Addded feature request template in YAML format

Added config .yml to remove blank template

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,116 @@
+name: Bug report
+description: Create a report to help us improve CrewAI
+title: "[BUG]"
+labels: ["bug"]
+assignees: []
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: Provide a clear and concise description of what the bug is.
+    validations:
+      required: true
+  - type: textarea
+    id: steps-to-reproduce
+    attributes:
+      label: Steps to Reproduce
+      description: Provide a step-by-step process to reproduce the behavior.
+      placeholder: |
+        1. Go to '...'
+        2. Click on '....'
+        3. Scroll down to '....'
+        4. See error
+    validations:
+      required: true
+  - type: textarea
+    id: expected-behavior
+    attributes:
+      label: Expected behavior
+      description: A clear and concise description of what you expected to happen.
+    validations:
+      required: true
+  - type: textarea
+    id: screenshots-code
+    attributes:
+      label: Screenshots/Code snippets
+      description: If applicable, add screenshots or code snippets to help explain your problem.
+    validations:
+      required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating System
+      description: Select the operating system you're using
+      options:
+        - Ubuntu 20.04
+        - Ubuntu 22.04
+        - Ubuntu 24.04
+        - macOS Catalina
+        - macOS Big Sur
+        - macOS Monterey
+        - macOS Ventura
+        - macOS Sonoma
+        - Windows 10
+        - Windows 11
+        - Other (specify in additional context)
+    validations:
+      required: true
+  - type: dropdown
+    id: python-version
+    attributes:
+      label: Python Version
+      description: Version of Python your Crew is running on
+      options:
+        - '3.10'
+        - '3.11'
+        - '3.12'
+        - '3.13'
+    validations:
+      required: true
+  - type: input
+    id: crewai-version
+    attributes:
+      label: crewAI Version
+      description: What version of CrewAI are you using
+    validations:
+      required: true
+  - type: input
+    id: crewai-tools-version
+    attributes:
+      label: crewAI Tools Version
+      description: What version of CrewAI Tools are you using 
+    validations:
+      required: true
+  - type: dropdown
+    id: virtual-environment
+    attributes:
+      label: Virtual Environment
+      description: What Virtual Environment are you running your crew in.
+      options:
+        - Venv
+        - Conda
+        - Poetry
+    validations:
+      required: true
+  - type: textarea
+    id: evidence
+    attributes:
+      label: Evidence
+      description: Include relevant information, logs or error messages. These can be screenshots.
+    validations:
+      required: true
+  - type: textarea
+    id: possible-solution
+    attributes:
+      label: Possible Solution
+      description: Have a solution in mind? Please suggest it here, or write "None".
+    validations:
+      required: true
+  - type: textarea
+    id: additional-context
+    attributes:
+      label: Additional context
+      description: Add any other context about the problem here.
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,65 @@
+name: Feature request
+description: Suggest a new feature for CrewAI
+title: "[FEATURE]"
+labels: ["feature-request"]
+assignees: []
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this feature request!
+  - type: dropdown
+    id: feature-area
+    attributes:
+      label: Feature Area
+      description: Which area of CrewAI does this feature primarily relate to?
+      options:
+        - Core functionality
+        - Agent capabilities
+        - Task management
+        - Integration with external tools
+        - Performance optimization
+        - Documentation
+        - Other (please specify in additional context)
+    validations:
+      required: true
+  - type: textarea
+    id: problem
+    attributes:
+      label: Is your feature request related to a an existing bug? Please link it here.
+      description: A link to the bug or NA if not related to an existing bug.
+    validations:
+      required: true
+  - type: textarea
+    id: solution
+    attributes:
+      label: Describe the solution you'd like
+      description: A clear and concise description of what you want to happen.
+    validations:
+      required: true
+  - type: textarea
+    id: alternatives
+    attributes:
+      label: Describe alternatives you've considered
+      description: A clear and concise description of any alternative solutions or features you've considered.
+    validations:
+      required: false
+  - type: textarea
+    id: context
+    attributes:
+      label: Additional context
+      description: Add any other context, screenshots, or examples about the feature request here.
+    validations:
+      required: false
+  - type: dropdown
+    id: willingness-to-contribute
+    attributes:
+      label: Willingness to Contribute
+      description: Would you be willing to contribute to the implementation of this feature?
+      options:
+        - Yes, I'd be happy to submit a pull request
+        - I could provide more detailed specifications
+        - I can test the feature once it's implemented
+        - No, I'm just suggesting the idea
+    validations:
+      required: true

docs/core-concepts/Agents.md

@@ -26,7 +26,7 @@ description: What are crewAI Agents and how to use them.
 | **Function Calling LLM** *(optional)* | `function_calling_llm`  | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
 | **Max Iter** *(optional)*  | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
 | **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
-| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the Maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
+| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
 | **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
 | **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
 | **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
@@ -34,6 +34,8 @@ description: What are crewAI Agents and how to use them.
 | **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
+| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.                                                                                                                                                                  |
 
 ## Creating an Agent
 
@@ -72,7 +74,8 @@ agent = Agent(
   tools_handler=my_tools_handler,  # Optional
   cache_handler=my_cache_handler,  # Optional
   callbacks=[callback1, callback2],  # Optional
-  agent_executor=my_agent_executor  # Optional
+  allow_code_execution=True,  # Optiona
+  max_retry_limit=2,  # Optional
 )
 ```
 
@@ -144,6 +147,5 @@ my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
 crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
 ```
 
-
 ## Conclusion
-Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.
+Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

docs/core-concepts/Collaboration.md

@@ -28,6 +28,8 @@ The `Crew` class has been enriched with several attributes to support advanced f
 - **Embedder Configuration (`embedder`)**: Specifies the configuration for the embedder to be used by the crew for understanding and generating language. This attribute supports customization of the language model provider.
 - **Cache Management (`cache`)**: Determines whether the crew should use a cache to store the results of tool executions, optimizing performance.
 - **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew execution.
+- **Planning Mode (`planning`)**: Allows crews to plan their actions before executing tasks by setting `planning=True` when creating the `Crew` instance. This feature enhances coordination and efficiency.
+- **Replay Feature**: Introduces a new CLI for listing tasks from the last run and replaying from a specific task, enhancing task management and troubleshooting.
 
 ## Delegation: Dividing to Conquer
 Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.

docs/core-concepts/Crews.md

@@ -32,8 +32,8 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
-| **Planning** *(optional)*             | `planning`             |  Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.
-| **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process. |
+| **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.
@@ -183,14 +183,14 @@ result = my_crew.kickoff()
 print(result)
 ```
 
-### Different ways to Kicking Off a Crew
+### Different Ways to Kick Off a Crew
 
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 
-`kickoff()`: Starts the execution process according to the defined process flow.
-`kickoff_for_each()`: Executes tasks for each agent individually.
-`kickoff_async()`: Initiates the workflow asynchronously.
-`kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
+- `kickoff()`: Starts the execution process according to the defined process flow.
+- `kickoff_for_each()`: Executes tasks for each agent individually.
+- `kickoff_async()`: Initiates the workflow asynchronously.
+- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
 
 ```python
 # Start the crew's task execution
@@ -215,33 +215,34 @@ for async_result in async_results:
     print(async_result)
 ```
 
-These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs
+These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
 
+### Replaying from a Specific Task
 
-### Replaying from specific task:
-You can now replay from a specific task using our cli command replay.
+You can now replay from a specific task using our CLI command `replay`.
 
 The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
 
 Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
 
+### Replaying from a Specific Task Using the CLI
 
-### Replaying from specific task Using the CLI
 To use the replay feature, follow these steps:
 
 1. Open your terminal or command prompt.
 2. Navigate to the directory where your CrewAI project is located.
 3. Run the following command:
 
-To view latest kickoff task_ids use:
+To view the latest kickoff task IDs, use:
 
 ```shell
 crewai log-tasks-outputs
 ```
 
+Then, to replay from a specific task, use:
 
 ```shell
 crewai replay -t <task_id>
 ```
 
-These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/core-concepts/Memory.md

@@ -29,9 +29,9 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
 When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
 By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
 
-The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.  
+The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.
 The **Long-Term Memory** uses SQLLite3 to store task results.  Currently, there is no way to override these storage implementations.
-The data storage files are saved into a platform specific location found using the appdirs package 
+The data storage files are saved into a platform specific location found using the appdirs package
 and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
 
 ### Example: Configuring Memory for a Crew
@@ -105,7 +105,7 @@ my_crew = Crew(
 			"provider": "azure_openai",
 			"config":{
 				"model": 'text-embedding-ada-002',
-				"deployment_name": "you_embedding_model_deployment_name"
+				"deployment_name": "your_embedding_model_deployment_name"
 			}
 		}
 )
@@ -159,8 +159,8 @@ my_crew = Crew(
 		embedder={
 			"provider": "cohere",
 			"config":{
-				"model": "embed-english-v3.0"
-    		"vector_dimension": 1024
+				"model": "embed-english-v3.0",
+        "vector_dimension": 1024
 			}
 		}
 )
@@ -197,12 +197,10 @@ crewai reset_memories [OPTIONS]
   - **Type:** Flag (boolean)
   - **Default:** False
 
-
-
 ## Benefits of Using crewAI's Memory System
 - **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
 - **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
 - **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
 
 ## Getting Started
-Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.
+Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/core-concepts/Pipeline.md

@@ -34,7 +34,7 @@ Each input creates its own run, flowing through all stages of the pipeline. Mult
 
 | Attribute  | Parameters | Description                                                                           |
 | :--------- | :--------- | :------------------------------------------------------------------------------------ |
-| **Stages** | `stages`   | A list of crews or lists of crews representing the stages to be executed in sequence. |
+| **Stages** | `stages`   | A list of crews, lists of crews, or routers representing the stages to be executed in sequence. |
 
 ## Creating a Pipeline
 
@@ -79,7 +79,7 @@ my_pipeline = Pipeline(
 ## 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.
+The output of a pipeline in the crewAI framework is encapsulated within the `PipelineKickoffResult` class. This class provides 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
 

docs/core-concepts/Planning.md

@@ -41,13 +41,11 @@ my_crew = Crew(
 )
 ```
 
-
 ### Example
 
 When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
 
-```bash
-
+```
 [2024-07-15 16:49:11][INFO]: Planning the crew execution
 **Step-by-Step Plan for Task Execution**
 
@@ -133,6 +131,4 @@ A list with 10 bullet points of the most relevant information about AI LLMs.
 
 **Expected Output:**
 A fully-fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
-
----
-```
+```

docs/core-concepts/Processes.md

@@ -55,10 +55,5 @@ Emulates a corporate hierarchy, CrewAI allows specifying a custom manager agent
 ## Process Class: Detailed Overview
 The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`). The consensual process is planned for future inclusion, emphasizing our commitment to continuous development and innovation.
 
-## Additional Task Features
-- **Asynchronous Execution**: Tasks can now be executed asynchronously, allowing for parallel processing and efficiency improvements. This feature is designed to enable tasks to be carried out concurrently, enhancing the overall productivity of the crew.
-- **Human Input Review**: An optional feature that enables the review of task outputs by humans to ensure quality and accuracy before finalization. This additional step introduces a layer of oversight, providing an opportunity for human intervention and validation.
-- **Output Customization**: Tasks support various output formats, including JSON (`output_json`), Pydantic models (`output_pydantic`), and file outputs (`output_file`), providing flexibility in how task results are captured and utilized. This allows for a wide range of output possibilities, catering to different needs and requirements.
-
 ## Conclusion
 The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.

docs/core-concepts/Tasks.md

@@ -17,16 +17,17 @@ Tasks within crewAI can be collaborative, requiring multiple agents to work toge
 | **Description**                  | `description`     | A clear, concise statement of what the task entails.                                                                 |
 | **Agent**                        | `agent`           | The agent responsible for the task, assigned either directly or by the crew's process.                               |
 | **Expected Output**              | `expected_output` | A detailed description of what the task's completion looks like.                                                     |
-| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task.                                             |
-| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion.                       |
+| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list.                  |
+| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False.    |
 | **Context** _(optional)_         | `context`         | Specifies tasks whose outputs are used as context for this task.                                                     |
-| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization.                   |
+| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
 | **Output JSON** _(optional)_     | `output_json`     | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
 | **Output Pydantic** _(optional)_ | `output_pydantic` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
 | **Output File** _(optional)_     | `output_file`     | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Output** _(optional)_          | `output`          | The output of the task, containing the raw, JSON, and Pydantic output plus additional details.                       |
-| **Callback** _(optional)_        | `callback`        | A Python callable that is executed with the task's output upon completion.                                           |
-| **Human Input** _(optional)_     | `human_input`     | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight.                  |
+| **Output** _(optional)_          | `output`          | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details.                  |
+| **Callback** _(optional)_        | `callback`        | A callable that is executed with the task's output upon completion.                                                  |
+| **Human Input** _(optional)_     | `human_input`     | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. Defaults to False.|
+| **Converter Class** _(optional)_ | `converter_cls`   | A converter class used to export structured output. Defaults to None.                                                |
 
 ## Creating a Task
 
@@ -56,7 +57,7 @@ By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput`
 | Attribute         | Parameters      | Type                       | Description                                                                                        |
 | :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
 | **Description**   | `description`   | `str`                      | A brief description of the task.                                                                   |
-| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the description.                                  |
+| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the first 10 words of the description.            |
 | **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
 | **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
 | **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
@@ -311,4 +312,4 @@ save_output_task = Task(
 
 ## Conclusion
 
-Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
+Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/core-concepts/Testing.md

@@ -5,12 +5,11 @@ description: Learn how to test your crewAI Crew and evaluate their performance.
 
 ## Introduction
 
-Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. And with crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
+Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. With crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
 
 ### Using the Testing Feature
 
-We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics.
-The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now the only provider available is OpenAI.
+We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
 
 ```bash
 crewai test
@@ -22,9 +21,10 @@ If you want to run more iterations or use a different model, you can specify the
 crewai test --n_iterations 5 --model gpt-4o
 ```
 
-What happens when you run the `crewai test` command is that the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
+When you run the `crewai test` command, the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
 
 A table of scores at the end will show the performance of the crew in terms of the following metrics:
+
 ```
                 Task Scores
           (1-10 Higher is better)
@@ -38,4 +38,3 @@ A table of scores at the end will show the performance of the crew in terms of t
 ```
 
 The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.
-

docs/core-concepts/Tools.md

@@ -80,11 +80,12 @@ write = Task(
     output_file='blog-posts/new_post.md'  # The final blog post will be saved here
 )
 
-# Assemble a crew
+# Assemble a crew with planning enabled
 crew = Crew(
     agents=[researcher, writer],
     tasks=[research, write],
-    verbose=True
+    verbose=True,
+    planning=True,  # Enable planning feature
 )
 
 # Execute tasks
@@ -105,6 +106,7 @@ Here is a list of the available tools and their descriptions:
 | **CodeInterpreterTool**     | A tool for interpreting python code.                                                          |
 | **ComposioTool**            | Enables use of Composio tools.                                                                |
 | **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
+| **DALL-E Tool**            | A tool for generating images using the DALL-E API.                                        |
 | **DirectorySearchTool**     | A RAG tool for searching within directories, useful for navigating through file systems.      |
 | **DOCXSearchTool**          | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.         |
 | **DirectoryReadTool**       | Facilitates reading and processing of directory structures and their contents.                |
@@ -121,6 +123,7 @@ Here is a list of the available tools and their descriptions:
 | **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
 | **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
 | **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
+| **Vision Tool**            | A tool for generating images using the DALL-E API.                                        |
 | **RagTool**                 | A general-purpose RAG tool capable of handling various data sources and types.                 |
 | **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.     |
 | **ScrapeWebsiteTool**       | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
@@ -195,6 +198,5 @@ writer1 = Agent(
     #...
 ```
 
-
 ## Conclusion
 Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/core-concepts/Training-Crew.md

@@ -16,9 +16,11 @@ To use the training feature, follow these steps:
 3. Run the following command:
 
 ```shell
-crewai train -n <n_iterations>
+crewai train -n <n_iterations> <filename>
 ```
 
+!!! note "Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`."
+
 ### Training Your Crew Programmatically
 To train your crew programmatically, use the following steps:
 
@@ -27,21 +29,20 @@ To train your crew programmatically, use the following steps:
 3. Execute the training command within a try-except block to handle potential errors.
 
 ```python
-    n_iterations = 2
-    inputs = {"topic": "CrewAI Training"}
+n_iterations = 2
+inputs = {"topic": "CrewAI Training"}
+filename = "your_model.pkl"
 
-    try:
-        YourCrewName_Crew().crew().train(n_iterations= n_iterations, inputs=inputs)
+try:
+    YourCrewName_Crew().crew().train(n_iterations=n_iterations, inputs=inputs, filename=filename)
 
-    except Exception as e:
-        raise Exception(f"An error occurred while training the crew: {e}")
+except Exception as e:
+    raise Exception(f"An error occurred while training the crew: {e}")
 ```
 
-!!! note "Replace `<n_iterations>` with the desired number of training iterations. This determines how many times the agents will go through the training process."
-
-
 ### Key Points to Note:
 - **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
+- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
 - **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
 
 It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.

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

@@ -7,10 +7,10 @@ description: A comprehensive guide to starting a new CrewAI project, including t
 
 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:
+Before 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.
+2. The preferred way of setting up CrewAI is using the `crewai create crew` command. This will create a new project folder and install a skeleton template for you to work on.
 
 ## Prerequisites
 
@@ -35,11 +35,11 @@ It is highly recommended that you use virtual environments to ensure that your C
 
 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.
+   Poetry is CrewAI's preferred tool for package / dependency 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):
+Most users of CrewAI use a Code Editor / Integrated Development Environment (IDE) for building their Crews. You can use any code IDE of your choice. See 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/)
@@ -48,24 +48,13 @@ Most users of CrewAI a Code Editor / Integrated Development Environment (IDE) fo
 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.
+In this example, we will be using Venv as our virtual environment manager.
 
-To setup a virtual environment, run the following CLI command:
+To set up a virtual environment, run the following CLI command:
+To create a new CrewAI project, 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>
+$ crewai create crew <project_name>
 ```
 
 This command will create a new project folder with the following structure:
@@ -128,13 +117,13 @@ research_candidates_task:
     {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
+  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.
+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 won't recognize the reference properly.
 
 #### Example References
 agent.yaml
@@ -162,23 +151,22 @@ email_summarizer_task:
       - research_task
 ```
 
-Use the annotations are used to properly reference the agent and task in the crew.py file.
+Use the annotations 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
-
+* [@agent](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L17)
+* [@task](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L4)
+* [@crew](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L69)
+* [@llm](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L23)
+* [@tool](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L39)
+* [@callback](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L44)
+* [@output_json](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L29)
+* [@output_pydantic](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L34)
+* [@cache_handler](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L49)
 
 crew.py
 ```py
-...
+# ...
     @llm
     def mixtal_llm(self):
         return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
@@ -194,11 +182,9 @@ crew.py
         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:
@@ -254,6 +240,26 @@ $ 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.
 
+### Replay Tasks from Latest Crew Kickoff
+
+CrewAI now includes a replay feature that allows you to list the tasks from the last run and replay from a specific one. To use this feature, run:
+
+```shell
+$ crewai replay <task_id>
+```
+
+Replace `<task_id>` with the ID of the task you want to replay.
+
+### Reset Crew Memory
+
+If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature:
+
+```shell
+$ crewai reset-memory
+```
+
+This will clear the crew's memory, allowing for a fresh start.
+
 ## 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/AgentOps-Observability.md

@@ -36,7 +36,7 @@ Additionally, AgentOps provides session drilldowns for viewing Crew agent intera
 ### Using AgentOps
 
 1. **Create an API Key:**
-   Create a user API key here: [Create API Key](app.agentops.ai/account)
+   Create a user API key here: [Create API Key](https://app.agentops.ai/account)
 
 2. **Configure Your Environment:**
    Add your API key to your environment variables
@@ -83,4 +83,4 @@ For feature requests or bug reports, please reach out to the AgentOps team on th
 <span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
 <a href="https://app.agentops.ai/?=crew">🖇️ AgentOps Dashboard</a>
 <span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://docs.agentops.ai/introduction">📙 Documentation</a>
+<a href="https://docs.agentops.ai/introduction">📙 Documentation</a>

docs/how-to/Coding-Agents.md

@@ -22,11 +22,13 @@ coding_agent = Agent(
 )
 ```
 
+**Note**: The `allow_code_execution` parameter defaults to `False`.
+
 ## Important Considerations
 
 1. **Model Selection**: It is strongly recommended to use more capable models like Claude 3.5 Sonnet and GPT-4 when enabling code execution. These models have a better understanding of programming concepts and are more likely to generate correct and efficient code.
 
-2. **Error Handling**: The code execution feature includes error handling. If executed code raises an exception, the agent will receive the error message and can attempt to correct the code or provide alternative solutions.
+2. **Error Handling**: The code execution feature includes error handling. If executed code raises an exception, the agent will receive the error message and can attempt to correct the code or provide alternative solutions. The `max_retry_limit` parameter, which defaults to 2, controls the maximum number of retries for a task.
 
 3. **Dependencies**: To use the code execution feature, you need to install the `crewai_tools` package. If not installed, the agent will log an info message: "Coding tools not available. Install crewai_tools."
 
@@ -73,4 +75,4 @@ result = analysis_crew.kickoff()
 print(result)
 ```
 
-In this example, the `coding_agent` can write and execute Python code to perform data analysis tasks.
+In this example, the `coding_agent` can write and execute Python code to perform data analysis tasks.

docs/how-to/Conditional-Tasks.md

@@ -7,9 +7,10 @@ description: Learn how to use conditional tasks in a crewAI kickoff
 
 Conditional Tasks in crewAI allow for dynamic workflow adaptation based on the outcomes of previous tasks. This powerful feature enables crews to make decisions and execute tasks selectively, enhancing the flexibility and efficiency of your AI-driven processes.
 
+## Example Usage
+
 ```python
 from typing import List
-
 from pydantic import BaseModel
 from crewai import Agent, Crew
 from crewai.tasks.conditional_task import ConditionalTask
@@ -17,11 +18,10 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.task import Task
 from crewai_tools import SerperDevTool
 
-
 # Define a condition function for the conditional task
 # if false task will be skipped, true, then execute task
 def is_data_missing(output: TaskOutput) -> bool:
-    return len(output.pydantic.events) < 10: # this will skip this task
+    return len(output.pydantic.events) < 10  # this will skip this task
 
 # Define the agents
 data_fetcher_agent = Agent(
@@ -46,11 +46,9 @@ summary_generator_agent = Agent(
     verbose=True,
 )
 
-
 class EventOutput(BaseModel):
     events: List[str]
 
-
 task1 = Task(
     description="Fetch data about events in San Francisco using Serper tool",
     expected_output="List of 10 things to do in SF this week",
@@ -64,7 +62,7 @@ conditional_task = ConditionalTask(
         fetch more events using Serper tool so that
         we have a total of 10 events in SF this week..
         """,
-    expected_output="List of 10 Things to do in SF this week ",
+    expected_output="List of 10 Things to do in SF this week",
     condition=is_data_missing,
     agent=data_processor_agent,
 )
@@ -80,8 +78,10 @@ crew = Crew(
     agents=[data_fetcher_agent, data_processor_agent, summary_generator_agent],
     tasks=[task1, conditional_task, task3],
     verbose=True,
+    planning=True  # Enable planning feature
 )
 
+# Run the crew
 result = crew.kickoff()
 print("results", result)
-```
+```

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

@@ -1,6 +1,6 @@
 ---
 title: Forcing Tool Output as Result
-description: Learn how to force tool output as the result in of an Agent's task in crewAI.
+description: Learn how to force tool output as the result in of an Agent's task in CrewAI.
 ---
 
 ## Introduction
@@ -13,19 +13,20 @@ Here's an example of how to force the tool output as the result of an agent's ta
 
 ```python
 # ...
+from crewai.agent import Agent
+
 # Define a custom tool that returns the result as the answer
-coding_agent =Agent(
+coding_agent = Agent(
         role="Data Scientist",
-        goal="Product amazing reports on AI",
+        goal="Produce amazing reports on AI",
         backstory="You work with data and AI",
         tools=[MyCustomTool(result_as_answer=True)],
     )
-# ...
 ```
 
-### Workflow in Action
+## Workflow in Action
 
 1. **Task Execution**: The agent executes the task using the tool provided.
 2. **Tool Output**: The tool generates the output, which is captured as the task result.
-3. **Agent Interaction**: The agent my reflect and take learnings from the tool but the output is not modified.
+3. **Agent Interaction**: The agent may reflect and take learnings from the tool but the output is not modified.
 4. **Result Return**: The tool output is returned as the task result without any modifications.

docs/how-to/Hierarchical.md

@@ -56,6 +56,7 @@ project_crew = Crew(
     process=Process.hierarchical,  # Specifies the hierarchical management approach
     memory=True,  # Enable memory usage for enhanced task execution
     manager_agent=None,  # Optional: explicitly set a specific agent as manager instead of the manager_llm
+    planning=True,  # Enable planning feature for pre-execution strategy
 )
 ```
 

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

@@ -83,6 +83,7 @@ crew = Crew(
     tasks=[task1, task2],
     verbose=True,
     memory=True,
+    planning=True  # Enable planning feature for the crew
 )
 
 # Get your crew to work!

docs/how-to/Kickoff-async.md

@@ -9,6 +9,21 @@ CrewAI provides the ability to kickoff a crew asynchronously, allowing you to st
 ## Asynchronous Crew Execution
 To kickoff a crew asynchronously, use the `kickoff_async()` method. This method initiates the crew execution in a separate thread, allowing the main thread to continue executing other tasks.
 
+### Method Signature
+
+```python
+def kickoff_async(self, inputs: dict) -> CrewOutput:
+```
+
+### Parameters
+
+- `inputs` (dict): A dictionary containing the input data required for the tasks.
+
+### Returns
+
+- `CrewOutput`: An object representing the result of the crew execution.
+
+## Example
 Here's an example of how to kickoff a crew asynchronously:
 
 ```python
@@ -34,7 +49,6 @@ analysis_crew = Crew(
     tasks=[data_analysis_task]
 )
 
-# Execute the crew
+# Execute the crew asynchronously
 result = analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
-```
-
+```

docs/how-to/LLM-Connections.md

@@ -9,7 +9,7 @@ description: Comprehensive guide on integrating CrewAI with various Large Langua
     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.
 
-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 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.
 
 The platform supports connections to an array of Generative AI models, including:
 
@@ -37,6 +37,7 @@ 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.
 
@@ -47,8 +48,8 @@ os.environ[OPENAI_API_KEY]='' # No API Key required for Ollama
 ```
 
 ## 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```.   
+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`
 ```
 from crewai import Agent, Task, Crew
@@ -56,7 +57,7 @@ from langchain_ollama import ChatOllama
 import os
 os.environ["OPENAI_API_KEY"] = "NA"
 
-llm = Ollama(
+llm = ChatOllama(
     model = "llama3.1",
     base_url = "http://localhost:11434")
 
@@ -165,7 +166,7 @@ llm = ChatCohere()
 For Azure OpenAI API integration, set the following environment variables:
 ```sh
 
-os.environ[AZURE_OPENAI_DEPLOYMENT] = "You deployment"
+os.environ[AZURE_OPENAI_DEPLOYMENT] = "Your 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>"
@@ -191,5 +192,6 @@ 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.
+Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.

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

@@ -11,14 +11,13 @@ You must run `crew.kickoff()` before you can replay a task. Currently, only the
 
 Here's an example of how to replay from a task:
 
-### Replaying from specific task Using the CLI
+### Replaying from Specific Task Using the CLI
 To use the replay feature, follow these steps:
 
 1. Open your terminal or command prompt.
 2. Navigate to the directory where your CrewAI project is located.
 3. Run the following command:
-
-To view latest kickoff task_ids use:
+To view the latest kickoff task_ids use:
 ```shell
 crewai log-tasks-outputs
 ```
@@ -29,21 +28,25 @@ crewai replay -t <task_id>
 ```
 
 
-### Replaying from a task Programmatically
+### Replaying from a Task Programmatically
 To replay from a task programmatically, use the following steps:
 
 1. Specify the task_id and input parameters for the replay process.
 2. Execute the replay command within a try-except block to handle potential errors.
 
 ```python
-   def replay():
+  def replay():
     """
     Replay the crew execution from a specific task.
     """
     task_id = '<task_id>'
-    inputs = {"topic": "CrewAI Training"} # this is optional, you can pass in the inputs you want to replay otherwise uses the previous kickoffs inputs
+    inputs = {"topic": "CrewAI Training"}  # This is optional; you can pass in the inputs you want to replay; otherwise, it uses the previous kickoff's inputs.
     try:
         YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
 
+    except subprocess.CalledProcessError as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")
+
     except Exception as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")
+        raise Exception(f"An unexpected error occurred: {e}")
+```

docs/how-to/Sequential.md

@@ -18,7 +18,7 @@ The sequential process ensures tasks are executed one after the other, following
 To use the sequential process, assemble your crew and define tasks in the order they need to be executed.
 
 ```python
-from crewai import Crew, Process, Agent, Task
+from crewai import Crew, Process, Agent, Task, TaskOutput, CrewOutput
 
 # Define your agents
 researcher = Agent(
@@ -37,6 +37,7 @@ writer = Agent(
   backstory='A skilled writer with a talent for crafting compelling narratives'
 )
 
+# Define your tasks
 research_task = Task(description='Gather relevant data...', agent=researcher, expected_output='Raw Data')
 analysis_task = Task(description='Analyze the data...', agent=analyst, expected_output='Data Insights')
 writing_task = Task(description='Compose the report...', agent=writer, expected_output='Final Report')
@@ -50,6 +51,10 @@ report_crew = Crew(
 
 # Execute the crew
 result = report_crew.kickoff()
+
+# Accessing the type safe output
+task_output: TaskOutput = result.tasks[0].output
+crew_output: CrewOutput = result.output
 ```
 
 ### Workflow in Action
@@ -82,4 +87,4 @@ CrewAI tracks token usage across all tasks and agents. You can access these metr
 1. **Order Matters**: Arrange tasks in a logical sequence where each task builds upon the previous one.
 2. **Clear Task Descriptions**: Provide detailed descriptions for each task to guide the agents effectively.
 3. **Appropriate Agent Selection**: Match agents' skills and roles to the requirements of each task.
-4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones
+4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones.

docs/index.md

@@ -66,6 +66,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Planning
                 </a>
             </li>
+            <li>
+                <a href="./core-concepts/Testing">
+                    Testing
+                </a>
+            </li>
         </ul>
     </div>
     <div style="width:30%">

docs/tools/DALL-ETool.md

@@ -0,0 +1,41 @@
+# DALL-E Tool
+
+## Description
+This tool is used to give the Agent the ability to generate images using the DALL-E model. It is a transformer-based model that generates images from textual descriptions. This tool allows the Agent to generate images based on the text input provided by the user.
+
+## Installation
+Install the crewai_tools package
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+
+Remember that when using this tool, the text must be generated by the Agent itself. The text must be a description of the image you want to generate.
+
+```python
+from crewai_tools import DallETool
+
+Agent(
+    ...
+    tools=[DallETool()],
+)
+```
+
+If needed you can also tweak the parameters of the DALL-E model by passing them as arguments to the `DallETool` class. For example:
+
+```python
+from crewai_tools import DallETool
+
+dalle_tool = DallETool(model="dall-e-3",
+                       size="1024x1024",
+                       quality="standard",
+                       n=1)
+
+Agent(
+    ...
+    tools=[dalle_tool]
+)
+```
+
+The parameters are based on the `client.images.generate` method from the OpenAI API. For more information on the parameters, please refer to the [OpenAI API documentation](https://platform.openai.com/docs/guides/images/introduction?lang=python).

docs/tools/FileWriteTool.md

@@ -0,0 +1,33 @@
+# FileWriterTool Documentation
+
+## Description
+The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files. It is particularly useful in scenarios such as generating reports, saving logs, creating configuration files, and more. This tool supports creating new directories if they don't exist, making it easier to organize your output.
+
+## Installation
+Install the crewai_tools package to use the `FileWriterTool` in your projects:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+To get started with the `FileWriterTool`:
+
+```python
+from crewai_tools import FileWriterTool
+
+# Initialize the tool
+file_writer_tool = FileWriterTool()
+
+# Write content to a file in a specified directory
+result = file_writer_tool._run('example.txt', 'This is a test content.', 'test_directory')
+print(result)
+```
+
+## Arguments
+- `filename`: The name of the file you want to create or overwrite.
+- `content`: The content to write into the file.
+- `directory` (optional): The path to the directory where the file will be created. Defaults to the current directory (`.`). If the directory does not exist, it will be created.
+
+## Conclusion
+By integrating the `FileWriterTool` into your crews, the agents can execute the process of writing content to files and creating directories. This tool is essential for tasks that require saving output data, creating structured file systems, and more. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is straightforward and efficient.

docs/tools/FirecrawlCrawlWebsiteTool.md

@@ -0,0 +1,42 @@
+# FirecrawlCrawlWebsiteTool
+
+## Description
+
+[Firecrawl](https://firecrawl.dev) is a platform for crawling and convert any website into clean markdown or structured data.
+
+## Installation
+
+- Get an API key from [firecrawl.dev](https://firecrawl.dev) and set it in environment variables (`FIRECRAWL_API_KEY`).
+- Install the [Firecrawl SDK](https://github.com/mendableai/firecrawl) along with `crewai[tools]` package:
+
+```
+pip install firecrawl-py 'crewai[tools]'
+```
+
+## Example
+
+Utilize the FirecrawlScrapeFromWebsiteTool as follows to allow your agent to load websites:
+
+```python
+from crewai_tools import FirecrawlCrawlWebsiteTool
+
+tool = FirecrawlCrawlWebsiteTool(url='firecrawl.dev')
+```
+
+## Arguments
+
+- `api_key`: Optional. Specifies Firecrawl API key. Defaults is the `FIRECRAWL_API_KEY` environment variable.
+- `url`: The base URL to start crawling from.
+- `page_options`: Optional. 
+  - `onlyMainContent`: Optional. Only return the main content of the page excluding headers, navs, footers, etc.
+  - `includeHtml`: Optional. Include the raw HTML content of the page. Will output a html key in the response.
+- `crawler_options`: Optional. Options for controlling the crawling behavior.
+  - `includes`: Optional. URL patterns to include in the crawl.
+  - `exclude`: Optional. URL patterns to exclude from the crawl.
+  - `generateImgAltText`: Optional. Generate alt text for images using LLMs (requires a paid plan).
+  - `returnOnlyUrls`: Optional. If true, returns only the URLs as a list in the crawl status. Note: the response will be a list of URLs inside the data, not a list of documents.
+  - `maxDepth`: Optional. Maximum depth to crawl. Depth 1 is the base URL, depth 2 includes the base URL and its direct children, and so on.
+  - `mode`: Optional. The crawling mode to use. Fast mode crawls 4x faster on websites without a sitemap but may not be as accurate and shouldn't be used on heavily JavaScript-rendered websites.
+  - `limit`: Optional. Maximum number of pages to crawl.
+  - `timeout`: Optional. Timeout in milliseconds for the crawling operation.
+

docs/tools/FirecrawlScrapeWebsiteTool.md

@@ -0,0 +1,38 @@
+# FirecrawlScrapeWebsiteTool
+
+## Description
+
+[Firecrawl](https://firecrawl.dev) is a platform for crawling and convert any website into clean markdown or structured data.
+
+## Installation
+
+- Get an API key from [firecrawl.dev](https://firecrawl.dev) and set it in environment variables (`FIRECRAWL_API_KEY`).
+- Install the [Firecrawl SDK](https://github.com/mendableai/firecrawl) along with `crewai[tools]` package:
+
+```
+pip install firecrawl-py 'crewai[tools]'
+```
+
+## Example
+
+Utilize the FirecrawlScrapeWebsiteTool as follows to allow your agent to load websites:
+
+```python
+from crewai_tools import FirecrawlScrapeWebsiteTool
+
+tool = FirecrawlScrapeWebsiteTool(url='firecrawl.dev')
+```
+
+## Arguments
+
+- `api_key`: Optional. Specifies Firecrawl API key. Defaults is the `FIRECRAWL_API_KEY` environment variable.
+- `url`: The URL to scrape.
+- `page_options`: Optional. 
+  - `onlyMainContent`: Optional. Only return the main content of the page excluding headers, navs, footers, etc.
+  - `includeHtml`: Optional. Include the raw HTML content of the page. Will output a html key in the response.
+- `extractor_options`: Optional. Options for LLM-based extraction of structured information from the page content
+  - `mode`: The extraction mode to use, currently supports 'llm-extraction'
+  - `extractionPrompt`: Optional. A prompt describing what information to extract from the page
+  - `extractionSchema`: Optional. The schema for the data to be extracted
+- `timeout`: Optional. Timeout in milliseconds for the request
+

docs/tools/FirecrawlSearchTool.md

@@ -0,0 +1,35 @@
+# FirecrawlSearchTool
+
+## Description
+
+[Firecrawl](https://firecrawl.dev) is a platform for crawling and convert any website into clean markdown or structured data.
+
+## Installation
+
+- Get an API key from [firecrawl.dev](https://firecrawl.dev) and set it in environment variables (`FIRECRAWL_API_KEY`).
+- Install the [Firecrawl SDK](https://github.com/mendableai/firecrawl) along with `crewai[tools]` package:
+
+```
+pip install firecrawl-py 'crewai[tools]'
+```
+
+## Example
+
+Utilize the FirecrawlSearchTool as follows to allow your agent to load websites:
+
+```python
+from crewai_tools import FirecrawlSearchTool
+
+tool = FirecrawlSearchTool(query='what is firecrawl?')
+```
+
+## Arguments
+
+- `api_key`: Optional. Specifies Firecrawl API key. Defaults is the `FIRECRAWL_API_KEY` environment variable.
+- `query`: The search query string to be used for searching.
+- `page_options`: Optional. Options for result formatting.
+  - `onlyMainContent`: Optional. Only return the main content of the page excluding headers, navs, footers, etc.
+  - `includeHtml`: Optional. Include the raw HTML content of the page. Will output a html key in the response.
+  - `fetchPageContent`: Optional. Fetch the full content of the page.
+- `search_options`: Optional. Options for controlling the crawling behavior.
+  - `limit`: Optional. Maximum number of pages to crawl.

docs/tools/MySQLTool.md

@@ -0,0 +1,56 @@
+# MySQLSearchTool
+
+## Description
+This tool is designed to facilitate semantic searches within MySQL database tables. Leveraging the RAG (Retrieve and Generate) technology, the MySQLSearchTool provides users with an efficient means of querying database table content, specifically tailored for MySQL databases. It simplifies the process of finding relevant data through semantic search queries, making it an invaluable resource for users needing to perform advanced queries on extensive datasets within a MySQL database.
+
+## Installation
+To install the `crewai_tools` package and utilize the MySQLSearchTool, execute the following command in your terminal:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+Below is an example showcasing how to use the MySQLSearchTool to conduct a semantic search on a table within a MySQL database:
+
+```python
+from crewai_tools import MySQLSearchTool
+
+# Initialize the tool with the database URI and the target table name
+tool = MySQLSearchTool(db_uri='mysql://user:password@localhost:3306/mydatabase', table_name='employees')
+
+```
+
+## Arguments
+The MySQLSearchTool requires the following arguments for its operation:
+
+- `db_uri`: A string representing the URI of the MySQL database to be queried. This argument is mandatory and must include the necessary authentication details and the location of the database.
+- `table_name`: A string specifying the name of the table within the database on which the semantic search will be performed. This argument is mandatory.
+
+## Custom model and embeddings
+
+By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
+
+```python
+tool = MySQLSearchTool(
+    config=dict(
+        llm=dict(
+            provider="ollama", # or google, openai, anthropic, llama2, ...
+            config=dict(
+                model="llama2",
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google",
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
+)
+```

docs/tools/NL2SQLTool.md

@@ -0,0 +1,74 @@
+# NL2SQL Tool
+
+## Description
+
+This tool is used to convert natural language to SQL queries. When passsed to the agent it will generate queries and then use them to interact with the database.
+
+This enables multiple workflows like having an Agent to access the database fetch information based on the goal and then use the information to generate a response, report or any other output. Along with that proivdes the ability for the Agent to update the database based on its goal.
+
+**Attention**: Make sure that the Agent has access to a Read-Replica or that is okay for the Agent to run insert/update queries on the database.
+
+## Requirements
+
+- SqlAlchemy
+- Any DB compatible library (e.g. psycopg2, mysql-connector-python)
+
+## Installation
+Install the crewai_tools package
+```shell
+pip install 'crewai[tools]'
+```
+
+## Usage
+
+In order to use the NL2SQLTool, you need to pass the database URI to the tool. The URI should be in the format `dialect+driver://username:password@host:port/database`.
+
+
+```python
+from crewai_tools import NL2SQLTool
+
+# psycopg2 was installed to run this example with PostgreSQL
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+
+@agent
+def researcher(self) -> Agent:
+    return Agent(
+        config=self.agents_config["researcher"],
+        allow_delegation=False,
+        tools=[nl2sql]
+    )
+```
+
+## Example
+
+The primary task goal was:
+
+"Retrieve the average, maximum, and minimum monthly revenue for each city, but only include cities that have more than one user. Also, count the number of user in each city and sort the results by the average monthly revenue in descending order"
+
+So the Agent tried to get information from the DB, the first one is wrong so the Agent tries again and gets the correct information and passes to the next agent.
+
+![alt text](https://github.com/crewAIInc/crewAI-tools/blob/main/crewai_tools/tools/nl2sql/images/image-2.png?raw=true)
+![alt text](https://github.com/crewAIInc/crewAI-tools/raw/main/crewai_tools/tools/nl2sql/images/image-3.png)
+
+
+The second task goal was:
+
+"Review the data and create a detailed report, and then create the table on the database with the fields based on the data provided.
+Include information on the average, maximum, and minimum monthly revenue for each city, but only include cities that have more than one user. Also, count the number of users in each city and sort the results by the average monthly revenue in descending order."
+
+Now things start to get interesting, the Agent generates the SQL query to not only create the table but also insert the data into the table. And in the end the Agent still returns the final report which is exactly what was in the database.
+
+![alt text](https://github.com/crewAIInc/crewAI-tools/raw/main/crewai_tools/tools/nl2sql/images/image-4.png)
+![alt text](https://github.com/crewAIInc/crewAI-tools/raw/main/crewai_tools/tools/nl2sql/images/image-5.png)
+
+![alt text](https://github.com/crewAIInc/crewAI-tools/raw/main/crewai_tools/tools/nl2sql/images/image-9.png)
+![alt text](https://github.com/crewAIInc/crewAI-tools/raw/main/crewai_tools/tools/nl2sql/images/image-7.png)
+
+
+This is a simple example of how the NL2SQLTool can be used to interact with the database and generate reports based on the data in the database.
+
+The Tool provides endless possibilities on the logic of the Agent and how it can interact with the database.
+
+```
+ DB -> Agent -> ... -> Agent -> DB
+```

docs/tools/VisionTool.md

@@ -0,0 +1,30 @@
+# Vision Tool
+
+## Description
+
+This tool is used to extract text from images. When passed to the agent it will extract the text from the image and then use it to generate a response, report or any other output. The URL or the PATH of the image should be passed to the Agent.
+
+
+## Installation
+Install the crewai_tools package
+```shell
+pip install 'crewai[tools]'
+```
+
+## Usage
+
+In order to use the VisionTool, the OpenAI API key should be set in the environment variable `OPENAI_API_KEY`.
+
+```python
+from crewai_tools import VisionTool
+
+vision_tool = VisionTool()
+
+@agent
+def researcher(self) -> Agent:
+    return Agent(
+        config=self.agents_config["researcher"],
+        allow_delegation=False,
+        tools=[vision_tool]
+    )
+```

mkdocs.yml

@@ -136,9 +136,6 @@ nav:
     - Using LangChain Tools: 'core-concepts/Using-LangChain-Tools.md'
     - Using LlamaIndex Tools: 'core-concepts/Using-LlamaIndex-Tools.md'
   - How to Guides:
-    - Starting Your crewAI Project: 'how-to/Start-a-New-CrewAI-Project.md'
-    - Installing CrewAI: 'how-to/Installing-CrewAI.md'
-    - Getting Started: 'how-to/Creating-a-Crew-and-kick-it-off.md'
     - Create Custom Tools: 'how-to/Create-Custom-Tools.md'
     - Using Sequential Process: 'how-to/Sequential.md'
     - Using Hierarchical Process: 'how-to/Hierarchical.md'
@@ -155,29 +152,37 @@ nav:
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
   - Tools Docs:
-    - Google Serper Search: 'tools/SerperDevTool.md'
     - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
-    - Composio Tools: 'tools/ComposioTool.md'
-    - Code Interpreter: 'tools/CodeInterpreterTool.md'
-    - Scrape Website: 'tools/ScrapeWebsiteTool.md'
-    - Directory Read: 'tools/DirectoryReadTool.md'
-    - Exa Serch Web Loader: 'tools/EXASearchTool.md'
-    - File Read: 'tools/FileReadTool.md'
-    - Selenium Scraper: 'tools/SeleniumScrapingTool.md'
-    - Directory RAG Search: 'tools/DirectorySearchTool.md'
-    - PDF RAG Search: 'tools/PDFSearchTool.md'
-    - TXT RAG Search: 'tools/TXTSearchTool.md'
-    - CSV RAG Search: 'tools/CSVSearchTool.md'
-    - XML RAG Search: 'tools/XMLSearchTool.md'
-    - JSON RAG Search: 'tools/JSONSearchTool.md'
-    - Docx Rag Search: 'tools/DOCXSearchTool.md'
-    - MDX RAG Search: 'tools/MDXSearchTool.md'
-    - PG RAG Search: 'tools/PGSearchTool.md'
-    - Website RAG Search: 'tools/WebsiteSearchTool.md'
-    - Github RAG Search: 'tools/GitHubSearchTool.md'
     - Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'
-    - Youtube Video RAG Search: 'tools/YoutubeVideoSearchTool.md'
+    - Code Interpreter: 'tools/CodeInterpreterTool.md'
+    - Composio Tools: 'tools/ComposioTool.md'
+    - CSV RAG Search: 'tools/CSVSearchTool.md'
+    - DALL-E Tool: 'tools/DALL-ETool.md'
+    - Directory RAG Search: 'tools/DirectorySearchTool.md'
+    - Directory Read: 'tools/DirectoryReadTool.md'
+    - Docx Rag Search: 'tools/DOCXSearchTool.md'
+    - EXA Serch Web Loader: 'tools/EXASearchTool.md'
+    - File Read: 'tools/FileReadTool.md'
+    - File Write: 'tools/FileWriteTool.md'
+    - Firecrawl Crawl Website Tool: 'tools/FirecrawlCrawlWebsiteTool.md'
+    - Firecrawl Scrape Website Tool: 'tools/FirecrawlScrapeWebsiteTool.md'
+    - Firecrawl Search Tool: 'tools/FirecrgstawlSearchTool.md'
+    - Github RAG Search: 'tools/GitHubSearchTool.md'
+    - Google Serper Search: 'tools/SerperDevTool.md'
+    - JSON RAG Search: 'tools/JSONSearchTool.md'
+    - MDX RAG Search: 'tools/MDXSearchTool.md'
+    - MySQL Tool: 'tools/MySQLTool.md'
+    - NL2SQL Tool: 'tools/NL2SQLTool.md'
+    - PDF RAG Search: 'tools/PDFSearchTool.md'
+    - PG RAG Search: 'tools/PGSearchTool.md'
+    - Scrape Website: 'tools/ScrapeWebsiteTool.md'
+    - Selenium Scraper: 'tools/SeleniumScrapingTool.md'
+    - TXT RAG Search: 'tools/TXTSearchTool.md'
+    - Vision Tool: 'tools/VisionTool.md'
+    - Website RAG Search: 'tools/WebsiteSearchTool.md'
+    - XML RAG Search: 'tools/XMLSearchTool.md'
     - Youtube Channel RAG Search: 'tools/YoutubeChannelSearchTool.md'
+    - Youtube Video RAG Search: 'tools/YoutubeVideoSearchTool.md'
   - Examples:
     - Trip Planner Crew: https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner"
     - Create Instagram Post: https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.46.0"
+version = "0.51.1"
 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"
@@ -21,7 +21,7 @@ opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
 regex = "^2023.12.25"
-crewai-tools = { version = "^0.4.26", optional = true }
+crewai-tools = { version = "^0.8.3", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -46,7 +46,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.4.26"
+crewai-tools = "^0.8.3"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"

src/crewai/agent.py

@@ -19,18 +19,28 @@ from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_F
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-agentops = None
-try:
-    import agentops  # type: ignore # Name "agentops" already defined on line 21
-    from agentops import track_agent
-except ImportError:
 
-    def track_agent():
+def mock_agent_ops_provider():
+    def track_agent(*args, **kwargs):
         def noop(f):
             return f
 
         return noop
 
+    return track_agent
+
+
+agentops = None
+
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        import agentops  # type: ignore # Name "agentops" already defined on line 21
+        from agentops import track_agent
+    except ImportError:
+        track_agent = mock_agent_ops_provider()
+else:
+    track_agent = mock_agent_ops_provider()
+
 
 @track_agent()
 class Agent(BaseAgent):

src/crewai/cli/cli.py

@@ -60,10 +60,17 @@ def version(tools):
     default=5,
     help="Number of iterations to train the crew",
 )
-def train(n_iterations: int):
+@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):
     """Train the crew."""
-    click.echo(f"Training the crew for {n_iterations} iterations")
-    train_crew(n_iterations)
+    click.echo(f"Training the Crew for {n_iterations} iterations")
+    train_crew(n_iterations, filename)
 
 
 @crewai.command()

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

@@ -25,7 +25,7 @@ def train():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), inputs=inputs)
+        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.46.0" }
+crewai = { extras = ["tools"], version = "^0.51.0" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.46.0" }
+crewai = { extras = ["tools"], version = "^0.51.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.46.0" }
+crewai = { extras = ["tools"], version = "^0.51.0" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:main"

src/crewai/cli/train_crew.py

@@ -3,19 +3,22 @@ import subprocess
 import click
 
 
-def train_crew(n_iterations: int) -> None:
+def train_crew(n_iterations: int, filename: str) -> 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)]
+    command = ["poetry", "run", "train", str(n_iterations), filename]
 
     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/crew.py

@@ -3,6 +3,7 @@ import json
 import uuid
 from concurrent.futures import Future
 from hashlib import md5
+import os
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 from langchain_core.callbacks import BaseCallbackHandler
@@ -34,7 +35,9 @@ 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.constants import (
+    TRAINING_DATA_FILE,
+)
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.formatter import (
@@ -45,10 +48,13 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-try:
-    import agentops
-except ImportError:
-    agentops = None
+
+agentops = None
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        import agentops
+    except ImportError:
+        pass
 
 if TYPE_CHECKING:
     from crewai.pipeline.pipeline import Pipeline
@@ -388,7 +394,7 @@ class Crew(BaseModel):
         del task_config["agent"]
         return Task(**task_config, agent=task_agent)
 
-    def _setup_for_training(self) -> None:
+    def _setup_for_training(self, filename: str) -> None:
         """Sets up the crew for training."""
         self._train = True
 
@@ -399,11 +405,13 @@ class Crew(BaseModel):
             agent.allow_delegation = False
 
         CrewTrainingHandler(TRAINING_DATA_FILE).initialize_file()
-        CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).initialize_file()
+        CrewTrainingHandler(filename).initialize_file()
 
-    def train(self, n_iterations: int, inputs: Optional[Dict[str, Any]] = {}) -> None:
+    def train(
+        self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {}
+    ) -> None:
         """Trains the crew for a given number of iterations."""
-        self._setup_for_training()
+        self._setup_for_training(filename)
 
         for n_iteration in range(n_iterations):
             self._train_iteration = n_iteration
@@ -416,7 +424,7 @@ class Crew(BaseModel):
                 training_data=training_data, agent_id=str(agent.id)
             )
 
-            CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).save_trained_data(
+            CrewTrainingHandler(filename).save_trained_data(
                 agent_id=str(agent.role), trained_data=result.model_dump()
             )
 
@@ -932,6 +940,9 @@ class Crew(BaseModel):
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations."""
+        self._test_execution_span = self._telemetry.test_execution_span(
+            self, n_iterations, inputs, openai_model_name
+        )
         evaluator = CrewEvaluator(self, openai_model_name)
 
         for i in range(1, n_iterations + 1):

src/crewai/memory/storage/rag_storage.py

@@ -47,7 +47,7 @@ class RAGStorage(Storage):
             os.environ["OPENAI_API_KEY"] = "fake"
 
         agents = crew.agents if crew else []
-        agents = [agent.role for agent in agents]
+        agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
 
         config = {
@@ -77,6 +77,12 @@ class RAGStorage(Storage):
         self.app.llm = FakeLLM()
         if allow_reset:
             self.app.reset()
+            
+    def _sanitize_role(self, role: str) -> str:
+        """
+        Sanitizes agent roles to ensure valid directory names.
+        """
+        return role.replace('\n', '').replace(' ', '_').replace('/', '_')
 
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         self._generate_embedding(value, metadata)

src/crewai/telemetry/telemetry.py

@@ -98,65 +98,70 @@ class Telemetry:
                 self._add_attribute(span, "crew_memory", crew.memory)
                 self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
                 self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
-                self._add_attribute(
-                    span,
-                    "crew_agents",
-                    json.dumps(
-                        [
-                            {
-                                "key": agent.key,
-                                "id": str(agent.id),
-                                "role": agent.role,
-                                "goal": agent.goal,
-                                "backstory": agent.backstory,
-                                "verbose?": agent.verbose,
-                                "max_iter": agent.max_iter,
-                                "max_rpm": agent.max_rpm,
-                                "i18n": agent.i18n.prompt_file,
-                                "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
-                                "delegation_enabled?": agent.allow_delegation,
-                                "tools_names": [
-                                    tool.name.casefold() for tool in agent.tools or []
-                                ],
-                            }
-                            for agent in crew.agents
-                        ]
-                    ),
-                )
-                self._add_attribute(
-                    span,
-                    "crew_tasks",
-                    json.dumps(
-                        [
-                            {
-                                "key": task.key,
-                                "id": str(task.id),
-                                "description": task.description,
-                                "expected_output": task.expected_output,
-                                "async_execution?": task.async_execution,
-                                "human_input?": task.human_input,
-                                "agent_role": task.agent.role if task.agent else "None",
-                                "agent_key": task.agent.key if task.agent else None,
-                                "context": (
-                                    [task.description for task in task.context]
-                                    if task.context
-                                    else None
-                                ),
-                                "tools_names": [
-                                    tool.name.casefold() for tool in task.tools or []
-                                ],
-                            }
-                            for task in crew.tasks
-                        ]
-                    ),
-                )
-                self._add_attribute(span, "platform", platform.platform())
-                self._add_attribute(span, "platform_release", platform.release())
-                self._add_attribute(span, "platform_system", platform.system())
-                self._add_attribute(span, "platform_version", platform.version())
-                self._add_attribute(span, "cpus", os.cpu_count())
-
                 if crew.share_crew:
+                    self._add_attribute(
+                        span,
+                        "crew_agents",
+                        json.dumps(
+                            [
+                                {
+                                    "key": agent.key,
+                                    "id": str(agent.id),
+                                    "role": agent.role,
+                                    "goal": agent.goal,
+                                    "backstory": agent.backstory,
+                                    "verbose?": agent.verbose,
+                                    "max_iter": agent.max_iter,
+                                    "max_rpm": agent.max_rpm,
+                                    "i18n": agent.i18n.prompt_file,
+                                    "llm": json.dumps(
+                                        self._safe_llm_attributes(agent.llm)
+                                    ),
+                                    "delegation_enabled?": agent.allow_delegation,
+                                    "tools_names": [
+                                        tool.name.casefold()
+                                        for tool in agent.tools or []
+                                    ],
+                                }
+                                for agent in crew.agents
+                            ]
+                        ),
+                    )
+                    self._add_attribute(
+                        span,
+                        "crew_tasks",
+                        json.dumps(
+                            [
+                                {
+                                    "key": task.key,
+                                    "id": str(task.id),
+                                    "description": task.description,
+                                    "expected_output": task.expected_output,
+                                    "async_execution?": task.async_execution,
+                                    "human_input?": task.human_input,
+                                    "agent_role": task.agent.role
+                                    if task.agent
+                                    else "None",
+                                    "agent_key": task.agent.key if task.agent else None,
+                                    "context": (
+                                        [task.description for task in task.context]
+                                        if task.context
+                                        else None
+                                    ),
+                                    "tools_names": [
+                                        tool.name.casefold()
+                                        for tool in task.tools or []
+                                    ],
+                                }
+                                for task in crew.tasks
+                            ]
+                        ),
+                    )
+                    self._add_attribute(span, "platform", platform.platform())
+                    self._add_attribute(span, "platform_release", platform.release())
+                    self._add_attribute(span, "platform_system", platform.system())
+                    self._add_attribute(span, "platform_version", platform.version())
+                    self._add_attribute(span, "cpus", os.cpu_count())
                     self._add_attribute(
                         span, "crew_inputs", json.dumps(inputs) if inputs else None
                     )
@@ -289,6 +294,61 @@ class Telemetry:
             except Exception:
                 pass
 
+    def individual_test_result_span(
+        self, crew: Crew, quality: int, exec_time: int, model_name: str
+    ):
+        if self.ready:
+            try:
+                tracer = trace.get_tracer("crewai.telemetry")
+                span = tracer.start_span("Crew Individual Test Result")
+
+                self._add_attribute(
+                    span,
+                    "crewai_version",
+                    pkg_resources.get_distribution("crewai").version,
+                )
+                self._add_attribute(span, "crew_key", crew.key)
+                self._add_attribute(span, "crew_id", str(crew.id))
+                self._add_attribute(span, "quality", str(quality))
+                self._add_attribute(span, "exec_time", str(exec_time))
+                self._add_attribute(span, "model_name", model_name)
+                span.set_status(Status(StatusCode.OK))
+                span.end()
+            except Exception:
+                pass
+
+    def test_execution_span(
+        self,
+        crew: Crew,
+        iterations: int,
+        inputs: dict[str, Any] | None,
+        model_name: str,
+    ):
+        if self.ready:
+            try:
+                tracer = trace.get_tracer("crewai.telemetry")
+                span = tracer.start_span("Crew Test Execution")
+
+                self._add_attribute(
+                    span,
+                    "crewai_version",
+                    pkg_resources.get_distribution("crewai").version,
+                )
+                self._add_attribute(span, "crew_key", crew.key)
+                self._add_attribute(span, "crew_id", str(crew.id))
+                self._add_attribute(span, "iterations", str(iterations))
+                self._add_attribute(span, "model_name", model_name)
+
+                if crew.share_crew:
+                    self._add_attribute(
+                        span, "inputs", json.dumps(inputs) if inputs else None
+                    )
+
+                span.set_status(Status(StatusCode.OK))
+                span.end()
+            except Exception:
+                pass
+
     def crew_execution_span(self, crew: Crew, inputs: dict[str, Any] | None):
         """Records the complete execution of a crew.
         This is only collected if the user has opted-in to share the crew.

src/crewai/tools/tool_usage.py

@@ -1,5 +1,6 @@
 import ast
 from difflib import SequenceMatcher
+import os
 from textwrap import dedent
 from typing import Any, List, Union
 
@@ -11,10 +12,12 @@ from crewai.telemetry import Telemetry
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.utilities import I18N, Converter, ConverterError, Printer
 
-try:
-    import agentops
-except ImportError:
-    agentops = None
+agentops = None
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        import agentops
+    except ImportError:
+        pass
 
 OPENAI_BIGGER_MODELS = ["gpt-4o"]
 
@@ -68,7 +71,14 @@ class ToolUsage:
         self.task = task
         self.action = action
         self.function_calling_llm = function_calling_llm
-
+      
+        # Handling bug (see https://github.com/langchain-ai/langchain/pull/16395): raise an error if tools_names have space for ChatOpenAI
+        if isinstance(self.function_calling_llm, ChatOpenAI):
+            if " " in self.tools_names:
+                raise Exception(
+                    "Tools names should not have spaces for ChatOpenAI models."
+                )
+              
         # Set the maximum parsing attempts for bigger models
         if (isinstance(self.function_calling_llm, ChatOpenAI)) and (
             self.function_calling_llm.openai_api_base is None

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -8,6 +8,7 @@ from rich.table import Table
 from crewai.agent import Agent
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
+from crewai.telemetry import Telemetry
 
 
 class TaskEvaluationPydanticOutput(BaseModel):
@@ -34,6 +35,7 @@ class CrewEvaluator:
     def __init__(self, crew, openai_model_name: str):
         self.crew = crew
         self.openai_model_name = openai_model_name
+        self._telemetry = Telemetry()
         self._setup_for_evaluating()
 
     def _setup_for_evaluating(self) -> None:
@@ -155,6 +157,12 @@ class CrewEvaluator:
         evaluation_result = evaluation_task.execute_sync()
 
         if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput):
+            self._test_result_span = self._telemetry.individual_test_result_span(
+                self.crew,
+                evaluation_result.pydantic.quality,
+                current_task._execution_time,
+                self.openai_model_name,
+            )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(
                 current_task._execution_time

src/crewai/utilities/evaluators/task_evaluator.py

@@ -1,3 +1,4 @@
+import os
 from typing import List
 
 from langchain_openai import ChatOpenAI
@@ -6,17 +7,27 @@ from pydantic import BaseModel, Field
 from crewai.utilities import Converter
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
-agentops = None
-try:
-    from agentops import track_agent
-except ImportError:
 
-    def track_agent(name):
+def mock_agent_ops_provider():
+    def track_agent(*args, **kwargs):
         def noop(f):
             return f
 
         return noop
 
+    return track_agent
+
+
+agentops = None
+
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        from agentops import track_agent
+    except ImportError:
+        track_agent = mock_agent_ops_provider()
+else:
+    track_agent = mock_agent_ops_provider()
+
 
 class Entity(BaseModel):
     name: str = Field(description="The name of the entity.")

src/crewai/utilities/file_handler.py

@@ -1,7 +1,5 @@
 import os
 import pickle
-
-
 from datetime import datetime
 
 
@@ -32,14 +30,16 @@ class PickleHandler:
         Parameters:
         - file_name (str): The name of the file for saving and loading data.
         """
+        if not file_name.endswith(".pkl"):
+            file_name += ".pkl"
+
         self.file_path = os.path.join(os.getcwd(), file_name)
 
     def initialize_file(self) -> None:
         """
-        Initialize the file with an empty dictionary if it does not exist or is empty.
+        Initialize the file with an empty dictionary and overwrite any existing data.
         """
-        if not os.path.exists(self.file_path) or os.path.getsize(self.file_path) == 0:
-            self.save({})  # Save an empty dictionary to initialize the file
+        self.save({})
 
     def save(self, data) -> None:
         """

tests/cassettes/test_json_property_without_output_json.yaml

@@ -27,7 +27,7 @@ interactions:
       host:
       - api.openai.com
       user-agent:
-      - OpenAI/Python 1.35.14
+      - OpenAI/Python 1.40.3
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -37,7 +37,7 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.35.14
+      - 1.40.3
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
@@ -46,64 +46,64 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: 'data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
+      string: 'data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"role":"assistant","content":"","refusal":null},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"Thought"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"Thought"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         I"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         now"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         can"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         give"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         a"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         great"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         answer"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"\n"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"\n"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"Final"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"Final"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         Answer"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9utqOzPf2ZjkSgVMuFXAypJoBznjA","object":"chat.completion.chunk","created":1723348060,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_3aa7262c27","choices":[{"index":0,"delta":{"content":"
         "},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9ltULlJQMDDjI2f7PpkjJ7DsxWjEQ","object":"chat.completion.chunk","created":1721201741,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_400f27fa1f","choices":[{"index":0,"delta":{"content":"4"},"logprobs":null,"finish_reason":null}]}
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+        7qQddToFaN+sCpynxnHJ9Iw2Drzu1wrDQxrAZY8xsTQjzjLp7UHYB8amrLSt0Tuvux1D5cq5lHl2
+        laksh+SY/AMAAP//AwCqdjJQ7AIAAA==
     headers:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 89ed158dee46a566-MIA
+      - 8b153f3f4cb302e9-GRU
       Connection:
       - keep-alive
       Content-Encoding:
@@ -223,35 +225,37 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Sat, 06 Jul 2024 05:09:42 GMT
+      - Sun, 11 Aug 2024 03:47:55 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
       - chunked
+      X-Content-Type-Options:
+      - nosniff
       alt-svc:
       - h3=":443"; ma=86400
       openai-organization:
       - crewai-iuxna1
       openai-processing-ms:
-      - '144'
+      - '139'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
-      - max-age=31536000; includeSubDomains
+      - max-age=15552000; includeSubDomains; preload
       x-ratelimit-limit-requests:
       - '10000'
       x-ratelimit-limit-tokens:
-      - '16000000'
+      - '30000000'
       x-ratelimit-remaining-requests:
       - '9999'
       x-ratelimit-remaining-tokens:
-      - '15999969'
+      - '29999968'
       x-ratelimit-reset-requests:
       - 6ms
       x-ratelimit-reset-tokens:
       - 0s
       x-request-id:
-      - req_990566332b9b1851c581486c0a4da0e6
+      - req_3d3dc2e3c00204620b28fa9a5ae45fb8
     status:
       code: 200
       message: OK

tests/cli/cli_test.py

@@ -15,18 +15,18 @@ def runner():
 def test_train_default_iterations(train_crew, runner):
     result = runner.invoke(train)
 
-    train_crew.assert_called_once_with(5)
+    train_crew.assert_called_once_with(5, "trained_agents_data.pkl")
     assert result.exit_code == 0
-    assert "Training the crew for 5 iterations" in result.output
+    assert "Training the Crew for 5 iterations" in result.output
 
 
 @mock.patch("crewai.cli.cli.train_crew")
 def test_train_custom_iterations(train_crew, runner):
     result = runner.invoke(train, ["--n_iterations", "10"])
 
-    train_crew.assert_called_once_with(10)
+    train_crew.assert_called_once_with(10, "trained_agents_data.pkl")
     assert result.exit_code == 0
-    assert "Training the crew for 10 iterations" in result.output
+    assert "Training the Crew for 10 iterations" in result.output
 
 
 @mock.patch("crewai.cli.cli.train_crew")

tests/cli/train_crew_test.py

@@ -6,7 +6,6 @@ from crewai.cli.train_crew import train_crew
 
 @mock.patch("crewai.cli.train_crew.subprocess.run")
 def test_train_crew_positive_iterations(mock_subprocess_run):
-    # Arrange
     n_iterations = 5
     mock_subprocess_run.return_value = subprocess.CompletedProcess(
         args=["poetry", "run", "train", str(n_iterations)],
@@ -15,12 +14,10 @@ def test_train_crew_positive_iterations(mock_subprocess_run):
         stderr="",
     )
 
-    # Act
-    train_crew(n_iterations)
+    train_crew(n_iterations, "trained_agents_data.pkl")
 
-    # Assert
     mock_subprocess_run.assert_called_once_with(
-        ["poetry", "run", "train", str(n_iterations)],
+        ["poetry", "run", "train", str(n_iterations), "trained_agents_data.pkl"],
         capture_output=False,
         text=True,
         check=True,
@@ -29,7 +26,7 @@ def test_train_crew_positive_iterations(mock_subprocess_run):
 
 @mock.patch("crewai.cli.train_crew.click")
 def test_train_crew_zero_iterations(click):
-    train_crew(0)
+    train_crew(0, "trained_agents_data.pkl")
     click.echo.assert_called_once_with(
         "An unexpected error occurred: The number of iterations must be a positive integer.",
         err=True,
@@ -38,7 +35,7 @@ def test_train_crew_zero_iterations(click):
 
 @mock.patch("crewai.cli.train_crew.click")
 def test_train_crew_negative_iterations(click):
-    train_crew(-2)
+    train_crew(-2, "trained_agents_data.pkl")
     click.echo.assert_called_once_with(
         "An unexpected error occurred: The number of iterations must be a positive integer.",
         err=True,
@@ -55,10 +52,13 @@ def test_train_crew_called_process_error(mock_subprocess_run, click):
         output="Error",
         stderr="Some error occurred",
     )
-    train_crew(n_iterations)
+    train_crew(n_iterations, "trained_agents_data.pkl")
 
     mock_subprocess_run.assert_called_once_with(
-        ["poetry", "run", "train", "5"], capture_output=False, text=True, check=True
+        ["poetry", "run", "train", str(n_iterations), "trained_agents_data.pkl"],
+        capture_output=False,
+        text=True,
+        check=True,
     )
     click.echo.assert_has_calls(
         [
@@ -74,13 +74,15 @@ def test_train_crew_called_process_error(mock_subprocess_run, click):
 @mock.patch("crewai.cli.train_crew.click")
 @mock.patch("crewai.cli.train_crew.subprocess.run")
 def test_train_crew_unexpected_exception(mock_subprocess_run, click):
-    # Arrange
     n_iterations = 5
     mock_subprocess_run.side_effect = Exception("Unexpected error")
-    train_crew(n_iterations)
+    train_crew(n_iterations, "trained_agents_data.pkl")
 
     mock_subprocess_run.assert_called_once_with(
-        ["poetry", "run", "train", "5"], capture_output=False, text=True, check=True
+        ["poetry", "run", "train", str(n_iterations), "trained_agents_data.pkl"],
+        capture_output=False,
+        text=True,
+        check=True,
     )
     click.echo.assert_called_once_with(
         "An unexpected error occurred: Unexpected error", err=True

tests/crew_test.py

@@ -8,6 +8,7 @@ from unittest.mock import MagicMock, patch
 
 import pydantic_core
 import pytest
+
 from crewai.agent import Agent
 from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
@@ -621,7 +622,7 @@ def test_sequential_async_task_execution_completion():
 
     sequential_result = sequential_crew.kickoff()
     assert sequential_result.raw.startswith(
-        "The history of artificial intelligence (AI) is marked by several pivotal events that have shaped its evolution and impact on various sectors."
+        "**The Evolution of Artificial Intelligence: Key Milestones in Shaping AI**"
     )
 
 
@@ -1806,7 +1807,9 @@ def test_crew_train_success(task_evaluator, crew_training_handler, kickoff):
         agents=[researcher, writer],
         tasks=[task],
     )
-    crew.train(n_iterations=2, inputs={"topic": "AI"})
+    crew.train(
+        n_iterations=2, inputs={"topic": "AI"}, filename="trained_agents_data.pkl"
+    )
     task_evaluator.assert_has_calls(
         [
             mock.call(researcher),
@@ -1890,7 +1893,7 @@ def test__setup_for_training():
     for agent in agents:
         assert agent.allow_delegation is True
 
-    crew._setup_for_training()
+    crew._setup_for_training("trained_agents_data.pkl")
 
     assert crew._train is True
     assert task.human_input is True

tests/memory/cassettes/test_save_and_search.yaml

@@ -18,7 +18,7 @@ interactions:
       host:
       - api.openai.com
       user-agent:
-      - OpenAI/Python 1.35.10
+      - OpenAI/Python 1.40.3
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -28,7 +28,7 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.35.10
+      - 1.40.3
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
@@ -38,120 +38,120 @@ interactions:
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     body:
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tests/task_test.py

@@ -1,5 +1,6 @@
 """Test Agent creation and execution basic functionality."""
 
+import os
 import hashlib
 import json
 from unittest.mock import MagicMock, patch
@@ -258,7 +259,7 @@ def test_output_pydantic_hierarchical():
     )
     result = crew.kickoff()
     assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
+    assert result.to_dict() == {"score": 5}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -277,6 +278,7 @@ def test_output_json_sequential():
         description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
         expected_output="The score of the title.",
         output_json=ScoreOutput,
+        output_file="score.json",
         agent=scorer,
     )
 
@@ -314,8 +316,8 @@ def test_output_json_hierarchical():
         manager_llm=ChatOpenAI(model="gpt-4o"),
     )
     result = crew.kickoff()
-    assert '{"score": 4}' == result.json
-    assert result.to_dict() == {"score": 4}
+    assert '{"score": 5}' == result.json
+    assert result.to_dict() == {"score": 5}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -399,8 +401,8 @@ def test_output_json_dict_hierarchical():
         manager_llm=ChatOpenAI(model="gpt-4o"),
     )
     result = crew.kickoff()
-    assert {"score": 5} == result.json_dict
-    assert result.to_dict() == {"score": 5}
+    assert {"score": 4} == result.json_dict
+    assert result.to_dict() == {"score": 4}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -471,7 +473,7 @@ def test_output_json_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2])
     result = crew.kickoff()
-    assert '{"score": 5}' == result.json
+    assert '{"score": 4}' == result.json
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -519,11 +521,13 @@ def test_save_task_json_output():
     )
 
     crew = Crew(agents=[scorer], tasks=[task])
+    crew.kickoff()
 
-    with patch.object(Task, "_save_file") as save_file:
-        save_file.return_value = None
-        crew.kickoff()
-        save_file.assert_called_once_with({"score": 4})
+    output_file_exists = os.path.exists("score.json")
+    assert output_file_exists
+    assert {"score": 4} == json.loads(open("score.json").read())
+    if output_file_exists:
+        os.remove("score.json")
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -547,11 +551,13 @@ def test_save_task_pydantic_output():
     )
 
     crew = Crew(agents=[scorer], tasks=[task])
+    crew.kickoff()
 
-    with patch.object(Task, "_save_file") as save_file:
-        save_file.return_value = None
-        crew.kickoff()
-        save_file.assert_called_once_with('{"score":4}')
+    output_file_exists = os.path.exists("score.json")
+    assert output_file_exists
+    assert {"score": 4} == json.loads(open("score.json").read())
+    if output_file_exists:
+        os.remove("score.json")
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])