-preview"
-os.environ["AZURE_OPENAI_ENDPOINT"] = "Your Endpoint"
-os.environ["AZURE_OPENAI_API_KEY"] = "<Your API Key>"
+AZURE_OPENAI_VERSION="2022-12-01"
+AZURE_OPENAI_DEPLOYMENT=""
+AZURE_OPENAI_ENDPOINT=""
+AZURE_OPENAI_KEY=""
 ```
 
 ### Example Agent with Azure LLM
@@ -191,5 +216,6 @@ azure_agent = Agent(
   llm=azure_llm
 )
 ```
+
 ## Conclusion
 Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.

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

@@ -0,0 +1,137 @@
+---
+title: Starting a New CrewAI Project
+description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
+---
+
+# Starting Your CrewAI Project
+
+Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
+
+## Prerequisites
+
+We assume you have already installed CrewAI. If not, please refer to the [installation guide](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

@@ -5,19 +5,6 @@
 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
 
 <div style="display:flex; margin:0 auto; justify-content: center;">
-    <div style="width:25%">
-        <h2>Getting Started</h2>
-        <ul>
-            <li><a href='./getting-started/Installing-CrewAI'>
-                   Installing CrewAI
-                 </a>
-            </li>
-            <li><a href='./getting-started/Start-a-New-CrewAI-Project-Template-Method'>
-                   Start a New CrewAI Project: Template Method
-                 </a>
-            </li>
-        </ul>
-    </div>
     <div style="width:25%">
         <h2>Core Concepts</h2>
         <ul>
@@ -46,11 +33,6 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Crews
                 </a>
             </li>
-            <li>
-                <a href="./core-concepts/Pipeline">
-                    Pipeline
-                </a>
-            </li>
             <li>
                 <a href="./core-concepts/Training-Crew">
                     Training
@@ -71,6 +53,21 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
     <div style="width:30%">
         <h2>How-To Guides</h2>
         <ul>
+            <li>
+                <a href="./how-to/Start-a-New-CrewAI-Project">
+                    Starting Your crewAI Project
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Installing-CrewAI">
+                    Installing crewAI
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Creating-a-Crew-and-kick-it-off">
+                    Getting Started
+                </a>
+            </li>
             <li>
                 <a href="./how-to/Create-Custom-Tools">
                     Create Custom Tools

docs/telemetry/Telemetry.md

@@ -5,7 +5,7 @@ description: Understanding the telemetry data collected by CrewAI and how it con
 
 ## Telemetry
 
-CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users. We don't offer a way to disable it now, but we will in the future.
+CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
 
 It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy.
 
@@ -22,7 +22,7 @@ It's pivotal to understand that **NO data is collected** concerning prompts, tas
 - **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas.
 
 ### Opt-In Further Telemetry Sharing
-Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
+Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. This opt-in approach respects user privacy and aligns with data protection standards by ensuring users have control over their data sharing preferences. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
 
 ### Updates and Revisions
 We are committed to maintaining the accuracy and transparency of our documentation. Regular reviews and updates are performed to ensure our documentation accurately reflects the latest developments of our codebase and telemetry practices. Users are encouraged to review this section for the most current information on our data collection practices and how they contribute to the improvement of CrewAI.

docs/tools/CodeInterpreterTool.md

@@ -1,9 +1,9 @@
 # CodeInterpreterTool
 
 ## Description
-This tool enables the Agent to execute Python 3 code that it has generated autonomously. The code is run in a secure, isolated environment, ensuring safety regardless of the content. 
+This tool is used to give the Agent the ability to run code (Python3) from the code generated by the Agent itself. The code is executed in a sandboxed environment, so it is safe to run any code.
 
-This functionality is particularly valuable as it allows the Agent to create code, execute it within the same ecosystem, obtain the results, and utilize that information to inform subsequent decisions and actions.
+It is incredible useful since it allows the Agent to generate code, run it in the same environment, get the result and use it to make decisions.
 
 ## Requirements
 

docs/tools/ComposioTool.md

@@ -2,7 +2,7 @@
 
 ## Description
 
-This tools is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the composio SDK.
+This tools is a wrapper around the composio toolset and gives your agent access to a wide variety of tools from the composio SDK.
 
 ## Installation
 
@@ -19,7 +19,7 @@ after the installation is complete, either run `composio login` or export your c
 
 The following example demonstrates how to initialize the tool and execute a github action:
 
-1. Initialize Composio tools
+1. Initialize toolset
 
 ```python
 from composio import App

docs/tools/SerperDevTool.md

@@ -29,69 +29,5 @@ To effectively use the `SerperDevTool`, follow these steps:
 2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at `serper.dev`.
 3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
 
-## Parameters
-
-The `SerperDevTool` comes with several parameters that will be passed to the API :
-
-- **search_url**: The URL endpoint for the search API. (Default is `https://google.serper.dev/search`)
-
-- **country**: Optional. Specify the country for the search results.
-- **location**: Optional. Specify the location for the search results.
-- **locale**: Optional. Specify the locale for the search results.
-- **n_results**: Number of search results to return. Default is `10`.
-
-The values for `country`, `location`, `locale` and `search_url` can be found on the [Serper Playground](https://serper.dev/playground).
-
-## Example with Parameters
-Here is an example demonstrating how to use the tool with additional parameters:
-
-```python
-from crewai_tools import SerperDevTool
-
-tool = SerperDevTool(
-    search_url="https://google.serper.dev/scholar",
-    n_results=2,
-)
-
-print(tool.run(search_query="ChatGPT"))
-
-# Using Tool: Search the internet
-
-# Search results: Title: Role of chat gpt in public health
-# Link: https://link.springer.com/article/10.1007/s10439-023-03172-7
-# Snippet: … ChatGPT in public health. In this overview, we will examine the potential uses of ChatGPT in
-# ---
-# Title: Potential use of chat gpt in global warming
-# Link: https://link.springer.com/article/10.1007/s10439-023-03171-8
-# Snippet: … as ChatGPT, have the potential to play a critical role in advancing our understanding of climate
-# ---
-
-```
-
-```python
-from crewai_tools import SerperDevTool
-
-tool = SerperDevTool(
-    country="fr",
-    locale="fr",
-    location="Paris, Paris, Ile-de-France, France",
-    n_results=2,
-)
-
-print(tool.run(search_query="Jeux Olympiques"))
-
-# Using Tool: Search the internet
-
-# Search results: Title: Jeux Olympiques de Paris 2024 - Actualités, calendriers, résultats
-# Link: https://olympics.com/fr/paris-2024
-# Snippet: Quels sont les sports présents aux Jeux Olympiques de Paris 2024 ? · Athlétisme · Aviron · Badminton · Basketball · Basketball 3x3 · Boxe · Breaking · Canoë ...
-# ---
-# Title: Billetterie Officielle de Paris 2024 - Jeux Olympiques et Paralympiques
-# Link: https://tickets.paris2024.org/
-# Snippet: Achetez vos billets exclusivement sur le site officiel de la billetterie de Paris 2024 pour participer au plus grand événement sportif au monde.
-# ---
-
-```
-
 ## Conclusion
-By integrating the `SerperDevTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. The updated parameters allow for more customized and localized search results. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
+By integrating the `SerperDevTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.

mkdocs.yml

@@ -119,9 +119,6 @@ theme:
 
 nav:
   - Home: '/'
-  - Getting Started:
-    - Installing CrewAI: 'getting-started/Installing-CrewAI.md'
-    - Starting a new CrewAI project: 'getting-started/Start-a-New-CrewAI-Project-Template-Method.md'
   - Core Concepts:
     - Agents: 'core-concepts/Agents.md'
     - Tasks: 'core-concepts/Tasks.md'

poetry.lock

@@ -2,13 +2,13 @@
 
 [[package]]
 name = "agentops"
-version = "0.3.4"
+version = "0.3.0"
 description = "Python SDK for developing AI agent evals and observability"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "agentops-0.3.4-py3-none-any.whl", hash = "sha256:126f7aed4ba43c1399b5488d67a03d10cb4c531e619c650776f826ca00c1aa24"},
-    {file = "agentops-0.3.4.tar.gz", hash = "sha256:a92c9cb7c511197f0ecb8cb5aca15d35022c15a3d2fd2aaaa34cd7e5dc59393f"},
+    {file = "agentops-0.3.0-py3-none-any.whl", hash = "sha256:22aeb3355e66b32a2b2a9f676048b81979b2488feddb088f9266034b3ed50539"},
+    {file = "agentops-0.3.0.tar.gz", hash = "sha256:6c0c08a57410fa5e826a7bafa1deeba9f7b3524709427d9e1abbd0964caaf76b"},
 ]
 
 [package.dependencies]
@@ -294,38 +294,38 @@ files = [
 
 [[package]]
 name = "bcrypt"
-version = "4.2.0"
+version = "4.1.3"
 description = "Modern password hashing for your software and your servers"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "bcrypt-4.2.0-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:096a15d26ed6ce37a14c1ac1e48119660f21b24cba457f160a4b830f3fe6b5cb"},
-    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c02d944ca89d9b1922ceb8a46460dd17df1ba37ab66feac4870f6862a1533c00"},
-    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d84cf6d877918620b687b8fd1bf7781d11e8a0998f576c7aa939776b512b98d"},
-    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:1bb429fedbe0249465cdd85a58e8376f31bb315e484f16e68ca4c786dcc04291"},
-    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:655ea221910bcac76ea08aaa76df427ef8625f92e55a8ee44fbf7753dbabb328"},
-    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:1ee38e858bf5d0287c39b7a1fc59eec64bbf880c7d504d3a06a96c16e14058e7"},
-    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:0da52759f7f30e83f1e30a888d9163a81353ef224d82dc58eb5bb52efcabc399"},
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+    {file = "bcrypt-4.1.3.tar.gz", hash = "sha256:2ee15dd749f5952fe3f0430d0ff6b74082e159c50332a1413d51b5689cf06623"},
 ]
 
 [package.extras]
@@ -355,17 +355,17 @@ lxml = ["lxml"]
 
 [[package]]
 name = "boto3"
-version = "1.34.149"
+version = "1.34.145"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.149-py3-none-any.whl", hash = "sha256:11edeeacdd517bda3b7615b754d8440820cdc9ddd66794cc995a9693ddeaa3be"},
-    {file = "boto3-1.34.149.tar.gz", hash = "sha256:f4e6489ba9dc7fb37d53e0e82dbc97f2cb0a4969ef3970e2c88b8f94023ae81a"},
+    {file = "boto3-1.34.145-py3-none-any.whl", hash = "sha256:69d5afb7a017d07dd6bdfb680d2912d5d369b3fafa0a45161207d9f393b14d7e"},
+    {file = "boto3-1.34.145.tar.gz", hash = "sha256:ac770fb53dde1743aec56bd8e56b7ee2e2f5ad42a37825968ec4ff8428822640"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.149,<1.35.0"
+botocore = ">=1.34.145,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -374,13 +374,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.149"
+version = "1.34.145"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.149-py3-none-any.whl", hash = "sha256:ae6c4be52eeee96f68c116b27d252bab069cd046d61a17cfe8e9da411cf22906"},
-    {file = "botocore-1.34.149.tar.gz", hash = "sha256:2e1eb5ef40102a3d796bb3dd05f2ac5e8fb43fe1ff114b4f6d33153437f5a372"},
+    {file = "botocore-1.34.145-py3-none-any.whl", hash = "sha256:2e72e262de02adcb0264ac2bac159a28f55dbba8d9e52aa0308773a42950dff5"},
+    {file = "botocore-1.34.145.tar.gz", hash = "sha256:edf0fb4c02186ae29b76263ac5fda18b0a085d334a310551c9984407cf1079e6"},
 ]
 
 [package.dependencies]
@@ -747,13 +747,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "5.6.2"
+version = "5.6.1"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "cohere-5.6.2-py3-none-any.whl", hash = "sha256:cfecf1343bcaa4091266c5a231fbcb3ccbd80cad05ea093ef80024a117aa3a2f"},
-    {file = "cohere-5.6.2.tar.gz", hash = "sha256:6bb901afdfb02f62ad8ed2d82f12d8ea87a6869710f5f880cb89190c4e994805"},
+    {file = "cohere-5.6.1-py3-none-any.whl", hash = "sha256:1c8bcd39a54622d64b83cafb865f102cd2565ce091b0856fd5ce11bf7169109a"},
+    {file = "cohere-5.6.1.tar.gz", hash = "sha256:5d7efda64f0e512d4cc35aa04b17a6f74b3d8c175a99f2797991a7f31dfac349"},
 ]
 
 [package.dependencies]
@@ -1012,13 +1012,13 @@ idna = ">=2.0.0"
 
 [[package]]
 name = "embedchain"
-version = "0.1.119"
+version = "0.1.118"
 description = "Simplest open source retrieval (RAG) framework"
 optional = false
 python-versions = "<=3.13,>=3.9"
 files = [
-    {file = "embedchain-0.1.119-py3-none-any.whl", hash = "sha256:8ec3e7f139939fa1dc8fda898f8d8d9d31a5abfe08e184b607e38733d863d606"},
-    {file = "embedchain-0.1.119.tar.gz", hash = "sha256:0f4f45e092b7f3192ea6fe82575726532573b1231d7af6c22edc695b701b4223"},
+    {file = "embedchain-0.1.118-py3-none-any.whl", hash = "sha256:38ead471df9d9234bf42e6f7a32cab26431d50d6f2f894f18a6cabc0b02bf31a"},
+    {file = "embedchain-0.1.118.tar.gz", hash = "sha256:1fa1e799882a1dc4e63af344595b043f1c1f30fbd59461b6660b1934b85a1e4b"},
 ]
 
 [package.dependencies]
@@ -1032,7 +1032,7 @@ langchain = ">0.2,<=0.3"
 langchain-cohere = ">=0.1.4,<0.2.0"
 langchain-community = ">=0.2.6,<0.3.0"
 langchain-openai = ">=0.1.7,<0.2.0"
-mem0ai = ">=0.0.9,<0.0.10"
+mem0ai = ">=0.0.5,<0.0.6"
 openai = ">=1.1.1"
 posthog = ">=3.0.2,<4.0.0"
 pypdf = ">=4.0.1,<5.0.0"
@@ -1061,6 +1061,20 @@ together = ["together (>=1.2.1,<2.0.0)"]
 vertexai = ["langchain-google-vertexai (>=1.0.6,<2.0.0)"]
 weaviate = ["weaviate-client (>=3.24.1,<4.0.0)"]
 
+[[package]]
+name = "eval-type-backport"
+version = "0.2.0"
+description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "eval_type_backport-0.2.0-py3-none-any.whl", hash = "sha256:ac2f73d30d40c5a30a80b8739a789d6bb5e49fdffa66d7912667e2015d9c9933"},
+    {file = "eval_type_backport-0.2.0.tar.gz", hash = "sha256:68796cfbc7371ebf923f03bdf7bef415f3ec098aeced24e054b253a0e78f7b37"},
+]
+
+[package.extras]
+tests = ["pytest"]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.2"
@@ -1388,13 +1402,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.60.0"
+version = "1.59.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.60.0.tar.gz", hash = "sha256:782c7f1ec0e77a7c7daabef3b65bfd506ed2b4b1dc2186753c43cd6faf8dd04e"},
-    {file = "google_cloud_aiplatform-1.60.0-py2.py3-none-any.whl", hash = "sha256:5f14159c9575f4b46335027e3ceb8fa57bd5eaa76a07f858105b8c6c034ec0d6"},
+    {file = "google-cloud-aiplatform-1.59.0.tar.gz", hash = "sha256:2bebb59c0ba3e3b4b568305418ca1b021977988adbee8691a5bed09b037e7e63"},
+    {file = "google_cloud_aiplatform-1.59.0-py2.py3-none-any.whl", hash = "sha256:549e6eb1844b0f853043309138ebe2db00de4bbd8197b3bde26804ac163ef52a"},
 ]
 
 [package.dependencies]
@@ -1416,8 +1430,8 @@ cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow
 datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
 full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"]
-langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.3)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "tenacity (<=8.3)"]
-langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.3)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
+langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "tenacity (<=8.3)"]
+langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (>=5.3.1,<7)"]
@@ -1427,7 +1441,7 @@ private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
 rapid-evaluation = ["pandas (>=1.0.0,<2.2.0)", "tqdm (>=4.23.0)"]
 ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "setuptools (<70.0.0)"]
 ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (==2.9.3)", "scikit-learn", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
-reasoningengine = ["cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"]
+reasoningengine = ["cloudpickle (>=3.0,<4.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"]
 tensorboard = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nltk", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "sentencepiece (>=0.2.0)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
 tokenization = ["sentencepiece (>=0.2.0)"]
@@ -1503,13 +1517,13 @@ protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4
 
 [[package]]
 name = "google-cloud-storage"
-version = "2.18.0"
+version = "2.17.0"
 description = "Google Cloud Storage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google_cloud_storage-2.18.0-py2.py3-none-any.whl", hash = "sha256:e8e1a9577952143c3fca8163005ecfadd2d70ec080fa158a8b305000e2c22fbb"},
-    {file = "google_cloud_storage-2.18.0.tar.gz", hash = "sha256:0aa3f7c57f3632f81b455d91558d2b27ada96eee2de3aaa17f689db1470d9578"},
+    {file = "google-cloud-storage-2.17.0.tar.gz", hash = "sha256:49378abff54ef656b52dca5ef0f2eba9aa83dc2b2c72c78714b03a1a95fe9388"},
+    {file = "google_cloud_storage-2.17.0-py2.py3-none-any.whl", hash = "sha256:5b393bc766b7a3bc6f5407b9e665b2450d36282614b7945e570b3480a456d1e1"},
 ]
 
 [package.dependencies]
@@ -1521,8 +1535,7 @@ google-resumable-media = ">=2.6.0"
 requests = ">=2.18.0,<3.0.0dev"
 
 [package.extras]
-protobuf = ["protobuf (<6.0.0dev)"]
-tracing = ["opentelemetry-api (>=1.1.0)"]
+protobuf = ["protobuf (<5.0.0dev)"]
 
 [[package]]
 name = "google-crc32c"
@@ -1742,6 +1755,25 @@ files = [
 backports-strenum = {version = ">=1.3", markers = "python_version < \"3.11\""}
 colorama = ">=0.4"
 
+[[package]]
+name = "groq"
+version = "0.9.0"
+description = "The official Python library for the groq API"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "groq-0.9.0-py3-none-any.whl", hash = "sha256:d0e46f4ad645504672bb09c8100af3ced3a7db0d5119dc13e4aca535fc455874"},
+    {file = "groq-0.9.0.tar.gz", hash = "sha256:130ed5e35d3acfaab46b9e7a078eeaebf91052f4a9d71f86f87fb319b5fec332"},
+]
+
+[package.dependencies]
+anyio = ">=3.5.0,<5"
+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+typing-extensions = ">=4.7,<5"
+
 [[package]]
 name = "grpc-google-iam-v1"
 version = "0.13.1"
@@ -2044,13 +2076,13 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.24.3"
+version = "0.24.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.24.3-py3-none-any.whl", hash = "sha256:69ecce486dd6cdad69937ba76779e893c224a670a9d947636c1d5cbd049e44d8"},
-    {file = "huggingface_hub-0.24.3.tar.gz", hash = "sha256:bfdc05cc9b64a0e24e8614a44222698799183268f6b68be209aa2df70cff2cde"},
+    {file = "huggingface_hub-0.24.0-py3-none-any.whl", hash = "sha256:7ad92edefb93d8145c061f6df8d99df2ff85f8379ba5fac8a95aca0642afa5d7"},
+    {file = "huggingface_hub-0.24.0.tar.gz", hash = "sha256:6c7092736b577d89d57b3cdfea026f1b0dc2234ae783fa0d59caf1bf7d52dfa7"},
 ]
 
 [package.dependencies]
@@ -2128,22 +2160,22 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "8.0.0"
+version = "7.1.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-8.0.0-py3-none-any.whl", hash = "sha256:15584cf2b1bf449d98ff8a6ff1abef57bf20f3ac6454f431736cd3e660921b2f"},
-    {file = "importlib_metadata-8.0.0.tar.gz", hash = "sha256:188bd24e4c346d3f0a933f275c2fec67050326a856b9a359881d7c2a697e8812"},
+    {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
+    {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "importlib-resources"
@@ -2423,19 +2455,19 @@ tests = ["aiohttp", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "py
 
 [[package]]
 name = "langchain"
-version = "0.2.11"
+version = "0.2.10"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain-0.2.11-py3-none-any.whl", hash = "sha256:5a7a8b4918f3d3bebce9b4f23b92d050699e6f7fb97591e8941177cf07a260a2"},
-    {file = "langchain-0.2.11.tar.gz", hash = "sha256:d7a9e4165f02dca0bd78addbc2319d5b9286b5d37c51d784124102b57e9fd297"},
+    {file = "langchain-0.2.10-py3-none-any.whl", hash = "sha256:b4fb58c7faf4f4999cfe3325474979a7121a1737dd101655a723a1d957ef0617"},
+    {file = "langchain-0.2.10.tar.gz", hash = "sha256:1f861c1b59ac9c91b02bb0fa58d3adad1c1d0686636872b5b357bbce3ce41d06"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
-langchain-core = ">=0.2.23,<0.3.0"
+langchain-core = ">=0.2.22,<0.3.0"
 langchain-text-splitters = ">=0.2.0,<0.3.0"
 langsmith = ">=0.1.17,<0.2.0"
 numpy = [
@@ -2471,20 +2503,20 @@ langchain-community = ["langchain-community (>=0.2.4)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.2.10"
+version = "0.2.9"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.2.10-py3-none-any.whl", hash = "sha256:9f4d1b5ab7f0b0a704f538e26e50fce45a461da6d2bf6b7b636d24f22fbc088a"},
-    {file = "langchain_community-0.2.10.tar.gz", hash = "sha256:3a0404bad4bd07d6f86affdb62fb3d080a456c66191754d586a409d9d6024d62"},
+    {file = "langchain_community-0.2.9-py3-none-any.whl", hash = "sha256:b51d3adf9346a1161c1098917585b9e303cf24e2f5c71f5d232a0504edada5f2"},
+    {file = "langchain_community-0.2.9.tar.gz", hash = "sha256:1e7c180232916cbe35fe00509680dd1f805e32d7c87b5e80b3a9ec8754ecae37"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
 langchain = ">=0.2.9,<0.3.0"
-langchain-core = ">=0.2.23,<0.3.0"
+langchain-core = ">=0.2.22,<0.3.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = [
     {version = ">=1,<2", markers = "python_version < \"3.12\""},
@@ -2497,13 +2529,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.24"
+version = "0.2.22"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.2.24-py3-none-any.whl", hash = "sha256:9444fc082d21ef075d925590a684a73fe1f9688a3d90087580ec929751be55e7"},
-    {file = "langchain_core-0.2.24.tar.gz", hash = "sha256:f2e3fa200b124e8c45d270da9bf836bed9c09532612c96ff3225e59b9a232f5a"},
+    {file = "langchain_core-0.2.22-py3-none-any.whl", hash = "sha256:7731a86440c0958b3186c003fb9b26b2d5a682a6344bda7bfb9174e2898f8b43"},
+    {file = "langchain_core-0.2.22.tar.gz", hash = "sha256:582d6f929a43b830139444e4124123cd415331ad62f25757b1406252958cdcac"},
 ]
 
 [package.dependencies]
@@ -2519,13 +2551,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-experimental"
-version = "0.0.63"
+version = "0.0.62"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_experimental-0.0.63-py3-none-any.whl", hash = "sha256:cb4ae7a685bb3c077d138b4533ed02e8df1f5f784333c3e52dcae8c80f031ca2"},
-    {file = "langchain_experimental-0.0.63.tar.gz", hash = "sha256:fc894599bfac43445004a9ff60d9a28751426b2fea1979e4b2fa453c847850c4"},
+    {file = "langchain_experimental-0.0.62-py3-none-any.whl", hash = "sha256:9240f9e3490e819976f20a37863970036e7baacb7104b9eb6833d19ab6d518c9"},
+    {file = "langchain_experimental-0.0.62.tar.gz", hash = "sha256:9737fbc8429d24457ea4d368e3c9ba9ed1cace0564fb5f1a96a3027a588bd0ac"},
 ]
 
 [package.dependencies]
@@ -2534,17 +2566,17 @@ langchain-core = ">=0.2.10,<0.3.0"
 
 [[package]]
 name = "langchain-openai"
-version = "0.1.19"
+version = "0.1.17"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_openai-0.1.19-py3-none-any.whl", hash = "sha256:a7a739f1469d54cd988865420e7fc21b50fb93727b2e6da5ad30273fc61ecf19"},
-    {file = "langchain_openai-0.1.19.tar.gz", hash = "sha256:3bf342bb302d1444f4abafdf01c467dbd9b248497e1133808c4bae70396c79b3"},
+    {file = "langchain_openai-0.1.17-py3-none-any.whl", hash = "sha256:30bef5574ecbbbb91b8025b2dc5a1bd81fd62157d3ad1a35d820141f31c5b443"},
+    {file = "langchain_openai-0.1.17.tar.gz", hash = "sha256:c5d70ddecdcb93e146f376bdbadbb6ec69de9ac0f402cd5b83de50b655ba85ee"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.2.24,<0.3.0"
+langchain-core = ">=0.2.20,<0.3.0"
 openai = ">=1.32.0,<2.0.0"
 tiktoken = ">=0.7,<1"
 
@@ -2740,20 +2772,23 @@ files = [
 
 [[package]]
 name = "mem0ai"
-version = "0.0.9"
+version = "0.0.5"
 description = "Long-term memory for AI Agents"
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "mem0ai-0.0.9-py3-none-any.whl", hash = "sha256:d4de435729af4fd3d597d022ffb2af89a0630d6c3b4769792bbe27d2ce816858"},
-    {file = "mem0ai-0.0.9.tar.gz", hash = "sha256:e4374d5d04aa3f543cd3325f700e4b62f5358ae1c6fa5c44b2ff790c10c4e5f1"},
+    {file = "mem0ai-0.0.5-py3-none-any.whl", hash = "sha256:6f6e5356fd522adf0510322cd581476ea456fd7ccefca11b5ac050e9a6f00f36"},
+    {file = "mem0ai-0.0.5.tar.gz", hash = "sha256:f2ac35d15e4e620becb8d06b8ebeb1ffa85fac0b7cb2d3138056babec48dd5dd"},
 ]
 
 [package.dependencies]
+boto3 = ">=1.34.144,<2.0.0"
+groq = ">=0.9.0,<0.10.0"
 openai = ">=1.33.0,<2.0.0"
 posthog = ">=3.5.0,<4.0.0"
 pydantic = ">=2.7.3,<3.0.0"
 qdrant-client = ">=1.9.1,<2.0.0"
+together = ">=1.2.1,<2.0.0"
 
 [[package]]
 name = "mergedeep"
@@ -2830,13 +2865,13 @@ pyyaml = ">=5.1"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.30"
+version = "9.5.29"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.30-py3-none-any.whl", hash = "sha256:fc070689c5250a180e9b9d79d8491ef9a3a7acb240db0728728d6c31eeb131d4"},
-    {file = "mkdocs_material-9.5.30.tar.gz", hash = "sha256:3fd417dd42d679e3ba08b9e2d72cd8b8af142cc4a3969676ad6b00993dd182ec"},
+    {file = "mkdocs_material-9.5.29-py3-none-any.whl", hash = "sha256:afc1f508e2662ded95f0a35a329e8a5acd73ee88ca07ba73836eb6fcdae5d8b4"},
+    {file = "mkdocs_material-9.5.29.tar.gz", hash = "sha256:3e977598ec15a4ddad5c4dfc9e08edab6023edb51e88f0729bd27be77e3d322a"},
 ]
 
 [package.dependencies]
@@ -3302,13 +3337,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.37.1"
+version = "1.36.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.37.1-py3-none-any.whl", hash = "sha256:9a6adda0d6ae8fce02d235c5671c399cfa40d6a281b3628914c7ebf244888ee3"},
-    {file = "openai-1.37.1.tar.gz", hash = "sha256:faf87206785a6b5d9e34555d6a3242482a6852bc802e453e2a891f68ee04ce55"},
+    {file = "openai-1.36.0-py3-none-any.whl", hash = "sha256:82b74ded1fe2ea94abb19a007178bc143675f1b6903cebd63e2968d654bb0a6f"},
+    {file = "openai-1.36.0.tar.gz", hash = "sha256:a124baf0e1657d6156e12248642f88489cd030be8655b69bc1c13eb50e71a93d"},
 ]
 
 [package.dependencies]
@@ -3325,42 +3360,42 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.26.0"
+version = "1.25.0"
 description = "OpenTelemetry Python API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_api-1.26.0-py3-none-any.whl", hash = "sha256:7d7ea33adf2ceda2dd680b18b1677e4152000b37ca76e679da71ff103b943064"},
-    {file = "opentelemetry_api-1.26.0.tar.gz", hash = "sha256:2bd639e4bed5b18486fef0b5a520aaffde5a18fc225e808a1ac4df363f43a1ce"},
+    {file = "opentelemetry_api-1.25.0-py3-none-any.whl", hash = "sha256:757fa1aa020a0f8fa139f8959e53dec2051cc26b832e76fa839a6d76ecefd737"},
+    {file = "opentelemetry_api-1.25.0.tar.gz", hash = "sha256:77c4985f62f2614e42ce77ee4c9da5fa5f0bc1e1821085e9a47533a9323ae869"},
 ]
 
 [package.dependencies]
 deprecated = ">=1.2.6"
-importlib-metadata = ">=6.0,<=8.0.0"
+importlib-metadata = ">=6.0,<=7.1"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.26.0"
+version = "1.25.0"
 description = "OpenTelemetry Protobuf encoding"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_common-1.26.0-py3-none-any.whl", hash = "sha256:ee4d8f8891a1b9c372abf8d109409e5b81947cf66423fd998e56880057afbc71"},
-    {file = "opentelemetry_exporter_otlp_proto_common-1.26.0.tar.gz", hash = "sha256:bdbe50e2e22a1c71acaa0c8ba6efaadd58882e5a5978737a44a4c4b10d304c92"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.25.0-py3-none-any.whl", hash = "sha256:15637b7d580c2675f70246563363775b4e6de947871e01d0f4e3881d1848d693"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.25.0.tar.gz", hash = "sha256:c93f4e30da4eee02bacd1e004eb82ce4da143a2f8e15b987a9f603e0a85407d3"},
 ]
 
 [package.dependencies]
-opentelemetry-proto = "1.26.0"
+opentelemetry-proto = "1.25.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.26.0"
+version = "1.25.0"
 description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.26.0-py3-none-any.whl", hash = "sha256:e2be5eff72ebcb010675b818e8d7c2e7d61ec451755b8de67a140bc49b9b0280"},
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.26.0.tar.gz", hash = "sha256:a65b67a9a6b06ba1ec406114568e21afe88c1cdb29c464f2507d529eb906d8ae"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0-py3-none-any.whl", hash = "sha256:3131028f0c0a155a64c430ca600fd658e8e37043cb13209f0109db5c1a3e4eb4"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0.tar.gz", hash = "sha256:c0b1661415acec5af87625587efa1ccab68b873745ca0ee96b69bb1042087eac"},
 ]
 
 [package.dependencies]
@@ -3368,39 +3403,39 @@ deprecated = ">=1.2.6"
 googleapis-common-protos = ">=1.52,<2.0"
 grpcio = ">=1.0.0,<2.0.0"
 opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.26.0"
-opentelemetry-proto = "1.26.0"
-opentelemetry-sdk = ">=1.26.0,<1.27.0"
+opentelemetry-exporter-otlp-proto-common = "1.25.0"
+opentelemetry-proto = "1.25.0"
+opentelemetry-sdk = ">=1.25.0,<1.26.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-http"
-version = "1.26.0"
+version = "1.25.0"
 description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_http-1.26.0-py3-none-any.whl", hash = "sha256:ee72a87c48ec977421b02f16c52ea8d884122470e0be573905237b540f4ee562"},
-    {file = "opentelemetry_exporter_otlp_proto_http-1.26.0.tar.gz", hash = "sha256:5801ebbcf7b527377883e6cbbdda35ee712dc55114fff1e93dfee210be56c908"},
+    {file = "opentelemetry_exporter_otlp_proto_http-1.25.0-py3-none-any.whl", hash = "sha256:2eca686ee11b27acd28198b3ea5e5863a53d1266b91cda47c839d95d5e0541a6"},
+    {file = "opentelemetry_exporter_otlp_proto_http-1.25.0.tar.gz", hash = "sha256:9f8723859e37c75183ea7afa73a3542f01d0fd274a5b97487ea24cb683d7d684"},
 ]
 
 [package.dependencies]
 deprecated = ">=1.2.6"
 googleapis-common-protos = ">=1.52,<2.0"
 opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.26.0"
-opentelemetry-proto = "1.26.0"
-opentelemetry-sdk = ">=1.26.0,<1.27.0"
+opentelemetry-exporter-otlp-proto-common = "1.25.0"
+opentelemetry-proto = "1.25.0"
+opentelemetry-sdk = ">=1.25.0,<1.26.0"
 requests = ">=2.7,<3.0"
 
 [[package]]
 name = "opentelemetry-instrumentation"
-version = "0.47b0"
+version = "0.46b0"
 description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation-0.47b0-py3-none-any.whl", hash = "sha256:88974ee52b1db08fc298334b51c19d47e53099c33740e48c4f084bd1afd052d5"},
-    {file = "opentelemetry_instrumentation-0.47b0.tar.gz", hash = "sha256:96f9885e450c35e3f16a4f33145f2ebf620aea910c9fd74a392bbc0f807a350f"},
+    {file = "opentelemetry_instrumentation-0.46b0-py3-none-any.whl", hash = "sha256:89cd721b9c18c014ca848ccd11181e6b3fd3f6c7669e35d59c48dc527408c18b"},
+    {file = "opentelemetry_instrumentation-0.46b0.tar.gz", hash = "sha256:974e0888fb2a1e01c38fbacc9483d024bb1132aad92d6d24e2e5543887a7adda"},
 ]
 
 [package.dependencies]
@@ -3410,55 +3445,55 @@ wrapt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "opentelemetry-instrumentation-asgi"
-version = "0.47b0"
+version = "0.46b0"
 description = "ASGI instrumentation for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_asgi-0.47b0-py3-none-any.whl", hash = "sha256:b798dc4957b3edc9dfecb47a4c05809036a4b762234c5071212fda39ead80ade"},
-    {file = "opentelemetry_instrumentation_asgi-0.47b0.tar.gz", hash = "sha256:e78b7822c1bca0511e5e9610ec484b8994a81670375e570c76f06f69af7c506a"},
+    {file = "opentelemetry_instrumentation_asgi-0.46b0-py3-none-any.whl", hash = "sha256:f13c55c852689573057837a9500aeeffc010c4ba59933c322e8f866573374759"},
+    {file = "opentelemetry_instrumentation_asgi-0.46b0.tar.gz", hash = "sha256:02559f30cf4b7e2a737ab17eb52aa0779bcf4cc06573064f3e2cb4dcc7d3040a"},
 ]
 
 [package.dependencies]
 asgiref = ">=3.0,<4.0"
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.47b0"
-opentelemetry-semantic-conventions = "0.47b0"
-opentelemetry-util-http = "0.47b0"
+opentelemetry-instrumentation = "0.46b0"
+opentelemetry-semantic-conventions = "0.46b0"
+opentelemetry-util-http = "0.46b0"
 
 [package.extras]
 instruments = ["asgiref (>=3.0,<4.0)"]
 
 [[package]]
 name = "opentelemetry-instrumentation-fastapi"
-version = "0.47b0"
+version = "0.46b0"
 description = "OpenTelemetry FastAPI Instrumentation"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_fastapi-0.47b0-py3-none-any.whl", hash = "sha256:5ac28dd401160b02e4f544a85a9e4f61a8cbe5b077ea0379d411615376a2bd21"},
-    {file = "opentelemetry_instrumentation_fastapi-0.47b0.tar.gz", hash = "sha256:0c7c10b5d971e99a420678ffd16c5b1ea4f0db3b31b62faf305fbb03b4ebee36"},
+    {file = "opentelemetry_instrumentation_fastapi-0.46b0-py3-none-any.whl", hash = "sha256:e0f5d150c6c36833dd011f0e6ef5ede6d7406c1aed0c7c98b2d3b38a018d1b33"},
+    {file = "opentelemetry_instrumentation_fastapi-0.46b0.tar.gz", hash = "sha256:928a883a36fc89f9702f15edce43d1a7104da93d740281e32d50ffd03dbb4365"},
 ]
 
 [package.dependencies]
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.47b0"
-opentelemetry-instrumentation-asgi = "0.47b0"
-opentelemetry-semantic-conventions = "0.47b0"
-opentelemetry-util-http = "0.47b0"
+opentelemetry-instrumentation = "0.46b0"
+opentelemetry-instrumentation-asgi = "0.46b0"
+opentelemetry-semantic-conventions = "0.46b0"
+opentelemetry-util-http = "0.46b0"
 
 [package.extras]
-instruments = ["fastapi (>=0.58,<1.0)", "fastapi-slim (>=0.111.0,<0.112.0)"]
+instruments = ["fastapi (>=0.58,<1.0)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.26.0"
+version = "1.25.0"
 description = "OpenTelemetry Python Proto"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_proto-1.26.0-py3-none-any.whl", hash = "sha256:6c4d7b4d4d9c88543bcf8c28ae3f8f0448a753dc291c18c5390444c90b76a725"},
-    {file = "opentelemetry_proto-1.26.0.tar.gz", hash = "sha256:c5c18796c0cab3751fc3b98dee53855835e90c0422924b484432ac852d93dc1e"},
+    {file = "opentelemetry_proto-1.25.0-py3-none-any.whl", hash = "sha256:f07e3341c78d835d9b86665903b199893befa5e98866f63d22b00d0b7ca4972f"},
+    {file = "opentelemetry_proto-1.25.0.tar.gz", hash = "sha256:35b6ef9dc4a9f7853ecc5006738ad40443701e52c26099e197895cbda8b815a3"},
 ]
 
 [package.dependencies]
@@ -3466,44 +3501,43 @@ protobuf = ">=3.19,<5.0"
 
 [[package]]
 name = "opentelemetry-sdk"
-version = "1.26.0"
+version = "1.25.0"
 description = "OpenTelemetry Python SDK"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_sdk-1.26.0-py3-none-any.whl", hash = "sha256:feb5056a84a88670c041ea0ded9921fca559efec03905dddeb3885525e0af897"},
-    {file = "opentelemetry_sdk-1.26.0.tar.gz", hash = "sha256:c90d2868f8805619535c05562d699e2f4fb1f00dbd55a86dcefca4da6fa02f85"},
+    {file = "opentelemetry_sdk-1.25.0-py3-none-any.whl", hash = "sha256:d97ff7ec4b351692e9d5a15af570c693b8715ad78b8aafbec5c7100fe966b4c9"},
+    {file = "opentelemetry_sdk-1.25.0.tar.gz", hash = "sha256:ce7fc319c57707ef5bf8b74fb9f8ebdb8bfafbe11898410e0d2a761d08a98ec7"},
 ]
 
 [package.dependencies]
-opentelemetry-api = "1.26.0"
-opentelemetry-semantic-conventions = "0.47b0"
+opentelemetry-api = "1.25.0"
+opentelemetry-semantic-conventions = "0.46b0"
 typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "opentelemetry-semantic-conventions"
-version = "0.47b0"
+version = "0.46b0"
 description = "OpenTelemetry Semantic Conventions"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_semantic_conventions-0.47b0-py3-none-any.whl", hash = "sha256:4ff9d595b85a59c1c1413f02bba320ce7ea6bf9e2ead2b0913c4395c7bbc1063"},
-    {file = "opentelemetry_semantic_conventions-0.47b0.tar.gz", hash = "sha256:a8d57999bbe3495ffd4d510de26a97dadc1dace53e0275001b2c1b2f67992a7e"},
+    {file = "opentelemetry_semantic_conventions-0.46b0-py3-none-any.whl", hash = "sha256:6daef4ef9fa51d51855d9f8e0ccd3a1bd59e0e545abe99ac6203804e36ab3e07"},
+    {file = "opentelemetry_semantic_conventions-0.46b0.tar.gz", hash = "sha256:fbc982ecbb6a6e90869b15c1673be90bd18c8a56ff1cffc0864e38e2edffaefa"},
 ]
 
 [package.dependencies]
-deprecated = ">=1.2.6"
-opentelemetry-api = "1.26.0"
+opentelemetry-api = "1.25.0"
 
 [[package]]
 name = "opentelemetry-util-http"
-version = "0.47b0"
+version = "0.46b0"
 description = "Web util for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_util_http-0.47b0-py3-none-any.whl", hash = "sha256:3d3215e09c4a723b12da6d0233a31395aeb2bb33a64d7b15a1500690ba250f19"},
-    {file = "opentelemetry_util_http-0.47b0.tar.gz", hash = "sha256:352a07664c18eef827eb8ddcbd64c64a7284a39dd1655e2f16f577eb046ccb32"},
+    {file = "opentelemetry_util_http-0.46b0-py3-none-any.whl", hash = "sha256:8dc1949ce63caef08db84ae977fdc1848fe6dc38e6bbaad0ae3e6ecd0d451629"},
+    {file = "opentelemetry_util_http-0.46b0.tar.gz", hash = "sha256:03b6e222642f9c7eae58d9132343e045b50aca9761fcb53709bd2b663571fdf6"},
 ]
 
 [[package]]
@@ -3882,13 +3916,13 @@ test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint"
 
 [[package]]
 name = "pre-commit"
-version = "3.8.0"
+version = "3.7.1"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -3917,22 +3951,22 @@ testing = ["google-api-core (>=1.31.5)"]
 
 [[package]]
 name = "protobuf"
-version = "4.25.4"
+version = "4.25.3"
 description = ""
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "protobuf-4.25.3-cp39-cp39-win32.whl", hash = "sha256:19b270aeaa0099f16d3ca02628546b8baefe2955bbe23224aaf856134eccf1e4"},
+    {file = "protobuf-4.25.3-cp39-cp39-win_amd64.whl", hash = "sha256:e3c97a1555fd6388f857770ff8b9703083de6bf1f9274a002a332d65fbb56c8c"},
+    {file = "protobuf-4.25.3-py3-none-any.whl", hash = "sha256:f0700d54bcf45424477e46a9f0944155b46fb0639d69728739c0e47bab83f2b9"},
+    {file = "protobuf-4.25.3.tar.gz", hash = "sha256:25b5d0b42fd000320bd7830b349e3b696435f3b329810427a6bcce6a5492cc5c"},
 ]
 
 [[package]]
@@ -4051,19 +4085,6 @@ files = [
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-    {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7c7916bff914ac5d4a8fe25b7a25e432ff921e72f6f2b7547d1e325c1ad9d155"},
-    {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f553ca691b9e94b202ff741bdd40f6ccb70cdd5fbf65c187af132f1317de6145"},
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-    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a155acc7f154b9ffcc85497509bcd0d43efb80d6f733b0dc3bb14e281f131c8b"},
-    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:dec8d129254d0188a49f8a1fc99e0560dc1b85f60af729f47de4046015f9b0a5"},
-    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:a48ddf5c3c6a6c505904545c25a4ae13646ae1f8ba703c4df4a1bfe4f4006bda"},
-    {file = "pyarrow-17.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:42bf93249a083aca230ba7e2786c5f673507fa97bbd9725a1e2754715151a204"},
-    {file = "pyarrow-17.0.0.tar.gz", hash = "sha256:4beca9521ed2c0921c1023e68d097d0299b62c362639ea315572a58f3f50fd28"},
 ]
 
 [package.dependencies]
@@ -4282,13 +4303,13 @@ torch = ["torch"]
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.9"
+version = "10.8.1"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pymdown_extensions-10.9-py3-none-any.whl", hash = "sha256:d323f7e90d83c86113ee78f3fe62fc9dee5f56b54d912660703ea1816fed5626"},
-    {file = "pymdown_extensions-10.9.tar.gz", hash = "sha256:6ff740bcd99ec4172a938970d42b96128bdc9d4b9bcad72494f29921dc69b753"},
+    {file = "pymdown_extensions-10.8.1-py3-none-any.whl", hash = "sha256:f938326115884f48c6059c67377c46cf631c733ef3629b6eed1349989d1b30cb"},
+    {file = "pymdown_extensions-10.8.1.tar.gz", hash = "sha256:3ab1db5c9e21728dabf75192d71471f8e50f216627e9a1fa9535ecb0231b9940"},
 ]
 
 [package.dependencies]
@@ -4300,13 +4321,13 @@ extra = ["pygments (>=2.12)"]
 
 [[package]]
 name = "pypdf"
-version = "4.3.1"
+version = "4.3.0"
 description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "pypdf-4.3.1-py3-none-any.whl", hash = "sha256:64b31da97eda0771ef22edb1bfecd5deee4b72c3d1736b7df2689805076d6418"},
-    {file = "pypdf-4.3.1.tar.gz", hash = "sha256:b2f37fe9a3030aa97ca86067a56ba3f9d3565f9a791b305c7355d8392c30d91b"},
+    {file = "pypdf-4.3.0-py3-none-any.whl", hash = "sha256:eeea4d019b57c099d02a0e1692eaaab23341ae3f255c1dafa3c8566b4636496d"},
+    {file = "pypdf-4.3.0.tar.gz", hash = "sha256:0d7a4c67fd03782f5a09d3f48c11c7a31e0bb9af78861a25229bb49259ed0504"},
 ]
 
 [package.dependencies]
@@ -4353,13 +4374,13 @@ files = [
 
 [[package]]
 name = "pyright"
-version = "1.1.373"
+version = "1.1.372"
 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pyright-1.1.373-py3-none-any.whl", hash = "sha256:b805413227f2c209f27b14b55da27fe5e9fb84129c9f1eb27708a5d12f6f000e"},
-    {file = "pyright-1.1.373.tar.gz", hash = "sha256:f41bcfc8b9d1802b09921a394d6ae1ce19694957b628bc657629688daf8a83ff"},
+    {file = "pyright-1.1.372-py3-none-any.whl", hash = "sha256:25b15fb8967740f0949fd35b963777187f0a0404c0bd753cc966ec139f3eaa0b"},
+    {file = "pyright-1.1.372.tar.gz", hash = "sha256:a9f5e0daa955daaa17e3d1ef76d3623e75f8afd5e37b437d3ff84d5b38c15420"},
 ]
 
 [package.dependencies]
@@ -4393,13 +4414,13 @@ files = [
 
 [[package]]
 name = "pytest"
-version = "8.3.2"
+version = "8.2.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.3.2-py3-none-any.whl", hash = "sha256:4ba08f9ae7dcf84ded419494d229b48d0903ea6407b030eaec46df5e6a73bba5"},
-    {file = "pytest-8.3.2.tar.gz", hash = "sha256:c132345d12ce551242c87269de812483f5bcc87cdbb4722e48487ba194f9fdce"},
+    {file = "pytest-8.2.2-py3-none-any.whl", hash = "sha256:c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343"},
+    {file = "pytest-8.2.2.tar.gz", hash = "sha256:de4bb8104e201939ccdc688b27a89a7be2079b22e2bd2b07f806b6ba71117977"},
 ]
 
 [package.dependencies]
@@ -4407,30 +4428,12 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.5,<2"
+pluggy = ">=1.5,<2.0"
 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
-[[package]]
-name = "pytest-asyncio"
-version = "0.23.8"
-description = "Pytest support for asyncio"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pytest_asyncio-0.23.8-py3-none-any.whl", hash = "sha256:50265d892689a5faefb84df80819d1ecef566eb3549cf915dfb33569359d1ce2"},
-    {file = "pytest_asyncio-0.23.8.tar.gz", hash = "sha256:759b10b33a6dc61cce40a8bd5205e302978bbbcc00e279a8b61d9a6a3c82e4d3"},
-]
-
-[package.dependencies]
-pytest = ">=7.0.0,<9"
-
-[package.extras]
-docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
-testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
-
 [[package]]
 name = "pytest-vcr"
 version = "1.0.2"
@@ -4866,22 +4869,22 @@ files = [
 
 [[package]]
 name = "selenium"
-version = "4.23.1"
+version = "4.22.0"
 description = "Official Python bindings for Selenium WebDriver"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "selenium-4.23.1-py3-none-any.whl", hash = "sha256:3a8d9f23dc636bd3840dd56f00c2739e32ec0c1e34a821dd553e15babef24477"},
-    {file = "selenium-4.23.1.tar.gz", hash = "sha256:128d099e66284437e7128d2279176ec7a06e6ec7426e167f5d34987166bd8f46"},
+    {file = "selenium-4.22.0-py3-none-any.whl", hash = "sha256:e424991196e9857e19bf04fe5c1c0a4aac076794ff5e74615b1124e729d93104"},
+    {file = "selenium-4.22.0.tar.gz", hash = "sha256:903c8c9d61b3eea6fcc9809dc7d9377e04e2ac87709876542cc8f863e482c4ce"},
 ]
 
 [package.dependencies]
 certifi = ">=2021.10.8"
 trio = ">=0.17,<1.0"
 trio-websocket = ">=0.9,<1.0"
-typing_extensions = ">=4.9,<5.0"
+typing_extensions = ">=4.9.0"
 urllib3 = {version = ">=1.26,<3", extras = ["socks"]}
-websocket-client = ">=1.8,<2.0"
+websocket-client = ">=1.8.0"
 
 [[package]]
 name = "semver"
@@ -4896,19 +4899,19 @@ files = [
 
 [[package]]
 name = "setuptools"
-version = "72.1.0"
+version = "71.0.4"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "setuptools-72.1.0-py3-none-any.whl", hash = "sha256:5a03e1860cf56bb6ef48ce186b0e557fdba433237481a9a625176c2831be15d1"},
-    {file = "setuptools-72.1.0.tar.gz", hash = "sha256:8d243eff56d095e5817f796ede6ae32941278f542e0f941867cc05ae52b162ec"},
+    {file = "setuptools-71.0.4-py3-none-any.whl", hash = "sha256:ed2feca703be3bdbd94e6bb17365d91c6935c6b2a8d0bb09b66a2c435ba0b1a5"},
+    {file = "setuptools-71.0.4.tar.gz", hash = "sha256:48297e5d393a62b7cb2a10b8f76c63a73af933bd809c9e0d0d6352a1a0135dd8"},
 ]
 
 [package.extras]
 core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.text (>=3.7)", "more-itertools (>=8.8)", "ordered-set (>=3.1.1)", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.11.*)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "shapely"
@@ -5251,6 +5254,34 @@ webencodings = ">=0.4"
 doc = ["sphinx", "sphinx_rtd_theme"]
 test = ["pytest", "ruff"]
 
+[[package]]
+name = "together"
+version = "1.2.2"
+description = "Python client for Together's Cloud Platform!"
+optional = false
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "together-1.2.2-py3-none-any.whl", hash = "sha256:7ce89f902dbaca67e46e693d90182514494f510f3bc16cb89d816a5031ab0433"},
+    {file = "together-1.2.2.tar.gz", hash = "sha256:fd026f4a604e1fb3ee2fa5803f31e5e36ad31b3d182ef47f611326de66907d13"},
+]
+
+[package.dependencies]
+aiohttp = ">=3.9.3,<4.0.0"
+click = ">=8.1.7,<9.0.0"
+eval-type-backport = ">=0.1.3,<0.3.0"
+filelock = ">=3.13.1,<4.0.0"
+numpy = [
+    {version = ">=1.23.5", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
+]
+pillow = ">=10.3.0,<11.0.0"
+pyarrow = ">=10.0.1"
+pydantic = ">=2.6.3,<3.0.0"
+requests = ">=2.31.0,<3.0.0"
+tabulate = ">=0.9.0,<0.10.0"
+tqdm = ">=4.66.2,<5.0.0"
+typer = ">=0.9,<0.13"
+
 [[package]]
 name = "tokenizers"
 version = "0.19.1"
@@ -6099,4 +6130,4 @@ tools = ["crewai-tools"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<=3.13"
-content-hash = "8df022f5ec0997c0a0f5710476139d9117c1057889c158e958f2c8efd22a4756"
+content-hash = "f5ad9babb3c57c405e39232020e8cbfaaeb5c315c2e7c5bb8fdf66792f260343"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.46.0"
+version = "0.41.1"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
@@ -52,7 +52,6 @@ crewai-tools = "^0.4.26"
 pytest = "^8.0.0"
 pytest-vcr = "^1.0.2"
 python-dotenv = "1.0.0"
-pytest-asyncio = "^0.23.7"
 
 [tool.poetry.scripts]
 crewai = "crewai.cli.cli:crewai"
@@ -60,7 +59,7 @@ crewai = "crewai.cli.cli:crewai"
 [tool.mypy]
 ignore_missing_imports = true
 disable_error_code = 'import-untyped'
-exclude = ["cli/templates"]
+exclude = ["cli/templates/main.py", "cli/templates/crew.py"]
 
 [build-system]
 requires = ["poetry-core"]

src/crewai/__init__.py

@@ -1,7 +1,6 @@
 from crewai.agent import Agent
 from crewai.crew import Crew
-from crewai.pipeline import Pipeline
 from crewai.process import Process
 from crewai.task import Task
 
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline"]
+__all__ = ["Agent", "Crew", "Process", "Task"]

src/crewai/agent.py

@@ -55,6 +55,8 @@ 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)
@@ -260,7 +262,6 @@ class Agent(BaseAgent):
             "tools_handler": self.tools_handler,
             "function_calling_llm": self.function_calling_llm,
             "callbacks": self.callbacks,
-            "max_tokens": self.max_tokens,
         }
 
         if self._rpm_controller:

src/crewai/agents/agent_builder/base_agent.py

@@ -45,7 +45,6 @@ class BaseAgent(ABC, BaseModel):
         i18n (I18N): Internationalization settings.
         cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class.
         tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class.
-        max_tokens: Maximum number of tokens for the agent to generate in a response.
 
 
     Methods:
@@ -119,9 +118,6 @@ class BaseAgent(ABC, BaseModel):
     tools_handler: InstanceOf[ToolsHandler] = Field(
         default=None, description="An instance of the ToolsHandler class."
     )
-    max_tokens: Optional[int] = Field(
-        default=None, description="Maximum number of tokens for the agent's execution."
-    )
 
     _original_role: str | None = None
     _original_goal: str | None = None
@@ -158,7 +154,7 @@ class BaseAgent(ABC, BaseModel):
     @model_validator(mode="after")
     def set_private_attrs(self):
         """Set private attributes."""
-        self._logger = Logger(verbose=self.verbose)
+        self._logger = Logger(self.verbose)
         if self.max_rpm and not self._rpm_controller:
             self._rpm_controller = RPMController(
                 max_rpm=self.max_rpm, logger=self._logger

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Optional
 
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
+from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.utilities.converter import ConverterError
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities import I18N
@@ -38,17 +39,18 @@ class CrewAgentExecutorMixin:
             and "Action: Delegate work to coworker" not in output.log
         ):
             try:
+                memory = ShortTermMemoryItem(
+                    data=output.log,
+                    agent=self.crew_agent.role,
+                    metadata={
+                        "observation": self.task.description,
+                    },
+                )
                 if (
                     hasattr(self.crew, "_short_term_memory")
                     and self.crew._short_term_memory
                 ):
-                    self.crew._short_term_memory.save(
-                        value=output.log,
-                        metadata={
-                            "observation": self.task.description,
-                        },
-                        agent=self.crew_agent.role,
-                    )
+                    self.crew._short_term_memory.save(memory)
             except Exception as e:
                 print(f"Failed to add to short term memory: {e}")
                 pass

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

@@ -1,4 +1,4 @@
-from crewai.types.usage_metrics import UsageMetrics
+from typing import Any, Dict
 
 
 class TokenProcess:
@@ -18,10 +18,10 @@ class TokenProcess:
     def sum_successful_requests(self, requests: int):
         self.successful_requests = self.successful_requests + requests
 
-    def get_summary(self) -> UsageMetrics:
-        return UsageMetrics(
-            total_tokens=self.total_tokens,
-            prompt_tokens=self.prompt_tokens,
-            completion_tokens=self.completion_tokens,
-            successful_requests=self.successful_requests,
-        )
+    def get_summary(self) -> Dict[str, Any]:
+        return {
+            "total_tokens": self.total_tokens,
+            "prompt_tokens": self.prompt_tokens,
+            "completion_tokens": self.completion_tokens,
+            "successful_requests": self.successful_requests,
+        }

src/crewai/agents/executor.py

@@ -1,8 +1,6 @@
 import threading
 import time
-from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
-import click
-
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
@@ -13,21 +11,12 @@ from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
 from pydantic import InstanceOf
 
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.summarize import load_summarize_chain
-
 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.exceptions.context_window_exceeding_exception import (
-    LLMContextLengthExceededException,
-)
 from crewai.utilities.training_handler import CrewTrainingHandler
-from crewai.utilities.logger import Logger
 
 
 class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
@@ -51,8 +40,6 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
     response_template: Optional[str] = None
-    _logger: Logger = Logger()
-    _fit_context_window_strategy: Optional[Literal["summarize"]] = "summarize"
 
     def _call(
         self,
@@ -69,7 +56,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
         )
         intermediate_steps: List[Tuple[AgentAction, str]] = []
         # Allowing human input given task setting
-        if self.task and self.task.human_input:
+        if self.task.human_input:
             self.should_ask_for_human_input = True
 
         # Let's start tracking the number of iterations and time elapsed
@@ -144,7 +131,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
 
             # Call the LLM to see what to do.
-            output = self.agent.plan(
+            output = self.agent.plan(  # type: ignore #  Incompatible types in assignment (expression has type "AgentAction | AgentFinish | list[AgentAction]", variable has type "AgentAction")
                 intermediate_steps,
                 callbacks=run_manager.get_child() if run_manager else None,
                 **inputs,
@@ -198,27 +185,6 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             yield AgentStep(action=output, observation=observation)
             return
 
-        except Exception as e:
-            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
-                str(e)
-            ):
-                output = self._handle_context_length_error(
-                    intermediate_steps, run_manager, inputs
-                )
-
-                if isinstance(output, AgentFinish):
-                    yield output
-                elif isinstance(output, list):
-                    for step in output:
-                        yield step
-                return
-
-            yield AgentStep(
-                action=AgentAction("_Exception", str(e), str(e)),
-                observation=str(e),
-            )
-            return
-
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):
             if self.should_ask_for_human_input:
@@ -269,7 +235,6 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                 agent=self.crew_agent,
                 action=agent_action,
             )
-
             tool_calling = tool_usage.parse(agent_action.log)
 
             if isinstance(tool_calling, ToolUsageErrorException):
@@ -315,91 +280,3 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             CrewTrainingHandler(TRAINING_DATA_FILE).append(
                 self.crew._train_iteration, agent_id, training_data
             )
-
-    def _handle_context_length(
-        self, intermediate_steps: List[Tuple[AgentAction, str]]
-    ) -> List[Tuple[AgentAction, str]]:
-        text = intermediate_steps[0][1]
-        original_action = intermediate_steps[0][0]
-
-        text_splitter = RecursiveCharacterTextSplitter(
-            separators=["\n\n", "\n"],
-            chunk_size=8000,
-            chunk_overlap=500,
-        )
-
-        if self._fit_context_window_strategy == "summarize":
-            docs = text_splitter.create_documents([text])
-            self._logger.log(
-                "debug",
-                "Summarizing Content, it is recommended to use a RAG tool",
-                color="bold_blue",
-            )
-            summarize_chain = load_summarize_chain(
-                self.llm, chain_type="map_reduce", verbose=True
-            )
-            summarized_docs = []
-            for doc in docs:
-                summary = summarize_chain.invoke(
-                    {"input_documents": [doc]}, return_only_outputs=True
-                )
-
-                summarized_docs.append(summary["output_text"])
-
-            formatted_results = "\n\n".join(summarized_docs)
-            summary_step = AgentStep(
-                action=AgentAction(
-                    tool=original_action.tool,
-                    tool_input=original_action.tool_input,
-                    log=original_action.log,
-                ),
-                observation=formatted_results,
-            )
-            summary_tuple = (summary_step.action, summary_step.observation)
-            return [summary_tuple]
-
-        return intermediate_steps
-
-    def _handle_context_length_error(
-        self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        run_manager: Optional[CallbackManagerForChainRun],
-        inputs: Dict[str, str],
-    ) -> Union[AgentFinish, List[AgentStep]]:
-        self._logger.log(
-            "debug",
-            "Context length exceeded. Asking user if they want to use summarize prompt to fit, this will reduce context length.",
-            color="yellow",
-        )
-        user_choice = click.confirm(
-            "Context length exceeded. Do you want to summarize the text to fit models context window?"
-        )
-        if user_choice:
-            self._logger.log(
-                "debug",
-                "Context length exceeded. Using summarize prompt to fit, this will reduce context length.",
-                color="bold_blue",
-            )
-            intermediate_steps = self._handle_context_length(intermediate_steps)
-
-            output = self.agent.plan(
-                intermediate_steps,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **inputs,
-            )
-
-            if isinstance(output, AgentFinish):
-                return output
-            elif isinstance(output, AgentAction):
-                return [AgentStep(action=output, observation=None)]
-            else:
-                return [AgentStep(action=action, observation=None) for action in output]
-        else:
-            self._logger.log(
-                "debug",
-                "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
-                color="red",
-            )
-            raise SystemExit(
-                "Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
-            )

src/crewai/cli/cli.py

@@ -1,16 +1,14 @@
 import click
 import pkg_resources
 
-from crewai.cli.create_crew import create_crew
-from crewai.cli.create_pipeline import create_pipeline
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
 
-from .evaluate_crew import evaluate_crew
+from .create_crew import create_crew
 from .replay_from_task import replay_task_command
 from .reset_memories_command import reset_memories_command
-from .run_crew import run_crew
+from .test_crew import test_crew
 from .train_crew import train_crew
 
 
@@ -20,19 +18,10 @@ def crewai():
 
 
 @crewai.command()
-@click.argument("type", type=click.Choice(["crew", "pipeline"]))
-@click.argument("name")
-@click.option(
-    "--router", is_flag=True, help="Create a pipeline with router functionality"
-)
-def create(type, name, router):
-    """Create a new crew or pipeline."""
-    if type == "crew":
-        create_crew(name)
-    elif type == "pipeline":
-        create_pipeline(name, router)
-    else:
-        click.secho("Error: Invalid type. Must be 'crew' or 'pipeline'.", fg="red")
+@click.argument("project_name")
+def create(project_name):
+    """Create a new crew."""
+    create_crew(project_name)
 
 
 @crewai.command()
@@ -60,17 +49,10 @@ def version(tools):
     default=5,
     help="Number of iterations to train the crew",
 )
-@click.option(
-    "-f",
-    "--filename",
-    type=str,
-    default="trained_agents_data.pkl",
-    help="Path to a custom file for training",
-)
-def train(n_iterations: int, filename: str):
+def train(n_iterations: int):
     """Train the crew."""
-    click.echo(f"Training the Crew for {n_iterations} iterations")
-    train_crew(n_iterations, filename)
+    click.echo(f"Training the crew for {n_iterations} iterations")
+    train_crew(n_iterations)
 
 
 @crewai.command()
@@ -162,14 +144,7 @@ def reset_memories(long, short, entities, kickoff_outputs, all):
 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}")
-    evaluate_crew(n_iterations, model)
-
-
-@crewai.command()
-def run():
-    """Run the crew."""
-    click.echo("Running the crew")
-    run_crew()
+    test_crew(n_iterations, model)
 
 
 if __name__ == "__main__":

src/crewai/cli/create_crew.py

@@ -1,35 +1,25 @@
+import os
 from pathlib import Path
 
 import click
 
-from crewai.cli.utils import copy_template
 
-
-def create_crew(name, parent_folder=None):
+def create_crew(name):
     """Create a new crew."""
     folder_name = name.replace(" ", "_").replace("-", "_").lower()
     class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
 
-    if parent_folder:
-        folder_path = Path(parent_folder) / folder_name
-    else:
-        folder_path = Path(folder_name)
+    click.secho(f"Creating folder {folder_name}...", fg="green", bold=True)
 
-    click.secho(
-        f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
-        fg="green",
-        bold=True,
-    )
-
-    if not folder_path.exists():
-        folder_path.mkdir(parents=True)
-        (folder_path / "tests").mkdir(exist_ok=True)
-        if not parent_folder:
-            (folder_path / "src" / folder_name).mkdir(parents=True)
-            (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
-            (folder_path / "src" / folder_name / "config").mkdir(parents=True)
-            with open(folder_path / ".env", "w") as file:
-                file.write("OPENAI_API_KEY=YOUR_API_KEY")
+    if not os.path.exists(folder_name):
+        os.mkdir(folder_name)
+        os.mkdir(folder_name + "/tests")
+        os.mkdir(folder_name + "/src")
+        os.mkdir(folder_name + f"/src/{folder_name}")
+        os.mkdir(folder_name + f"/src/{folder_name}/tools")
+        os.mkdir(folder_name + f"/src/{folder_name}/config")
+        with open(folder_name + "/.env", "w") as file:
+            file.write("OPENAI_API_KEY=YOUR_API_KEY")
     else:
         click.secho(
             f"\tFolder {folder_name} already exists. Please choose a different name.",
@@ -38,34 +28,53 @@ def create_crew(name, parent_folder=None):
         return
 
     package_dir = Path(__file__).parent
-    templates_dir = package_dir / "templates" / "crew"
+    templates_dir = package_dir / "templates"
 
     # List of template files to copy
-    root_template_files = (
-        [".gitignore", "pyproject.toml", "README.md"] if not parent_folder else []
-    )
+    root_template_files = [
+        ".gitignore",
+        "pyproject.toml",
+        "README.md",
+    ]
     tools_template_files = ["tools/custom_tool.py", "tools/__init__.py"]
     config_template_files = ["config/agents.yaml", "config/tasks.yaml"]
-    src_template_files = (
-        ["__init__.py", "main.py", "crew.py"] if not parent_folder else ["crew.py"]
-    )
+    src_template_files = ["__init__.py", "main.py", "crew.py"]
 
     for file_name in root_template_files:
         src_file = templates_dir / file_name
-        dst_file = folder_path / file_name
+        dst_file = Path(folder_name) / file_name
         copy_template(src_file, dst_file, name, class_name, folder_name)
 
-    src_folder = folder_path / "src" / folder_name if not parent_folder else folder_path
-
     for file_name in src_template_files:
         src_file = templates_dir / file_name
-        dst_file = src_folder / file_name
+        dst_file = Path(folder_name) / "src" / folder_name / file_name
         copy_template(src_file, dst_file, name, class_name, folder_name)
 
-    if not parent_folder:
-        for file_name in tools_template_files + config_template_files:
-            src_file = templates_dir / file_name
-            dst_file = src_folder / file_name
-            copy_template(src_file, dst_file, name, class_name, folder_name)
+    for file_name in tools_template_files:
+        src_file = templates_dir / file_name
+        dst_file = Path(folder_name) / "src" / folder_name / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_name)
+
+    for file_name in config_template_files:
+        src_file = templates_dir / file_name
+        dst_file = Path(folder_name) / "src" / folder_name / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_name)
 
     click.secho(f"Crew {name} created successfully!", fg="green", bold=True)
+
+
+def copy_template(src, dst, name, class_name, folder_name):
+    """Copy a file from src to dst."""
+    with open(src, "r") as file:
+        content = file.read()
+
+    # Interpolate the content
+    content = content.replace("{{name}}", name)
+    content = content.replace("{{crew_name}}", class_name)
+    content = content.replace("{{folder_name}}", folder_name)
+
+    # Write the interpolated content to the new file
+    with open(dst, "w") as file:
+        file.write(content)
+
+    click.secho(f"  - Created {dst}", fg="green")

src/crewai/cli/create_pipeline.py

@@ -1,107 +0,0 @@
-import shutil
-from pathlib import Path
-
-import click
-
-
-def create_pipeline(name, router=False):
-    """Create a new pipeline project."""
-    folder_name = name.replace(" ", "_").replace("-", "_").lower()
-    class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
-
-    click.secho(f"Creating pipeline {folder_name}...", fg="green", bold=True)
-
-    project_root = Path(folder_name)
-    if project_root.exists():
-        click.secho(f"Error: Folder {folder_name} already exists.", fg="red")
-        return
-
-    # Create directory structure
-    (project_root / "src" / folder_name).mkdir(parents=True)
-    (project_root / "src" / folder_name / "pipelines").mkdir(parents=True)
-    (project_root / "src" / folder_name / "crews").mkdir(parents=True)
-    (project_root / "src" / folder_name / "tools").mkdir(parents=True)
-    (project_root / "tests").mkdir(exist_ok=True)
-
-    # Create .env file
-    with open(project_root / ".env", "w") as file:
-        file.write("OPENAI_API_KEY=YOUR_API_KEY")
-
-    package_dir = Path(__file__).parent
-    template_folder = "pipeline_router" if router else "pipeline"
-    templates_dir = package_dir / "templates" / template_folder
-
-    # List of template files to copy
-    root_template_files = [".gitignore", "pyproject.toml", "README.md"]
-    src_template_files = ["__init__.py", "main.py"]
-    tools_template_files = ["tools/__init__.py", "tools/custom_tool.py"]
-
-    if router:
-        crew_folders = [
-            "classifier_crew",
-            "normal_crew",
-            "urgent_crew",
-        ]
-        pipelines_folders = [
-            "pipelines/__init__.py",
-            "pipelines/pipeline_classifier.py",
-            "pipelines/pipeline_normal.py",
-            "pipelines/pipeline_urgent.py",
-        ]
-    else:
-        crew_folders = [
-            "research_crew",
-            "write_linkedin_crew",
-            "write_x_crew",
-        ]
-        pipelines_folders = ["pipelines/__init__.py", "pipelines/pipeline.py"]
-
-    def process_file(src_file, dst_file):
-        with open(src_file, "r") as file:
-            content = file.read()
-
-        content = content.replace("{{name}}", name)
-        content = content.replace("{{crew_name}}", class_name)
-        content = content.replace("{{folder_name}}", folder_name)
-        content = content.replace("{{pipeline_name}}", class_name)
-
-        with open(dst_file, "w") as file:
-            file.write(content)
-
-    # Copy and process root template files
-    for file_name in root_template_files:
-        src_file = templates_dir / file_name
-        dst_file = project_root / file_name
-        process_file(src_file, dst_file)
-
-    # Copy and process src template files
-    for file_name in src_template_files:
-        src_file = templates_dir / file_name
-        dst_file = project_root / "src" / folder_name / file_name
-        process_file(src_file, dst_file)
-
-    # Copy tools files
-    for file_name in tools_template_files:
-        src_file = templates_dir / file_name
-        dst_file = project_root / "src" / folder_name / file_name
-        shutil.copy(src_file, dst_file)
-
-    # Copy pipelines folders
-    for file_name in pipelines_folders:
-        src_file = templates_dir / file_name
-        dst_file = project_root / "src" / folder_name / file_name
-        process_file(src_file, dst_file)
-
-    # Copy crew folders
-    for crew_folder in crew_folders:
-        src_crew_folder = templates_dir / "crews" / crew_folder
-        dst_crew_folder = project_root / "src" / folder_name / "crews" / crew_folder
-        if src_crew_folder.exists():
-            shutil.copytree(src_crew_folder, dst_crew_folder)
-        else:
-            click.secho(
-                f"Warning: Crew folder {crew_folder} not found in template.",
-                fg="yellow",
-            )
-
-    click.secho(f"Pipeline {name} created successfully!", fg="green", bold=True)

src/crewai/cli/reset_memories_command.py

@@ -9,14 +9,10 @@ from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandle
 
 def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
     """
-    Reset the crew memories.
+    Replay the crew execution from a specific task.
 
     Args:
-      long (bool): Whether to reset the long-term memory.
-      short (bool): Whether to reset the short-term memory.
-      entity (bool): Whether to reset the entity memory.
-      kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
-      all (bool): Whether to reset all memories.
+      task_id (str): The ID of the task to replay from.
     """
 
     try:

src/crewai/cli/run_crew.py

@@ -1,23 +0,0 @@
-import subprocess
-
-import click
-
-
-def run_crew() -> None:
-    """
-    Run the crew by running a command in the Poetry environment.
-    """
-    command = ["poetry", "run", "run_crew"]
-
-    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 running 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/cli/templates/config/tasks.yaml

@@ -5,7 +5,6 @@ research_task:
     the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
-  agent: researcher
 
 reporting_task:
   description: >
@@ -14,4 +13,3 @@ reporting_task:
   expected_output: >
     A fully fledge reports with the mains topics, each with a full section of information.
     Formatted as markdown without '```'
-  agent: reporting_analyst

src/crewai/cli/templates/crew.py

@@ -32,12 +32,14 @@ class {{crew_name}}Crew():
 	def research_task(self) -> Task:
 		return Task(
 			config=self.tasks_config['research_task'],
+			agent=self.researcher()
 		)
 
 	@task
 	def reporting_task(self) -> Task:
 		return Task(
 			config=self.tasks_config['reporting_task'],
+			agent=self.reporting_analyst(),
 			output_file='report.md'
 		)
 
@@ -48,6 +50,6 @@ class {{crew_name}}Crew():
 			agents=self.agents, # Automatically created by the @agent decorator
 			tasks=self.tasks, # Automatically created by the @task decorator
 			process=Process.sequential,
-			verbose=True,
+			verbose=2,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
 		)

src/crewai/cli/templates/crew/README.md

@@ -1,61 +0,0 @@
-# {{crew_name}} Crew
-
-Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
-
-## Installation
-
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
-
-First, if you haven't already, install Poetry:
-
-```bash
-pip install poetry
-```
-
-Next, navigate to your project directory and install the dependencies:
-
-1. First lock the dependencies and then install them:
-```bash
-poetry lock
-```
-```bash
-poetry install
-```
-### Customizing
-
-**Add your `OPENAI_API_KEY` into the `.env` file**
-
-- Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
-- Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks
-- Modify `src/{{folder_name}}/crew.py` to add your own logic, tools and specific args
-- Modify `src/{{folder_name}}/main.py` to add custom inputs for your agents and tasks
-
-## Running the Project
-
-To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
-
-```bash
-$ crewai run
-```
-or
-```bash
-poetry run {{folder_name}}
-```
-
-This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
-
-This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
-
-## Understanding Your Crew
-
-The {{name}} Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
-
-## Support
-
-For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
-- Visit our [documentation](https://docs.crewai.com)
-- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
-- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
-- [Chat with our docs](https://chatg.pt/DWjSBZn)
-
-Let's create wonders together with the power and simplicity of crewAI.

src/crewai/cli/templates/main.py

@@ -25,7 +25,7 @@ def train():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
+        {{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}")

src/crewai/cli/templates/pipeline/.gitignore

@@ -1,2 +0,0 @@
-.env
-__pycache__/

src/crewai/cli/templates/pipeline/crews/research_crew/config/agents.yaml

@@ -1,19 +0,0 @@
-researcher:
-  role: >
-    {topic} Senior Data Researcher
-  goal: >
-    Uncover cutting-edge developments in {topic}
-  backstory: >
-    You're a seasoned researcher with a knack for uncovering the latest
-    developments in {topic}. Known for your ability to find the most relevant
-    information and present it in a clear and concise manner.
-
-reporting_analyst:
-  role: >
-    {topic} Reporting Analyst
-  goal: >
-    Create detailed reports based on {topic} data analysis and research findings
-  backstory: >
-    You're a meticulous analyst with a keen eye for detail. You're known for
-    your ability to turn complex data into clear and concise reports, making
-    it easy for others to understand and act on the information you provide.

src/crewai/cli/templates/pipeline/crews/research_crew/config/tasks.yaml

@@ -1,16 +0,0 @@
-research_task:
-  description: >
-    Conduct a thorough research about {topic}
-    Make sure you find any interesting and relevant information given
-    the current year is 2024.
-  expected_output: >
-    A list with 10 bullet points of the most relevant information about {topic}
-  agent: researcher
-
-reporting_task:
-  description: >
-    Review the context you got and expand each topic into a full section for a report.
-    Make sure the report is detailed and contains any and all relevant information.
-  expected_output: >
-    A fully fledge reports with a title, mains topics, each with a full section of information.
-  agent: reporting_analyst

src/crewai/cli/templates/pipeline/crews/research_crew/research_crew.py

@@ -1,58 +0,0 @@
-from pydantic import BaseModel
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-
-# Uncomment the following line to use an example of a custom tool
-# from demo_pipeline.tools.custom_tool import MyCustomTool
-
-# Check our tools documentations for more information on how to use them
-# from crewai_tools import SerperDevTool
-
-
-class ResearchReport(BaseModel):
-	"""Research Report"""
-	title: str
-	body: str
-
-@CrewBase
-class ResearchCrew():
-	"""Research Crew"""
-	agents_config = 'config/agents.yaml'
-	tasks_config = 'config/tasks.yaml'
-
-	@agent
-	def researcher(self) -> Agent:
-		return Agent(
-			config=self.agents_config['researcher'],
-			verbose=True
-		)
-
-	@agent
-	def reporting_analyst(self) -> Agent:
-		return Agent(
-			config=self.agents_config['reporting_analyst'],
-			verbose=True
-		)
-
-	@task
-	def research_task(self) -> Task:
-		return Task(
-			config=self.tasks_config['research_task'],
-		)
-
-	@task
-	def reporting_task(self) -> Task:
-		return Task(
-			config=self.tasks_config['reporting_task'],
-			output_pydantic=ResearchReport
-		)
-
-	@crew
-	def crew(self) -> Crew:
-		"""Creates the Research Crew"""
-		return Crew(
-			agents=self.agents, # Automatically created by the @agent decorator
-			tasks=self.tasks, # Automatically created by the @task decorator
-			process=Process.sequential,
-			verbose=True,
-		)

src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/write_linkedin_crew.py

@@ -1,51 +0,0 @@
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-
-# Uncomment the following line to use an example of a custom tool
-# from {{folder_name}}.tools.custom_tool import MyCustomTool
-
-# Check our tools documentations for more information on how to use them
-# from crewai_tools import SerperDevTool
-
-@CrewBase
-class WriteLinkedInCrew():
-	"""Research Crew"""
-	agents_config = 'config/agents.yaml'
-	tasks_config = 'config/tasks.yaml'
-
-	@agent
-	def researcher(self) -> Agent:
-		return Agent(
-			config=self.agents_config['researcher'],
-			verbose=True
-		)
-
-	@agent
-	def reporting_analyst(self) -> Agent:
-		return Agent(
-			config=self.agents_config['reporting_analyst'],
-			verbose=True
-		)
-
-	@task
-	def research_task(self) -> Task:
-		return Task(
-			config=self.tasks_config['research_task'],
-		)
-
-	@task
-	def reporting_task(self) -> Task:
-		return Task(
-			config=self.tasks_config['reporting_task'],
-			output_file='report.md'
-		)
-
-	@crew
-	def crew(self) -> Crew:
-		"""Creates the {{crew_name}} crew"""
-		return Crew(
-			agents=self.agents, # Automatically created by the @agent decorator
-			tasks=self.tasks, # Automatically created by the @task decorator
-			process=Process.sequential,
-			verbose=True,
-		)

src/crewai/cli/templates/pipeline/crews/write_x_crew/config/agents.yaml

@@ -1,14 +0,0 @@
-x_writer_agent:
-  role: >
-    Expert Social Media Content Creator specializing in short form written content
-  goal: >
-    Create viral-worthy, engaging short form posts that distill complex {topic} information
-    into compelling 280-character messages
-  backstory: >
-    You're a social media virtuoso with a particular talent for short form content. Your posts
-    consistently go viral due to your ability to craft hooks that stop users mid-scroll.
-    You've studied the techniques of social media masters like Justin Welsh, Dickie Bush,
-    Nicolas Cole, and Shaan Puri, incorporating their best practices into your own unique style.
-    Your superpower is taking intricate {topic} concepts and transforming them into
-    bite-sized, shareable content that resonates with a wide audience. You know exactly
-    how to structure a post for maximum impact and engagement.

src/crewai/cli/templates/pipeline/crews/write_x_crew/config/tasks.yaml

@@ -1,22 +0,0 @@
-write_x_task:
-  description: >
-    Using the research report provided, create an engaging short form post about {topic}.
-    Your post should have a great hook, summarize key points, and be structured for easy
-    consumption on a digital platform. The post must be under 280 characters.
-    Follow these guidelines:
-    1. Start with an attention-grabbing hook
-    2. Condense the main insights from the research
-    3. Use clear, concise language
-    4. Include a call-to-action or thought-provoking question if space allows
-    5. Ensure the post flows well and is easy to read quickly
-
-    Here is the title of the research report you will be using
-
-    Title: {title}
-    Research: 
-    {body}
-
-  expected_output: >
-    A compelling X post under 280 characters that effectively summarizes the key findings
-    about {topic}, starts with a strong hook, and is optimized for engagement on the platform.
-  agent: x_writer_agent

src/crewai/cli/templates/pipeline/crews/write_x_crew/write_x_crew.py

@@ -1,36 +0,0 @@
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-
-# Uncomment the following line to use an example of a custom tool
-# from demo_pipeline.tools.custom_tool import MyCustomTool
-
-# Check our tools documentations for more information on how to use them
-# from crewai_tools import SerperDevTool
-
-
-@CrewBase
-class WriteXCrew:
-    """Research Crew"""
-
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
-
-    @agent
-    def x_writer_agent(self) -> Agent:
-        return Agent(config=self.agents_config["x_writer_agent"], verbose=True)
-
-    @task
-    def write_x_task(self) -> Task:
-        return Task(
-            config=self.tasks_config["write_x_task"],
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        """Creates the Write X Crew"""
-        return Crew(
-            agents=self.agents,  # Automatically created by the @agent decorator
-            tasks=self.tasks,  # Automatically created by the @task decorator
-            process=Process.sequential,
-            verbose=True,
-        )

src/crewai/cli/templates/pipeline/main.py

@@ -1,26 +0,0 @@
-#!/usr/bin/env python
-import asyncio
-from {{folder_name}}.pipelines.pipeline import {{pipeline_name}}Pipeline
-
-async def run():
-    """
-    Run the pipeline.
-    """
-    inputs = [
-        {"topic": "AI wearables"},
-    ]
-    pipeline = {{pipeline_name}}Pipeline()
-    results = await pipeline.kickoff(inputs)
-    
-    # Process and print results
-    for result in results:
-        print(f"Raw output: {result.raw}")
-        if result.json_dict:
-            print(f"JSON output: {result.json_dict}")
-        print("\n")
-
-def main():
-    asyncio.run(run())
-
-if __name__ == "__main__":
-    main()

src/crewai/cli/templates/pipeline/pipelines/pipeline.py

@@ -1,87 +0,0 @@
-"""
-This pipeline file includes two different examples to demonstrate the flexibility of crewAI pipelines.
-
-Example 1: Two-Stage Pipeline
------------------------------
-This pipeline consists of two crews:
-1. ResearchCrew: Performs research on a given topic.
-2. WriteXCrew: Generates an X (Twitter) post based on the research findings.
-
-Key features:
-- The ResearchCrew's final task uses output_json to store all research findings in a JSON object.
-- This JSON object is then passed to the WriteXCrew, where tasks can access the research findings.
-
-Example 2: Two-Stage Pipeline with Parallel Execution
--------------------------------------------------------
-This pipeline consists of three crews:
-1. ResearchCrew: Performs research on a given topic.
-2. WriteXCrew and WriteLinkedInCrew: Run in parallel, using the research findings to generate posts for X and LinkedIn, respectively.
-
-Key features:
-- Demonstrates the ability to run multiple crews in parallel.
-- Shows how to structure a pipeline with both sequential and parallel stages.
-
-Usage:
-- To switch between examples, comment/uncomment the respective code blocks below.
-- Ensure that you have implemented all necessary crew classes (ResearchCrew, WriteXCrew, WriteLinkedInCrew) before running.
-"""
-
-# Common imports for both examples
-from crewai import Pipeline
-
-
-
-# Uncomment the crews you need for your chosen example
-from ..crews.research_crew.research_crew import ResearchCrew
-from ..crews.write_x_crew.write_x_crew import WriteXCrew
-# from .crews.write_linkedin_crew.write_linkedin_crew import WriteLinkedInCrew  # Uncomment for Example 2
-
-# EXAMPLE 1: Two-Stage Pipeline
-# -----------------------------
-# Uncomment the following code block to use Example 1
-
-class {{pipeline_name}}Pipeline:
-    def __init__(self):
-        # Initialize crews
-        self.research_crew = ResearchCrew().crew()
-        self.write_x_crew = WriteXCrew().crew()
-    
-    def create_pipeline(self):
-        return Pipeline(
-            stages=[
-                self.research_crew,
-                self.write_x_crew
-            ]
-        )
-    
-    async def kickoff(self, inputs):
-        pipeline = self.create_pipeline()
-        results = await pipeline.kickoff(inputs)
-        return results
-
-
-# EXAMPLE 2: Two-Stage Pipeline with Parallel Execution
-# -------------------------------------------------------
-# Uncomment the following code block to use Example 2
-
-# @PipelineBase
-# class {{pipeline_name}}Pipeline:
-#     def __init__(self):
-#         # Initialize crews
-#         self.research_crew = ResearchCrew().crew()
-#         self.write_x_crew = WriteXCrew().crew()
-#         self.write_linkedin_crew = WriteLinkedInCrew().crew()
-    
-#     @pipeline
-#     def create_pipeline(self):
-#         return Pipeline(
-#             stages=[
-#                 self.research_crew,
-#                 [self.write_x_crew, self.write_linkedin_crew]  # Parallel execution
-#             ]
-#         )
-
-#     async def run(self, inputs):
-#         pipeline = self.create_pipeline()
-#         results = await pipeline.kickoff(inputs)
-#         return results

src/crewai/cli/templates/pipeline/pyproject.toml

@@ -1,17 +0,0 @@
-[tool.poetry]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-
-[tool.poetry.dependencies]
-python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.46.0" }
-asyncio = "*"
-
-[tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

src/crewai/cli/templates/pipeline/tools/custom_tool.py

@@ -1,12 +0,0 @@
-from crewai_tools import BaseTool
-
-
-class MyCustomTool(BaseTool):
-    name: str = "Name of my tool"
-    description: str = (
-        "Clear description for what this tool is useful for, you agent will need this information to use it."
-    )
-
-    def _run(self, argument: str) -> str:
-        # Implementation goes here
-        return "this is an example of a tool output, ignore it and move along."

src/crewai/cli/templates/pipeline_router/.gitignore

@@ -1,2 +0,0 @@
-.env
-__pycache__/

src/crewai/cli/templates/pipeline_router/README.md

@@ -1,57 +0,0 @@
-# {{crew_name}} Crew
-
-Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
-
-## Installation
-
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
-
-First, if you haven't already, install Poetry:
-
-```bash
-pip install poetry
-```
-
-Next, navigate to your project directory and install the dependencies:
-
-1. First lock the dependencies and then install them:
-```bash
-poetry lock
-```
-```bash
-poetry install
-```
-### Customizing
-
-**Add your `OPENAI_API_KEY` into the `.env` file**
-
-- Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
-- Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks
-- Modify `src/{{folder_name}}/crew.py` to add your own logic, tools and specific args
-- Modify `src/{{folder_name}}/main.py` to add custom inputs for your agents and tasks
-
-## Running the Project
-
-To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
-
-```bash
-poetry run {{folder_name}}
-```
-
-This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
-
-This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
-
-## Understanding Your Crew
-
-The {{name}} Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
-
-## Support
-
-For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
-- Visit our [documentation](https://docs.crewai.com)
-- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
-- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
-- [Chat with our docs](https://chatg.pt/DWjSBZn)
-
-Let's create wonders together with the power and simplicity of crewAI.

src/crewai/cli/templates/pipeline_router/config/agents.yaml

@@ -1,19 +0,0 @@
-researcher:
-  role: >
-    {topic} Senior Data Researcher
-  goal: >
-    Uncover cutting-edge developments in {topic}
-  backstory: >
-    You're a seasoned researcher with a knack for uncovering the latest
-    developments in {topic}. Known for your ability to find the most relevant
-    information and present it in a clear and concise manner.
-
-reporting_analyst:
-  role: >
-    {topic} Reporting Analyst
-  goal: >
-    Create detailed reports based on {topic} data analysis and research findings
-  backstory: >
-    You're a meticulous analyst with a keen eye for detail. You're known for
-    your ability to turn complex data into clear and concise reports, making
-    it easy for others to understand and act on the information you provide.

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

@@ -1,17 +0,0 @@
-research_task:
-  description: >
-    Conduct a thorough research about {topic}
-    Make sure you find any interesting and relevant information given
-    the current year is 2024.
-  expected_output: >
-    A list with 10 bullet points of the most relevant information about {topic}
-  agent: researcher
-
-reporting_task:
-  description: >
-    Review the context you got and expand each topic into a full section for a report.
-    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 '```'
-  agent: reporting_analyst

src/crewai/cli/templates/pipeline_router/crews/classifier_crew/classifier_crew.py

@@ -1,40 +0,0 @@
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-from pydantic import BaseModel
-
-# Uncomment the following line to use an example of a custom tool
-# from demo_pipeline.tools.custom_tool import MyCustomTool
-
-# Check our tools documentations for more information on how to use them
-# from crewai_tools import SerperDevTool
-
-class UrgencyScore(BaseModel):
-    urgency_score: int
-
-@CrewBase
-class ClassifierCrew:
-    """Email Classifier Crew"""
-
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
-
-    @agent
-    def classifier(self) -> Agent:
-        return Agent(config=self.agents_config["classifier"], verbose=True)
-
-    @task
-    def urgent_task(self) -> Task:
-        return Task(
-            config=self.tasks_config["classify_email"],
-            output_pydantic=UrgencyScore,
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        """Creates the Email Classifier Crew"""
-        return Crew(
-            agents=self.agents,  # Automatically created by the @agent decorator
-            tasks=self.tasks,  # Automatically created by the @task decorator
-            process=Process.sequential,
-            verbose=True,
-        )

src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/agents.yaml

@@ -1,7 +0,0 @@
-classifier:
-  role: >
-    Email Classifier
-  goal: >
-    Classify the email: {email} as urgent or normal from a score of 1 to 10, where 1 is not urgent and 10 is urgent. Return the urgency score only.`
-  backstory: >
-    You are a highly efficient and experienced email classifier, trained to quickly assess and classify emails. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing normal situations and maintaining smooth operations.

src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/tasks.yaml

@@ -1,7 +0,0 @@
-classify_email:
-  description: >
-    Classify the email: {email}
-    as urgent or normal.
-  expected_output: >
-    Classify the email from a scale of 1 to 10, where 1 is not urgent and 10 is urgent. Return the urgency score only.
-  agent: classifier

src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/agents.yaml

@@ -1,7 +0,0 @@
-normal_handler:
-  role: >
-    Normal Email Processor
-  goal: >
-    Process normal emails and create an email to respond to the sender.
-  backstory: >
-    You are a highly efficient and experienced normal email handler, trained to quickly assess and respond to normal communications. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing normal situations and maintaining smooth operations.

src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/tasks.yaml

@@ -1,6 +0,0 @@
-normal_task:
-  description: >
-    Process and respond to normal email quickly.
-  expected_output: >
-    An email response to the normal email.
-  agent: normal_handler

src/crewai/cli/templates/pipeline_router/crews/normal_crew/normal_crew.py

@@ -1,36 +0,0 @@
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-
-# Uncomment the following line to use an example of a custom tool
-# from demo_pipeline.tools.custom_tool import MyCustomTool
-
-# Check our tools documentations for more information on how to use them
-# from crewai_tools import SerperDevTool
-
-
-@CrewBase
-class NormalCrew:
-    """Normal Email Crew"""
-
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
-
-    @agent
-    def normal_handler(self) -> Agent:
-        return Agent(config=self.agents_config["normal_handler"], verbose=True)
-
-    @task
-    def urgent_task(self) -> Task:
-        return Task(
-            config=self.tasks_config["normal_task"],
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        """Creates the Normal Email Crew"""
-        return Crew(
-            agents=self.agents,  # Automatically created by the @agent decorator
-            tasks=self.tasks,  # Automatically created by the @task decorator
-            process=Process.sequential,
-            verbose=True,
-        )

src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/agents.yaml

@@ -1,7 +0,0 @@
-urgent_handler:
-  role: >
-    Urgent Email Processor
-  goal: >
-    Process urgent emails and create an email to respond to the sender.
-  backstory: >
-    You are a highly efficient and experienced urgent email handler, trained to quickly assess and respond to time-sensitive communications. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing critical situations and maintaining smooth operations.

src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/tasks.yaml

@@ -1,6 +0,0 @@
-urgent_task:
-  description: >
-    Process and respond to urgent email quickly.
-  expected_output: >
-    An email response to the urgent email.
-  agent: urgent_handler

src/crewai/cli/templates/pipeline_router/crews/urgent_crew/urgent_crew.py

@@ -1,36 +0,0 @@
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-
-# Uncomment the following line to use an example of a custom tool
-# from demo_pipeline.tools.custom_tool import MyCustomTool
-
-# Check our tools documentations for more information on how to use them
-# from crewai_tools import SerperDevTool
-
-
-@CrewBase
-class UrgentCrew:
-    """Urgent Email Crew"""
-
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
-
-    @agent
-    def urgent_handler(self) -> Agent:
-        return Agent(config=self.agents_config["urgent_handler"], verbose=True)
-
-    @task
-    def urgent_task(self) -> Task:
-        return Task(
-            config=self.tasks_config["urgent_task"],
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        """Creates the Urgent Email Crew"""
-        return Crew(
-            agents=self.agents,  # Automatically created by the @agent decorator
-            tasks=self.tasks,  # Automatically created by the @task decorator
-            process=Process.sequential,
-            verbose=True,
-        )

src/crewai/cli/templates/pipeline_router/main.py

@@ -1,75 +0,0 @@
-#!/usr/bin/env python
-import asyncio
-from crewai.routers.router import Route
-from crewai.routers.router import Router
-
-from {{folder_name}}.pipelines.pipeline_classifier import EmailClassifierPipeline
-from {{folder_name}}.pipelines.pipeline_normal import NormalPipeline
-from {{folder_name}}.pipelines.pipeline_urgent import UrgentPipeline
-
-async def run():
-    """
-    Run the pipeline.
-    """
-    inputs = [
-       {
-        "email": """
-            Subject: URGENT: Marketing Campaign Launch - Immediate Action Required
-            Dear Team,
-            I'm reaching out regarding our upcoming marketing campaign that requires your immediate attention and swift action. We're facing a critical deadline, and our success hinges on our ability to mobilize quickly.
-            Key points:
-            
-            Campaign launch: 48 hours from now
-            Target audience: 250,000 potential customers
-            Expected ROI: 35% increase in Q3 sales
-            
-            What we need from you NOW:
-            
-            Final approval on creative assets (due in 3 hours)
-            Confirmation of media placements (due by end of day)
-            Last-minute budget allocation for paid social media push
-            
-            Our competitors are poised to launch similar campaigns, and we must act fast to maintain our market advantage. Delays could result in significant lost opportunities and potential revenue.
-            Please prioritize this campaign above all other tasks. I'll be available for the next 24 hours to address any concerns or roadblocks.
-            Let's make this happen!
-            [Your Name]
-            Marketing Director
-            P.S. I'll be scheduling an emergency team meeting in 1 hour to discuss our action plan. Attendance is mandatory.
-        """
-       }
-    ]
-
-    pipeline_classifier = EmailClassifierPipeline().create_pipeline()
-    pipeline_urgent = UrgentPipeline().create_pipeline()
-    pipeline_normal = NormalPipeline().create_pipeline()
-
-    router = Router(
-        routes={
-            "high_urgency": Route(
-                condition=lambda x: x.get("urgency_score", 0) > 7,
-                pipeline=pipeline_urgent
-            ),
-            "low_urgency": Route(
-                condition=lambda x: x.get("urgency_score", 0) <= 7,
-                pipeline=pipeline_normal
-            )
-        },
-        default=pipeline_normal
-    )
-
-    pipeline = pipeline_classifier >> router
-
-    results = await pipeline.kickoff(inputs)
-
-    # Process and print results
-    for result in results:
-        print(f"Raw output: {result.raw}")
-        if result.json_dict:
-            print(f"JSON output: {result.json_dict}")
-        print("\n")
-
-def main():
-    asyncio.run(run())
-
-if __name__ == "__main__":
-    main()

src/crewai/cli/templates/pipeline_router/pipelines/pipeline_classifier.py

@@ -1,24 +0,0 @@
-from crewai import Pipeline
-from crewai.project import PipelineBase
-from ..crews.classifier_crew.classifier_crew import ClassifierCrew
-
-
-@PipelineBase
-class EmailClassifierPipeline:
-    def __init__(self):
-        # Initialize crews
-        self.classifier_crew = ClassifierCrew().crew()
-
-    def create_pipeline(self):
-        return Pipeline(
-            stages=[
-                self.classifier_crew
-            ]
-        )
-    
-    async def kickoff(self, inputs):
-        pipeline = self.create_pipeline()
-        results = await pipeline.kickoff(inputs)
-        return results
-
-

src/crewai/cli/templates/pipeline_router/pipelines/pipeline_normal.py

@@ -1,24 +0,0 @@
-from crewai import Pipeline
-from crewai.project import PipelineBase
-from ..crews.normal_crew.normal_crew import NormalCrew
-
-
-@PipelineBase
-class NormalPipeline:
-    def __init__(self):
-        # Initialize crews
-        self.normal_crew = NormalCrew().crew()
-
-    def create_pipeline(self):
-        return Pipeline(
-            stages=[
-                self.normal_crew
-            ]
-        )
-    
-    async def kickoff(self, inputs):
-        pipeline = self.create_pipeline()
-        results = await pipeline.kickoff(inputs)
-        return results
-
-

src/crewai/cli/templates/pipeline_router/pipelines/pipeline_urgent.py

@@ -1,23 +0,0 @@
-from crewai import Pipeline
-from crewai.project import PipelineBase
-from ..crews.urgent_crew.urgent_crew import UrgentCrew
-
-@PipelineBase
-class UrgentPipeline:
-    def __init__(self):
-        # Initialize crews
-        self.urgent_crew = UrgentCrew().crew()
-    
-    def create_pipeline(self):
-        return Pipeline(
-            stages=[
-                self.urgent_crew
-            ]
-        )
-    
-    async def kickoff(self, inputs):
-        pipeline = self.create_pipeline()
-        results = await pipeline.kickoff(inputs)
-        return results
-
-

src/crewai/cli/templates/pipeline_router/pyproject.toml

@@ -1,19 +0,0 @@
-[tool.poetry]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-
-[tool.poetry.dependencies]
-python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.46.0" }
-
-[tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-train = "{{folder_name}}.main:train"
-replay = "{{folder_name}}.main:replay"
-test = "{{folder_name}}.main:test"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

src/crewai/cli/templates/pipeline_router/tools/custom_tool.py

@@ -1,12 +0,0 @@
-from crewai_tools import BaseTool
-
-
-class MyCustomTool(BaseTool):
-    name: str = "Name of my tool"
-    description: str = (
-        "Clear description for what this tool is useful for, you agent will need this information to use it."
-    )
-
-    def _run(self, argument: str) -> str:
-        # Implementation goes here
-        return "this is an example of a tool output, ignore it and move along."

src/crewai/cli/templates/pyproject.toml

@@ -6,11 +6,10 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.46.0" }
+crewai = { extras = ["tools"], version = "^0.41.1" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"
-run_crew = "{{folder_name}}.main:run"
 train = "{{folder_name}}.main:train"
 replay = "{{folder_name}}.main:replay"
 test = "{{folder_name}}.main:test"

src/crewai/cli/test_crew.py

@@ -3,9 +3,9 @@ import subprocess
 import click
 
 
-def evaluate_crew(n_iterations: int, model: str) -> None:
+def test_crew(n_iterations: int, model: str) -> None:
     """
-    Test and Evaluate the crew by running a command in the Poetry environment.
+    Test the crew by running a command in the Poetry environment.
 
     Args:
         n_iterations (int): The number of iterations to test the crew.

src/crewai/cli/train_crew.py

@@ -3,22 +3,19 @@ import subprocess
 import click
 
 
-def train_crew(n_iterations: int, filename: str) -> None:
+def train_crew(n_iterations: int) -> None:
     """
     Train the crew by running a command in the Poetry environment.
 
     Args:
         n_iterations (int): The number of iterations to train the crew.
     """
-    command = ["poetry", "run", "train", str(n_iterations), filename]
+    command = ["poetry", "run", "train", str(n_iterations)]
 
     try:
         if n_iterations <= 0:
             raise ValueError("The number of iterations must be a positive integer.")
 
-        if not filename.endswith(".pkl"):
-            raise ValueError("The filename must not end with .pkl")
-
         result = subprocess.run(command, capture_output=False, text=True, check=True)
 
         if result.stderr:

src/crewai/cli/utils.py

@@ -1,18 +0,0 @@
-import click
-
-
-def copy_template(src, dst, name, class_name, folder_name):
-    """Copy a file from src to dst."""
-    with open(src, "r") as file:
-        content = file.read()
-
-    # Interpolate the content
-    content = content.replace("{{name}}", name)
-    content = content.replace("{{crew_name}}", class_name)
-    content = content.replace("{{folder_name}}", folder_name)
-
-    # Write the interpolated content to the new file
-    with open(dst, "w") as file:
-        file.write(content)
-
-    click.secho(f"  - Created {dst}", fg="green")

src/crewai/crew.py

@@ -3,7 +3,7 @@ import json
 import uuid
 from concurrent.futures import Future
 from hashlib import md5
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 from langchain_core.callbacks import BaseCallbackHandler
 from pydantic import (
@@ -32,9 +32,9 @@ 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.types.usage_metrics import UsageMetrics
 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
@@ -52,9 +52,6 @@ try:
 except ImportError:
     agentops = None
 
-if TYPE_CHECKING:
-    from crewai.pipeline.pipeline import Pipeline
-
 
 class Crew(BaseModel):
     """
@@ -100,13 +97,12 @@ class Crew(BaseModel):
         default_factory=TaskOutputStorageHandler
     )
 
-    name: Optional[str] = Field(default=None)
     cache: bool = Field(default=True)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     tasks: List[Task] = Field(default_factory=list)
     agents: List[BaseAgent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
-    verbose: bool = Field(default=False)
+    verbose: Union[int, bool] = Field(default=0)
     memory: bool = Field(
         default=False,
         description="Whether the crew should use memory to store memories of it's execution",
@@ -115,7 +111,7 @@ class Crew(BaseModel):
         default={"provider": "openai"},
         description="Configuration for the embedder to be used for the crew.",
     )
-    usage_metrics: Optional[UsageMetrics] = Field(
+    usage_metrics: Optional[dict] = Field(
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
@@ -151,18 +147,14 @@ class Crew(BaseModel):
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
-    output_log_file: Optional[str] = Field(
-        default=None,
+    output_log_file: Optional[Union[bool, str]] = Field(
+        default=False,
         description="output_log_file",
     )
     planning: Optional[bool] = Field(
         default=False,
         description="Plan the crew execution and add the plan to the crew.",
     )
-    planning_llm: Optional[Any] = Field(
-        default=None,
-        description="Language model that will run the AgentPlanner if planning is True.",
-    )
     task_execution_output_json_files: Optional[List[str]] = Field(
         default=None,
         description="List of file paths for task execution JSON files.",
@@ -200,7 +192,7 @@ class Crew(BaseModel):
     def set_private_attrs(self) -> "Crew":
         """Set private attributes."""
         self._cache_handler = CacheHandler()
-        self._logger = Logger(verbose=self.verbose)
+        self._logger = Logger(self.verbose)
         if self.output_log_file:
             self._file_handler = FileHandler(self.output_log_file)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
@@ -275,6 +267,20 @@ class Crew(BaseModel):
 
         return self
 
+    @model_validator(mode="after")
+    def check_tasks_in_hierarchical_process_not_async(self):
+        """Validates that the tasks in hierarchical process are not flagged with async_execution."""
+        if self.process == Process.hierarchical:
+            for task in self.tasks:
+                if task.async_execution:
+                    raise PydanticCustomError(
+                        "async_execution_in_hierarchical_process",
+                        "Hierarchical process error: Tasks cannot be flagged with async_execution.",
+                        {},
+                    )
+
+        return self
+
     @model_validator(mode="after")
     def validate_end_with_at_most_one_async_task(self):
         """Validates that the crew ends with at most one asynchronous task."""
@@ -390,7 +396,7 @@ class Crew(BaseModel):
         del task_config["agent"]
         return Task(**task_config, agent=task_agent)
 
-    def _setup_for_training(self, filename: str) -> None:
+    def _setup_for_training(self) -> None:
         """Sets up the crew for training."""
         self._train = True
 
@@ -401,13 +407,11 @@ class Crew(BaseModel):
             agent.allow_delegation = False
 
         CrewTrainingHandler(TRAINING_DATA_FILE).initialize_file()
-        CrewTrainingHandler(filename).initialize_file()
+        CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).initialize_file()
 
-    def train(
-        self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {}
-    ) -> None:
+    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(filename)
+        self._setup_for_training()
 
         for n_iteration in range(n_iterations):
             self._train_iteration = n_iteration
@@ -420,7 +424,7 @@ class Crew(BaseModel):
                 training_data=training_data, agent_id=str(agent.id)
             )
 
-            CrewTrainingHandler(filename).save_trained_data(
+            CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).save_trained_data(
                 agent_id=str(agent.role), trained_data=result.model_dump()
             )
 
@@ -459,7 +463,7 @@ class Crew(BaseModel):
         if self.planning:
             self._handle_crew_planning()
 
-        metrics: List[UsageMetrics] = []
+        metrics = []
 
         if self.process == Process.sequential:
             result = self._run_sequential_process()
@@ -469,12 +473,11 @@ class Crew(BaseModel):
             raise NotImplementedError(
                 f"The process '{self.process}' is not implemented yet."
             )
-
         metrics += [agent._token_process.get_summary() for agent in self.agents]
 
-        self.usage_metrics = UsageMetrics()
-        for metric in metrics:
-            self.usage_metrics.add_usage_metrics(metric)
+        self.usage_metrics = {
+            key: sum([m[key] for m in metrics if m is not None]) for key in metrics[0]
+        }
 
         return result
 
@@ -483,7 +486,12 @@ class Crew(BaseModel):
         results: List[CrewOutput] = []
 
         # Initialize the parent crew's usage metrics
-        total_usage_metrics = UsageMetrics()
+        total_usage_metrics = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+        }
 
         for input_data in inputs:
             crew = self.copy()
@@ -491,7 +499,8 @@ class Crew(BaseModel):
             output = crew.kickoff(inputs=input_data)
 
             if crew.usage_metrics:
-                total_usage_metrics.add_usage_metrics(crew.usage_metrics)
+                for key in total_usage_metrics:
+                    total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
 
             results.append(output)
 
@@ -520,10 +529,29 @@ class Crew(BaseModel):
 
         results = await asyncio.gather(*tasks)
 
-        total_usage_metrics = UsageMetrics()
+        total_usage_metrics = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+        }
         for crew in crew_copies:
             if crew.usage_metrics:
-                total_usage_metrics.add_usage_metrics(crew.usage_metrics)
+                for key in total_usage_metrics:
+                    total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
+
+        self.usage_metrics = total_usage_metrics
+
+        total_usage_metrics = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+        }
+        for crew in crew_copies:
+            if crew.usage_metrics:
+                for key in total_usage_metrics:
+                    total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
 
         self.usage_metrics = total_usage_metrics
         self._task_output_handler.reset()
@@ -532,12 +560,15 @@ class Crew(BaseModel):
     def _handle_crew_planning(self):
         """Handles the Crew planning."""
         self._logger.log("info", "Planning the crew execution")
-        result = CrewPlanner(
-            tasks=self.tasks, planning_agent_llm=self.planning_llm
-        )._handle_crew_planning()
+        result = CrewPlanner(self.tasks)._handle_crew_planning()
 
-        for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
-            task.description += step_plan
+        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:
+            self._logger.log(
+                "info", "Something went wrong with the planning process of the Crew"
+            )
 
     def _store_execution_log(
         self,
@@ -575,7 +606,7 @@ class Crew(BaseModel):
     def _run_hierarchical_process(self) -> CrewOutput:
         """Creates and assigns a manager agent to make sure the crew completes the tasks."""
         self._create_manager_agent()
-        return self._execute_tasks(self.tasks)
+        return self._execute_tasks(self.tasks, self.manager_agent)
 
     def _create_manager_agent(self):
         i18n = I18N(prompt_file=self.prompt_file)
@@ -599,6 +630,7 @@ class Crew(BaseModel):
     def _execute_tasks(
         self,
         tasks: List[Task],
+        manager: Optional[BaseAgent] = None,
         start_index: Optional[int] = 0,
         was_replayed: bool = False,
     ) -> CrewOutput:
@@ -626,13 +658,13 @@ class Crew(BaseModel):
                         last_sync_output = task.output
                 continue
 
-            agent_to_use = self._get_agent_to_use(task)
+            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."
                 )
 
-            self._prepare_agent_tools(task)
+            self._prepare_agent_tools(task, manager)
             self._log_task_start(task, agent_to_use.role)
 
             if isinstance(task, ConditionalTask):
@@ -698,18 +730,20 @@ class Crew(BaseModel):
             return skipped_task_output
         return None
 
-    def _prepare_agent_tools(self, task: Task):
+    def _prepare_agent_tools(self, task: Task, manager: Optional[BaseAgent]):
         if self.process == Process.hierarchical:
-            if self.manager_agent:
-                self._update_manager_tools(task)
+            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) -> Optional[BaseAgent]:
+    def _get_agent_to_use(
+        self, task: Task, manager: Optional[BaseAgent]
+    ) -> Optional[BaseAgent]:
         if self.process == Process.hierarchical:
-            return self.manager_agent
+            return manager
         return task.agent
 
     def _add_delegation_tools(self, task: Task):
@@ -745,14 +779,11 @@ class Crew(BaseModel):
         if self.output_log_file:
             self._file_handler.log(agent=role, task=task.description, status="started")
 
-    def _update_manager_tools(self, task: Task):
-        if self.manager_agent:
-            if task.agent:
-                self.manager_agent.tools = task.agent.get_delegation_tools([task.agent])
-            else:
-                self.manager_agent.tools = self.manager_agent.get_delegation_tools(
-                    self.agents
-                )
+    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 = (
@@ -851,7 +882,7 @@ class Crew(BaseModel):
             self.tasks[i].output = task_output
 
         self._logging_color = "bold_blue"
-        result = self._execute_tasks(self.tasks, start_index, True)
+        result = self._execute_tasks(self.tasks, self.manager_agent, start_index, True)
         return result
 
     def copy(self):
@@ -914,18 +945,25 @@ class Crew(BaseModel):
             )
         self._telemetry.end_crew(self, final_string_output)
 
-    def calculate_usage_metrics(self) -> UsageMetrics:
+    def calculate_usage_metrics(self) -> Dict[str, int]:
         """Calculates and returns the usage metrics."""
-        total_usage_metrics = UsageMetrics()
+        total_usage_metrics = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+        }
 
         for agent in self.agents:
             if hasattr(agent, "_token_process"):
                 token_sum = agent._token_process.get_summary()
-                total_usage_metrics.add_usage_metrics(token_sum)
+                for key in total_usage_metrics:
+                    total_usage_metrics[key] += token_sum.get(key, 0)
 
         if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
             token_sum = self.manager_agent._token_process.get_summary()
-            total_usage_metrics.add_usage_metrics(token_sum)
+            for key in total_usage_metrics:
+                total_usage_metrics[key] += token_sum.get(key, 0)
 
         return total_usage_metrics
 
@@ -944,17 +982,5 @@ class Crew(BaseModel):
 
         evaluator.print_crew_evaluation_result()
 
-    def __rshift__(self, other: "Crew") -> "Pipeline":
-        """
-        Implements the >> operator to add another Crew to an existing Pipeline.
-        """
-        from crewai.pipeline.pipeline import Pipeline
-
-        if not isinstance(other, Crew):
-            raise TypeError(
-                f"Unsupported operand type for >>: '{type(self).__name__}' and '{type(other).__name__}'"
-            )
-        return Pipeline(stages=[self, other])
-
     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/crew_output.py

@@ -5,7 +5,6 @@ from pydantic import BaseModel, Field
 
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
-from crewai.types.usage_metrics import UsageMetrics
 
 
 class CrewOutput(BaseModel):
@@ -21,7 +20,9 @@ class CrewOutput(BaseModel):
     tasks_output: list[TaskOutput] = Field(
         description="Output of each task", default=[]
     )
-    token_usage: UsageMetrics = Field(description="Processed token summary", default={})
+    token_usage: Dict[str, Any] = Field(
+        description="Processed token summary", default={}
+    )
 
     @property
     def json(self) -> Optional[str]:

src/crewai/memory/short_term/short_term_memory.py

@@ -1,4 +1,3 @@
-from typing import Any, Dict, Optional
 from crewai.memory.memory import Memory
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -19,15 +18,8 @@ class ShortTermMemory(Memory):
         )
         super().__init__(storage)
 
-    def save(
-        self,
-        value: Any,
-        metadata: Optional[Dict[str, Any]] = None,
-        agent: Optional[str] = None,
-    ) -> None:
-        item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
-
-        super().save(value=item.data, metadata=item.metadata, agent=item.agent)
+    def save(self, item: ShortTermMemoryItem) -> None:
+        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

src/crewai/memory/short_term/short_term_memory_item.py

@@ -3,10 +3,7 @@ from typing import Any, Dict, Optional
 
 class ShortTermMemoryItem:
     def __init__(
-        self,
-        data: Any,
-        agent: Optional[str] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        self, data: Any, agent: str, metadata: Optional[Dict[str, Any]] = None
     ):
         self.data = data
         self.agent = agent

src/crewai/memory/storage/interface.py

@@ -4,7 +4,7 @@ from typing import Any, Dict
 class Storage:
     """Abstract base class defining the storage interface"""
 
-    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+    def save(self, key: str, value: Any, metadata: Dict[str, Any]) -> None:
         pass
 
     def search(self, key: str) -> Dict[str, Any]:  # type: ignore

src/crewai/memory/storage/rag_storage.py

@@ -5,13 +5,13 @@ import os
 import shutil
 from typing import Any, Dict, List, Optional
 
-from crewai.memory.storage.interface import Storage
-from crewai.utilities.paths import db_storage_path
 from embedchain import App
 from embedchain.llm.base import BaseLlm
-from embedchain.models.data_type import DataType
 from embedchain.vectordb.chroma import InvalidDimensionException
 
+from crewai.memory.storage.interface import Storage
+from crewai.utilities.paths import db_storage_path
+
 
 @contextlib.contextmanager
 def suppress_logging(
@@ -101,7 +101,8 @@ class RAGStorage(Storage):
         return [r for r in results if r["metadata"]["score"] >= score_threshold]
 
     def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> Any:
-        self.app.add(text, data_type=DataType.TEXT, metadata=metadata)
+        with suppress_logging():
+            self.app.add(text, data_type="text", metadata=metadata)
 
     def reset(self) -> None:
         try:

src/crewai/pipeline/__init__.py

@@ -1,3 +0,0 @@
-from crewai.pipeline.pipeline import Pipeline
-from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult
-from crewai.pipeline.pipeline_output import PipelineOutput

src/crewai/pipeline/pipeline.py

@@ -1,405 +0,0 @@
-import asyncio
-import copy
-from typing import Any, Dict, List, Tuple, Union
-
-from pydantic import BaseModel, Field, model_validator
-
-from crewai.crew import Crew
-from crewai.crews.crew_output import CrewOutput
-from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult
-from crewai.routers.router import Router
-from crewai.types.usage_metrics import UsageMetrics
-
-Trace = Union[Union[str, Dict[str, Any]], List[Union[str, Dict[str, Any]]]]
-PipelineStage = Union[Crew, List[Crew], Router]
-
-"""
-Developer Notes:
-
-This module defines a Pipeline class that represents a sequence of operations (stages)
-to process inputs. Each stage can be either sequential or parallel, and the pipeline
-can process multiple kickoffs concurrently.
-
-Core Loop Explanation:
-1. The `process_kickoffs` method processes multiple kickoffs in parallel, each going through
-   all pipeline stages.
-2. The `process_single_kickoff` method handles the processing of a single kickouff through
-   all stages, updating metrics and input data along the way.
-3. The `_process_stage` method determines whether a stage is sequential or parallel
-   and processes it accordingly.
-4. The `_process_single_crew` and `_process_parallel_crews` methods handle the
-   execution of single and parallel crew stages.
-5. The `_update_metrics_and_input` method updates usage metrics and the current input
-   with the outputs from a stage.
-6. The `_build_pipeline_kickoff_results` method constructs the final results of the
-   pipeline kickoff, including traces and outputs.
-
-Handling Traces and Crew Outputs:
-- During the processing of stages, we handle the results (traces and crew outputs)
-  for all stages except the last one differently from the final stage.
-- For intermediate stages, the primary focus is on passing the input data between stages.
-  This involves merging the output dictionaries from all crews in a stage into a single
-  dictionary and passing it to the next stage. This merged dictionary allows for smooth
-  data flow between stages.
-- For the final stage, in addition to passing the input data, we also need to prepare
-  the final outputs and traces to be returned as the overall result of the pipeline kickoff.
-  In this case, we do not merge the results, as each result needs to be included
-  separately in its own pipeline kickoff result.
-
-Pipeline Terminology:
-- Pipeline: The overall structure that defines a sequence of operations.
-- Stage: A distinct part of the pipeline, which can be either sequential or parallel.
-- Kickoff: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
-- Branch: Parallel executions within a stage (e.g., concurrent crew operations).
-- Trace: The journey of an individual input through the entire pipeline.
-
-Example pipeline structure:
-crew1 >> crew2 >> crew3 
-
-This represents a pipeline with three sequential stages:
-1. crew1 is the first stage, which processes the input and passes its output to crew2.
-2. crew2 is the second stage, which takes the output from crew1 as its input, processes it, and passes its output to crew3.
-3. crew3 is the final stage, which takes the output from crew2 as its input and produces the final output of the pipeline.
-
-Each input creates its own kickoff, flowing through all stages of the pipeline.
-Multiple kickoffss can be processed concurrently, each following the defined pipeline structure.
-
-Another example pipeline structure:
-crew1 >> [crew2, crew3] >> crew4
-
-This represents a pipeline with three stages:
-1. A sequential stage (crew1)
-2. A parallel stage with two branches (crew2 and crew3 executing concurrently)
-3. Another sequential stage (crew4)
-
-Each input creates its own kickoff, flowing through all stages of the pipeline.
-Multiple kickoffs can be processed concurrently, each following the defined pipeline structure.
-"""
-
-
-class Pipeline(BaseModel):
-    stages: List[PipelineStage] = Field(
-        ..., description="List of crews representing stages to be executed in sequence"
-    )
-
-    @model_validator(mode="before")
-    @classmethod
-    def validate_stages(cls, values):
-        stages = values.get("stages", [])
-
-        def check_nesting_and_type(item, depth=0):
-            if depth > 1:
-                raise ValueError("Double nesting is not allowed in pipeline stages")
-            if isinstance(item, list):
-                for sub_item in item:
-                    check_nesting_and_type(sub_item, depth + 1)
-            elif not isinstance(item, (Crew, Router)):
-                raise ValueError(
-                    f"Expected Crew instance, Router instance, or list of Crews, got {type(item)}"
-                )
-
-        for stage in stages:
-            check_nesting_and_type(stage)
-        return values
-
-    async def kickoff(
-        self, inputs: List[Dict[str, Any]]
-    ) -> List[PipelineKickoffResult]:
-        """
-        Processes multiple runs in parallel, each going through all pipeline stages.
-
-        Args:
-            inputs (List[Dict[str, Any]]): List of inputs for each run.
-
-        Returns:
-            List[PipelineKickoffResult]: List of results from each run.
-        """
-        pipeline_results: List[PipelineKickoffResult] = []
-
-        # Process all runs in parallel
-        all_run_results = await asyncio.gather(
-            *(self.process_single_kickoff(input_data) for input_data in inputs)
-        )
-
-        # Flatten the list of lists into a single list of results
-        pipeline_results.extend(
-            result for run_result in all_run_results for result in run_result
-        )
-
-        return pipeline_results
-
-    async def process_single_kickoff(
-        self, kickoff_input: Dict[str, Any]
-    ) -> List[PipelineKickoffResult]:
-        """
-        Processes a single run through all pipeline stages.
-
-        Args:
-            input (Dict[str, Any]): The input for the run.
-
-        Returns:
-            List[PipelineKickoffResult]: The results of processing the run.
-        """
-        initial_input = copy.deepcopy(kickoff_input)
-        current_input = copy.deepcopy(kickoff_input)
-        stages = self._copy_stages()
-        pipeline_usage_metrics: Dict[str, UsageMetrics] = {}
-        all_stage_outputs: List[List[CrewOutput]] = []
-        traces: List[List[Union[str, Dict[str, Any]]]] = [[initial_input]]
-
-        stage_index = 0
-        while stage_index < len(stages):
-            stage = stages[stage_index]
-            stage_input = copy.deepcopy(current_input)
-
-            if isinstance(stage, Router):
-                next_pipeline, route_taken = stage.route(stage_input)
-                stages = (
-                    stages[: stage_index + 1]
-                    + list(next_pipeline.stages)
-                    + stages[stage_index + 1 :]
-                )
-                traces.append([{"route_taken": route_taken}])
-                stage_index += 1
-                continue
-
-            stage_outputs, stage_trace = await self._process_stage(stage, stage_input)
-
-            self._update_metrics_and_input(
-                pipeline_usage_metrics, current_input, stage, stage_outputs
-            )
-            traces.append(stage_trace)
-            all_stage_outputs.append(stage_outputs)
-            stage_index += 1
-
-        return self._build_pipeline_kickoff_results(
-            all_stage_outputs, traces, pipeline_usage_metrics
-        )
-
-    async def _process_stage(
-        self, stage: PipelineStage, current_input: Dict[str, Any]
-    ) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]:
-        """
-        Processes a single stage of the pipeline, which can be either sequential or parallel.
-
-        Args:
-            stage (Union[Crew, List[Crew]]): The stage to process.
-            current_input (Dict[str, Any]): The input for the stage.
-
-        Returns:
-            Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The outputs and trace of the stage.
-        """
-        if isinstance(stage, Crew):
-            return await self._process_single_crew(stage, current_input)
-        elif isinstance(stage, list) and all(isinstance(crew, Crew) for crew in stage):
-            return await self._process_parallel_crews(stage, current_input)
-        else:
-            raise ValueError(f"Unsupported stage type: {type(stage)}")
-
-    async def _process_single_crew(
-        self, crew: Crew, current_input: Dict[str, Any]
-    ) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]:
-        """
-        Processes a single crew.
-
-        Args:
-            crew (Crew): The crew to process.
-            current_input (Dict[str, Any]): The input for the crew.
-
-        Returns:
-            Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The output and trace of the crew.
-        """
-        output = await crew.kickoff_async(inputs=current_input)
-        return [output], [crew.name or str(crew.id)]
-
-    async def _process_parallel_crews(
-        self, crews: List[Crew], current_input: Dict[str, Any]
-    ) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]:
-        """
-        Processes multiple crews in parallel.
-
-        Args:
-            crews (List[Crew]): The list of crews to process in parallel.
-            current_input (Dict[str, Any]): The input for the crews.
-
-        Returns:
-            Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The outputs and traces of the crews.
-        """
-        parallel_outputs = await asyncio.gather(
-            *[crew.kickoff_async(inputs=current_input) for crew in crews]
-        )
-        return parallel_outputs, [crew.name or str(crew.id) for crew in crews]
-
-    def _update_metrics_and_input(
-        self,
-        usage_metrics: Dict[str, UsageMetrics],
-        current_input: Dict[str, Any],
-        stage: PipelineStage,
-        outputs: List[CrewOutput],
-    ) -> None:
-        """
-        Updates metrics and current input with the outputs of a stage.
-
-        Args:
-            usage_metrics (Dict[str, Any]): The usage metrics to update.
-            current_input (Dict[str, Any]): The current input to update.
-            stage (Union[Crew, List[Crew]]): The stage that was processed.
-            outputs (List[CrewOutput]): The outputs of the stage.
-        """
-        if isinstance(stage, Crew):
-            usage_metrics[stage.name or str(stage.id)] = outputs[0].token_usage
-            current_input.update(outputs[0].to_dict())
-        elif isinstance(stage, list) and all(isinstance(crew, Crew) for crew in stage):
-            for crew, output in zip(stage, outputs):
-                usage_metrics[crew.name or str(crew.id)] = output.token_usage
-                current_input.update(output.to_dict())
-        else:
-            raise ValueError(f"Unsupported stage type: {type(stage)}")
-
-    def _build_pipeline_kickoff_results(
-        self,
-        all_stage_outputs: List[List[CrewOutput]],
-        traces: List[List[Union[str, Dict[str, Any]]]],
-        token_usage: Dict[str, UsageMetrics],
-    ) -> List[PipelineKickoffResult]:
-        """
-        Builds the results of a pipeline run.
-
-        Args:
-            all_stage_outputs (List[List[CrewOutput]]): All stage outputs.
-            traces (List[List[Union[str, Dict[str, Any]]]]): All traces.
-            token_usage (Dict[str, Any]): Token usage metrics.
-
-        Returns:
-            List[PipelineKickoffResult]: The results of the pipeline run.
-        """
-        formatted_traces = self._format_traces(traces)
-        formatted_crew_outputs = self._format_crew_outputs(all_stage_outputs)
-
-        return [
-            PipelineKickoffResult(
-                token_usage=token_usage,
-                trace=formatted_trace,
-                raw=crews_outputs[-1].raw,
-                pydantic=crews_outputs[-1].pydantic,
-                json_dict=crews_outputs[-1].json_dict,
-                crews_outputs=crews_outputs,
-            )
-            for crews_outputs, formatted_trace in zip(
-                formatted_crew_outputs, formatted_traces
-            )
-        ]
-
-    def _format_traces(
-        self, traces: List[List[Union[str, Dict[str, Any]]]]
-    ) -> List[List[Trace]]:
-        """
-        Formats the traces of a pipeline run.
-
-        Args:
-            traces (List[List[Union[str, Dict[str, Any]]]]): The traces to format.
-
-        Returns:
-            List[List[Trace]]: The formatted traces.
-        """
-        formatted_traces: List[Trace] = self._format_single_trace(traces[:-1])
-        return self._format_multiple_traces(formatted_traces, traces[-1])
-
-    def _format_single_trace(
-        self, traces: List[List[Union[str, Dict[str, Any]]]]
-    ) -> List[Trace]:
-        """
-        Formats single traces.
-
-        Args:
-            traces (List[List[Union[str, Dict[str, Any]]]]): The traces to format.
-
-        Returns:
-            List[Trace]: The formatted single traces.
-        """
-        formatted_traces: List[Trace] = []
-        for trace in traces:
-            formatted_traces.append(trace[0] if len(trace) == 1 else trace)
-        return formatted_traces
-
-    def _format_multiple_traces(
-        self,
-        formatted_traces: List[Trace],
-        final_trace: List[Union[str, Dict[str, Any]]],
-    ) -> List[List[Trace]]:
-        """
-        Formats multiple traces.
-
-        Args:
-            formatted_traces (List[Trace]): The formatted single traces.
-            final_trace (List[Union[str, Dict[str, Any]]]): The final trace to format.
-
-        Returns:
-            List[List[Trace]]: The formatted multiple traces.
-        """
-        traces_to_return: List[List[Trace]] = []
-        if len(final_trace) == 1:
-            formatted_traces.append(final_trace[0])
-            traces_to_return.append(formatted_traces)
-        else:
-            for trace in final_trace:
-                copied_traces = formatted_traces.copy()
-                copied_traces.append(trace)
-                traces_to_return.append(copied_traces)
-        return traces_to_return
-
-    def _format_crew_outputs(
-        self, all_stage_outputs: List[List[CrewOutput]]
-    ) -> List[List[CrewOutput]]:
-        """
-        Formats the outputs of all stages into a list of crew outputs.
-
-        Args:
-            all_stage_outputs (List[List[CrewOutput]]): All stage outputs.
-
-        Returns:
-            List[List[CrewOutput]]: Formatted crew outputs.
-        """
-        crew_outputs: List[CrewOutput] = [
-            output
-            for stage_outputs in all_stage_outputs[:-1]
-            for output in stage_outputs
-        ]
-        return [crew_outputs + [output] for output in all_stage_outputs[-1]]
-
-    def _copy_stages(self):
-        """Create a deep copy of the Pipeline's stages."""
-        new_stages = []
-        for stage in self.stages:
-            if isinstance(stage, list):
-                new_stages.append(
-                    [
-                        crew.copy() if hasattr(crew, "copy") else copy.deepcopy(crew)
-                        for crew in stage
-                    ]
-                )
-            elif hasattr(stage, "copy"):
-                new_stages.append(stage.copy())
-            else:
-                new_stages.append(copy.deepcopy(stage))
-
-        return new_stages
-
-    def __rshift__(self, other: PipelineStage) -> "Pipeline":
-        """
-        Implements the >> operator to add another Stage (Crew or List[Crew]) to an existing Pipeline.
-
-        Args:
-            other (Any): The stage to add.
-
-        Returns:
-            Pipeline: A new pipeline with the added stage.
-        """
-        if isinstance(other, (Crew, Router)) or (
-            isinstance(other, list) and all(isinstance(item, Crew) for item in other)
-        ):
-            return type(self)(stages=self.stages + [other])
-        else:
-            raise TypeError(
-                f"Unsupported operand type for >>: '{type(self).__name__}' and '{type(other).__name__}'"
-            )

src/crewai/pipeline/pipeline_kickoff_result.py

@@ -1,61 +0,0 @@
-import json
-import uuid
-from typing import Any, Dict, List, Optional, Union
-
-from pydantic import UUID4, BaseModel, Field
-
-from crewai.crews.crew_output import CrewOutput
-from crewai.types.usage_metrics import UsageMetrics
-
-
-class PipelineKickoffResult(BaseModel):
-    """Class that represents the result of a pipeline run."""
-
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-    raw: str = Field(description="Raw output of the pipeline run", default="")
-    pydantic: Any = Field(
-        description="Pydantic output of the pipeline run", default=None
-    )
-    json_dict: Union[Dict[str, Any], None] = Field(
-        description="JSON dict output of the pipeline run", default={}
-    )
-
-    token_usage: Dict[str, UsageMetrics] = Field(
-        description="Token usage for each crew in the run"
-    )
-    trace: List[Any] = Field(
-        description="Trace of the journey of inputs through the run"
-    )
-    crews_outputs: List[CrewOutput] = Field(
-        description="Output from each crew in the run",
-        default=[],
-    )
-
-    @property
-    def json(self) -> Optional[str]:
-        if self.crews_outputs[-1].tasks_output[-1].output_format != "json":
-            raise ValueError(
-                "No JSON output found in the final task of the final crew. 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