feat: Add only on release to deploy docs

Create Cli.md

.github/ISSUE_TEMPLATE/bug_report.md

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

.github/ISSUE_TEMPLATE/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/config.yml

@@ -0,0 +1 @@
+blank_issues_enabled: false

.github/ISSUE_TEMPLATE/custom.md

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

.github/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

.github/workflows/mkdocs.yml

@@ -1,10 +1,8 @@
 name: Deploy MkDocs
 
 on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
+  release:
+    types: [published]
 
 permissions:
   contents: write

.github/workflows/security-checker.yml

@@ -0,0 +1,23 @@
+name: Security Checker
+
+on: [pull_request]
+
+jobs:
+  security-check:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11.9"
+
+      - name: Install dependencies
+        run: pip install bandit
+
+      - name: Run Bandit
+        run: bandit -c pyproject.toml -r src/ -lll
+

.github/workflows/stale.yml

@@ -24,3 +24,4 @@ jobs:
         stale-pr-message: 'This PR is stale because it has been open for 45 days with no activity.'
         days-before-pr-stale: 45
         days-before-pr-close: -1
+        operations-per-run: 500

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/Cli.md

@@ -0,0 +1,142 @@
+# CrewAI CLI Documentation
+
+The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews and pipelines.
+
+## Installation
+
+To use the CrewAI CLI, make sure you have CrewAI & Poetry installed:
+
+```
+pip install crewai poetry
+```
+
+## Basic Usage
+
+The basic structure of a CrewAI CLI command is:
+
+```
+crewai [COMMAND] [OPTIONS] [ARGUMENTS]
+```
+
+## Available Commands
+
+### 1. create
+
+Create a new crew or pipeline.
+
+```
+crewai create [OPTIONS] TYPE NAME
+```
+
+- `TYPE`: Choose between "crew" or "pipeline"
+- `NAME`: Name of the crew or pipeline
+- `--router`: (Optional) Create a pipeline with router functionality
+
+Example:
+```
+crewai create crew my_new_crew
+crewai create pipeline my_new_pipeline --router
+```
+
+### 2. version
+
+Show the installed version of CrewAI.
+
+```
+crewai version [OPTIONS]
+```
+
+- `--tools`: (Optional) Show the installed version of CrewAI tools
+
+Example:
+```
+crewai version
+crewai version --tools
+```
+
+### 3. train
+
+Train the crew for a specified number of iterations.
+
+```
+crewai train [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
+- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
+
+Example:
+```
+crewai train -n 10 -f my_training_data.pkl
+```
+
+### 4. replay
+
+Replay the crew execution from a specific task.
+
+```
+crewai replay [OPTIONS]
+```
+
+- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
+
+Example:
+```
+crewai replay -t task_123456
+```
+
+### 5. log_tasks_outputs
+
+Retrieve your latest crew.kickoff() task outputs.
+
+```
+crewai log_tasks_outputs
+```
+
+### 6. reset_memories
+
+Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
+
+```
+crewai reset_memories [OPTIONS]
+```
+
+- `-l, --long`: Reset LONG TERM memory
+- `-s, --short`: Reset SHORT TERM memory
+- `-e, --entities`: Reset ENTITIES memory
+- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
+- `-a, --all`: Reset ALL memories
+
+Example:
+```
+crewai reset_memories --long --short
+crewai reset_memories --all
+```
+
+### 7. test
+
+Test the crew and evaluate the results.
+
+```
+crewai test [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
+- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
+
+Example:
+```
+crewai test -n 5 -m gpt-3.5-turbo
+```
+
+### 8. run
+
+Run the crew.
+
+```
+crewai run
+```
+
+## Note
+
+Make sure to run these commands from the directory where your CrewAI project is set up. Some commands may require additional configuration or setup within your project structure.

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/Create-a-New-CrewAI-Pipeline-Template-Method.md

@@ -0,0 +1,129 @@
+# Creating a CrewAI Pipeline Project
+
+Welcome to the comprehensive guide for creating a new CrewAI pipeline project. This document will walk you through the steps to create, customize, and run your CrewAI pipeline project, ensuring you have everything you need to get started.
+
+To learn more about CrewAI pipelines, visit the [CrewAI documentation](https://docs.crewai.com/core-concepts/Pipeline/).
+
+## Prerequisites
+
+Before getting started with CrewAI pipelines, make sure that you have installed CrewAI via pip:
+
+```shell
+$ pip install crewai crewai-tools
+```
+
+The same prerequisites for virtual environments and Code IDEs apply as in regular CrewAI projects.
+
+## Creating a New Pipeline Project
+
+To create a new CrewAI pipeline project, you have two options:
+
+1. For a basic pipeline template:
+
+```shell
+$ crewai create pipeline <project_name>
+```
+
+2. For a pipeline example that includes a router:
+
+```shell
+$ crewai create pipeline --router <project_name>
+```
+
+These commands will create a new project folder with the following structure:
+
+```
+<project_name>/
+├── README.md
+├── poetry.lock
+├── pyproject.toml
+├── src/
+│   └── <project_name>/
+│       ├── __init__.py
+│       ├── main.py
+│       ├── crews/
+│       │   ├── crew1/
+│       │   │   ├── crew1.py
+│       │   │   └── config/
+│       │   │       ├── agents.yaml
+│       │   │       └── tasks.yaml
+│       │   ├── crew2/
+│       │   │   ├── crew2.py
+│       │   │   └── config/
+│       │   │       ├── agents.yaml
+│       │   │       └── tasks.yaml
+│       ├── pipelines/
+│       │   ├── __init__.py
+│       │   ├── pipeline1.py
+│       │   └── pipeline2.py
+│       └── tools/
+│           ├── __init__.py
+│           └── custom_tool.py
+└── tests/
+```
+
+## Customizing Your Pipeline Project
+
+To customize your pipeline project, you can:
+
+1. Modify the crew files in `src/<project_name>/crews/` to define your agents and tasks for each crew.
+2. Modify the pipeline files in `src/<project_name>/pipelines/` to define your pipeline structure.
+3. Modify `src/<project_name>/main.py` to set up and run your pipelines.
+4. Add your environment variables into the `.env` file.
+
+### Example: Defining a Pipeline
+
+Here's an example of how to define a pipeline in `src/<project_name>/pipelines/normal_pipeline.py`:
+
+```python
+from crewai import Pipeline
+from crewai.project import PipelineBase
+from ..crews.normal_crew import NormalCrew
+
+@PipelineBase
+class NormalPipeline:
+    def __init__(self):
+        # Initialize crews
+        self.normal_crew = NormalCrew().crew()
+
+    def create_pipeline(self):
+        return Pipeline(
+            stages=[
+                self.normal_crew
+            ]
+        )
+
+    async def kickoff(self, inputs):
+        pipeline = self.create_pipeline()
+        results = await pipeline.kickoff(inputs)
+        return results
+```
+
+### Annotations
+
+The main annotation you'll use for pipelines is `@PipelineBase`. This annotation is used to decorate your pipeline classes, similar to how `@CrewBase` is used for crews.
+
+## Installing Dependencies
+
+To install the dependencies for your project, use Poetry:
+
+```shell
+$ cd <project_name>
+$ crewai install
+```
+
+## Running Your Pipeline Project
+
+To run your pipeline project, use the following command:
+
+```shell
+$ crewai run
+```
+
+This will initialize your pipeline and begin task execution as defined in your `main.py` file.
+
+## Deploying Your Pipeline Project
+
+Pipelines can be deployed in the same way as regular CrewAI projects. The easiest way is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your pipeline in a few clicks.
+
+Remember, when working with pipelines, you're orchestrating multiple crews to work together in a sequence or parallel fashion. This allows for more complex workflows and information processing tasks.

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")
@@ -194,19 +182,16 @@ 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:
 
 ```shell
 $ cd my_project
-$ poetry lock
-$ poetry install
+$ crewai install
 ```
 
 This will install the dependencies specified in the `pyproject.toml` file.
@@ -247,12 +232,27 @@ To run your project, use the following command:
 ```shell
 $ crewai run
 ```
-or
+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
-$ poetry run my_project
+$ crewai replay <task_id>
 ```
 
-This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
+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
 

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

@@ -8,13 +8,20 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
     <div style="width:25%">
         <h2>Getting Started</h2>
         <ul>
-            <li><a href='./getting-started/Installing-CrewAI'>
+            <li>
+                <a href='./getting-started/Installing-CrewAI'>
                    Installing CrewAI
-                 </a>
+                </a>
             </li>
-            <li><a href='./getting-started/Start-a-New-CrewAI-Project-Template-Method'>
+            <li>
+                <a href='./getting-started/Start-a-New-CrewAI-Project-Template-Method'>
                    Start a New CrewAI Project: Template Method
-                 </a>
+                </a>
+            </li>
+            <li>
+                <a href='./getting-started/Create-a-New-CrewAI-Pipeline-Template-Method'>
+                    Create a New CrewAI Pipeline: Template Method
+                </a>
             </li>
         </ul>
     </div>
@@ -66,6 +73,11 @@ 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

@@ -129,6 +129,7 @@ nav:
     - Processes: 'core-concepts/Processes.md'
     - Crews: 'core-concepts/Crews.md'
     - Collaboration: 'core-concepts/Collaboration.md'
+    - Pipeline: 'core-concepts/Pipeline.md'
     - Training: 'core-concepts/Training-Crew.md'
     - Memory: 'core-concepts/Memory.md'
     - Planning: 'core-concepts/Planning.md'
@@ -136,9 +137,6 @@ 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 +153,37 @@ 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"
@@ -62,6 +62,9 @@ ignore_missing_imports = true
 disable_error_code = 'import-untyped'
 exclude = ["cli/templates"]
 
+[tool.bandit]
+exclude_dirs = ["src/crewai/cli/templates"]
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

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):
@@ -103,10 +113,11 @@ class Agent(BaseAgent):
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
     )
 
-    def __init__(__pydantic_self__, **data):
-        config = data.pop("config", {})
-        super().__init__(**config, **data)
-        __pydantic_self__.agent_ops_agent_name = __pydantic_self__.role
+    @model_validator(mode="after")
+    def set_agent_ops_agent_name(self) -> "Agent":
+        """Set agent ops agent name."""
+        self.agent_ops_agent_name = self.role
+        return self
 
     @model_validator(mode="after")
     def set_agent_executor(self) -> "Agent":
@@ -203,7 +214,7 @@ class Agent(BaseAgent):
                 raise e
             result = self.execute_task(task, context, tools)
 
-        if self.max_rpm:
+        if self.max_rpm and self._rpm_controller:
             self._rpm_controller.stop_rpm_counter()
 
         # If there was any tool in self.tools_results that had result_as_answer

src/crewai/agents/agent_builder/base_agent.py

@@ -7,7 +7,6 @@ from typing import Any, Dict, List, Optional, TypeVar
 from pydantic import (
     UUID4,
     BaseModel,
-    ConfigDict,
     Field,
     InstanceOf,
     PrivateAttr,
@@ -74,12 +73,17 @@ class BaseAgent(ABC, BaseModel):
     """
 
     __hash__ = object.__hash__  # type: ignore
-    _logger: Logger = PrivateAttr()
-    _rpm_controller: RPMController = PrivateAttr(default=None)
+    _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
+    _rpm_controller: Optional[RPMController] = PrivateAttr(default=None)
     _request_within_rpm_limit: Any = PrivateAttr(default=None)
-    formatting_errors: int = 0
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    _original_role: Optional[str] = PrivateAttr(default=None)
+    _original_goal: Optional[str] = PrivateAttr(default=None)
+    _original_backstory: Optional[str] = PrivateAttr(default=None)
+    _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
+    formatting_errors: int = Field(
+        default=0, description="Number of formatting errors."
+    )
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Objective of the agent")
     backstory: str = Field(description="Backstory of the agent")
@@ -123,15 +127,6 @@ class BaseAgent(ABC, BaseModel):
         default=None, description="Maximum number of tokens for the agent's execution."
     )
 
-    _original_role: str | None = None
-    _original_goal: str | None = None
-    _original_backstory: str | None = None
-    _token_process: TokenProcess = TokenProcess()
-
-    def __init__(__pydantic_self__, **data):
-        config = data.pop("config", {})
-        super().__init__(**config, **data)
-
     @model_validator(mode="after")
     def set_config_attributes(self):
         if self.config:
@@ -170,7 +165,7 @@ class BaseAgent(ABC, BaseModel):
     @property
     def key(self):
         source = [self.role, self.goal, self.backstory]
-        return md5("|".join(source).encode()).hexdigest()
+        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
 
     @abstractmethod
     def execute_task(

src/crewai/agents/cache/cache_handler.py

@@ -1,13 +1,12 @@
-from typing import Optional
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, PrivateAttr
 
 
-class CacheHandler:
+class CacheHandler(BaseModel):
     """Callback handler for tool usage."""
 
-    _cache: dict = {}
-
-    def __init__(self):
-        self._cache = {}
+    _cache: Dict[str, Any] = PrivateAttr(default_factory=dict)
 
     def add(self, tool, input, output):
         self._cache[f"{tool}-{input}"] = output

src/crewai/agents/executor.py

@@ -1,33 +1,29 @@
 import threading
 import time
 from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
+
 import click
-
-
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
 from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.chains.summarize import load_summarize_chain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_core.agents import AgentAction, AgentFinish, AgentStep
 from langchain_core.exceptions import OutputParserException
 from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
 from pydantic import InstanceOf
 
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.summarize import load_summarize_chain
-
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.tools_handler import ToolsHandler
-
-
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N
 from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
-from crewai.utilities.training_handler import CrewTrainingHandler
 from crewai.utilities.logger import Logger
+from crewai.utilities.training_handler import CrewTrainingHandler
 
 
 class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
@@ -213,11 +209,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                         yield step
                 return
 
-            yield AgentStep(
-                action=AgentAction("_Exception", str(e), str(e)),
-                observation=str(e),
-            )
-            return
+            raise e
 
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):

src/crewai/cli/cli.py

@@ -8,6 +8,7 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (
 )
 
 from .evaluate_crew import evaluate_crew
+from .install_crew import install_crew
 from .replay_from_task import replay_task_command
 from .reset_memories_command import reset_memories_command
 from .run_crew import run_crew
@@ -165,10 +166,16 @@ def test(n_iterations: int, model: str):
     evaluate_crew(n_iterations, model)
 
 
+@crewai.command()
+def install():
+    """Install the Crew."""
+    install_crew()
+
+
 @crewai.command()
 def run():
-    """Run the crew."""
-    click.echo("Running the crew")
+    """Run the Crew."""
+    click.echo("Running the Crew")
     run_crew()
 
 

src/crewai/cli/install_crew.py

@@ -0,0 +1,21 @@
+import subprocess
+
+import click
+
+
+def install_crew() -> None:
+    """
+    Install the crew by running the Poetry command to lock and install.
+    """
+    try:
+        subprocess.run(["poetry", "lock"], check=True, capture_output=False, text=True)
+        subprocess.run(
+            ["poetry", "install"], check=True, capture_output=False, text=True
+        )
+
+    except subprocess.CalledProcessError as e:
+        click.echo(f"An error occurred while running the crew: {e}", err=True)
+        click.echo(e.output, err=True)
+
+    except Exception as e:
+        click.echo(f"An unexpected error occurred: {e}", err=True)

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

@@ -14,12 +14,9 @@ pip install poetry
 
 Next, navigate to your project directory and install the dependencies:
 
-1. First lock the dependencies and then install them:
+1. First lock the dependencies and install them by using the CLI command:
 ```bash
-poetry lock
-```
-```bash
-poetry install
+crewai install
 ```
 ### Customizing
 
@@ -37,10 +34,6 @@ To kickstart your crew of AI agents and begin task execution, run this from the
 ```bash
 $ crewai run
 ```
-or
-```bash
-poetry run {{folder_name}}
-```
 
 This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
 

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

@@ -6,7 +6,8 @@ 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,<1.0.0" }
+
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"

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

@@ -15,12 +15,11 @@ pip install poetry
 Next, navigate to your project directory and install the dependencies:
 
 1. First lock the dependencies and then install them:
+
 ```bash
-poetry lock
-```
-```bash
-poetry install
+crewai install
 ```
+
 ### Customizing
 
 **Add your `OPENAI_API_KEY` into the `.env` file**
@@ -35,7 +34,7 @@ poetry install
 To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
 
 ```bash
-poetry run {{folder_name}}
+crewai run
 ```
 
 This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
@@ -49,6 +48,7 @@ The {{name}} Crew is composed of multiple AI agents, each with unique roles, goa
 ## Support
 
 For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
+
 - Visit our [documentation](https://docs.crewai.com)
 - Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
 - [Join our Discord](https://discord.com/invite/X4JWnZnxPb)

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,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -16,10 +16,7 @@ Next, navigate to your project directory and install the dependencies:
 
 1. First lock the dependencies and then install them:
 ```bash
-poetry lock
-```
-```bash
-poetry install
+crewai install
 ```
 ### Customizing
 
@@ -35,7 +32,7 @@ poetry install
 To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
 
 ```bash
-poetry run {{folder_name}}
+crewai run
 ```
 
 This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.

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

@@ -6,7 +6,8 @@ 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,<1.0.0" }
+
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:main"

src/crewai/crew.py

@@ -1,5 +1,6 @@
 import asyncio
 import json
+import os
 import uuid
 from concurrent.futures import Future
 from hashlib import md5
@@ -9,7 +10,6 @@ from langchain_core.callbacks import BaseCallbackHandler
 from pydantic import (
     UUID4,
     BaseModel,
-    ConfigDict,
     Field,
     InstanceOf,
     Json,
@@ -47,10 +47,12 @@ 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  # type: ignore
+    except ImportError:
+        pass
 
 if TYPE_CHECKING:
     from crewai.pipeline.pipeline import Pipeline
@@ -102,7 +104,6 @@ class Crew(BaseModel):
 
     name: Optional[str] = Field(default=None)
     cache: bool = Field(default=True)
-    model_config = ConfigDict(arbitrary_types_allowed=True)
     tasks: List[Task] = Field(default_factory=list)
     agents: List[BaseAgent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
@@ -360,7 +361,7 @@ class Crew(BaseModel):
         source = [agent.key for agent in self.agents] + [
             task.key for task in self.tasks
         ]
-        return md5("|".join(source).encode()).hexdigest()
+        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
 
     def _setup_from_config(self):
         assert self.config is not None, "Config should not be None."
@@ -537,7 +538,7 @@ class Crew(BaseModel):
         )._handle_crew_planning()
 
         for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
-            task.description += step_plan
+            task.description += step_plan.plan
 
     def _store_execution_log(
         self,
@@ -936,6 +937,9 @@ 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/project/annotations.py

@@ -6,12 +6,20 @@ def task(func):
         task.registration_order = []
 
     func.is_task = True
-    wrapped_func = memoize(func)
+    memoized_func = memoize(func)
 
     # Append the function name to the registration order list
     task.registration_order.append(func.__name__)
 
-    return wrapped_func
+    def wrapper(*args, **kwargs):
+        result = memoized_func(*args, **kwargs)
+
+        if not result.name:
+            result.name = func.__name__
+
+        return result
+
+    return wrapper
 
 
 def agent(func):

src/crewai/project/crew_base.py

@@ -1,56 +1,45 @@
 import inspect
-import os
 from pathlib import Path
 from typing import Any, Callable, Dict
 
 import yaml
 from dotenv import load_dotenv
-from pydantic import ConfigDict
 
 load_dotenv()
 
 
 def CrewBase(cls):
     class WrappedClass(cls):
-        model_config = ConfigDict(arbitrary_types_allowed=True)
         is_crew_class: bool = True  # type: ignore
 
-        base_directory = None
-        for frame_info in inspect.stack():
-            if "site-packages" not in frame_info.filename:
-                base_directory = Path(frame_info.filename).parent.resolve()
-                break
+        # Get the directory of the class being decorated
+        base_directory = Path(inspect.getfile(cls)).parent
 
         original_agents_config_path = getattr(
             cls, "agents_config", "config/agents.yaml"
         )
-
         original_tasks_config_path = getattr(cls, "tasks_config", "config/tasks.yaml")
 
         def __init__(self, *args, **kwargs):
             super().__init__(*args, **kwargs)
 
-            if self.base_directory is None:
-                raise Exception(
-                    "Unable to dynamically determine the project's base directory, you must run it from the project's root directory."
-                )
+            agents_config_path = self.base_directory / self.original_agents_config_path
+            tasks_config_path = self.base_directory / self.original_tasks_config_path
 
-            self.agents_config = self.load_yaml(
-                os.path.join(self.base_directory, self.original_agents_config_path)
-            )
-
-            self.tasks_config = self.load_yaml(
-                os.path.join(self.base_directory, self.original_tasks_config_path)
-            )
+            self.agents_config = self.load_yaml(agents_config_path)
+            self.tasks_config = self.load_yaml(tasks_config_path)
 
             self.map_all_agent_variables()
             self.map_all_task_variables()
 
         @staticmethod
-        def load_yaml(config_path: str):
-            with open(config_path, "r") as file:
-                # parsedContent = YamlParser.parse(file)  # type: ignore # Argument 1 to "parse" has incompatible type "TextIOWrapper"; expected "YamlParser"
-                return yaml.safe_load(file)
+        def load_yaml(config_path: Path):
+            try:
+                with open(config_path, "r") as file:
+                    return yaml.safe_load(file)
+            except FileNotFoundError:
+                print(f"File not found: {config_path}")
+                raise
 
         def _get_all_functions(self):
             return {

src/crewai/project/pipeline_base.py

@@ -1,24 +1,24 @@
-from typing import Callable, Dict
-
-from pydantic import ConfigDict
+from typing import Any, Callable, Dict, List, Type, Union
 
 from crewai.crew import Crew
 from crewai.pipeline.pipeline import Pipeline
 from crewai.routers.router import Router
 
+PipelineStage = Union[Crew, List[Crew], Router]
+
 
 # TODO: Could potentially remove. Need to check with @joao and @gui if this is needed for CrewAI+
-def PipelineBase(cls):
+def PipelineBase(cls: Type[Any]) -> Type[Any]:
     class WrappedClass(cls):
-        model_config = ConfigDict(arbitrary_types_allowed=True)
-        is_pipeline_class: bool = True
+        is_pipeline_class: bool = True  # type: ignore
+        stages: List[PipelineStage]
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any) -> None:
             super().__init__(*args, **kwargs)
             self.stages = []
             self._map_pipeline_components()
 
-        def _get_all_functions(self):
+        def _get_all_functions(self) -> Dict[str, Callable[..., Any]]:
             return {
                 name: getattr(self, name)
                 for name in dir(self)
@@ -26,15 +26,15 @@ def PipelineBase(cls):
             }
 
         def _filter_functions(
-            self, functions: Dict[str, Callable], attribute: str
-        ) -> Dict[str, Callable]:
+            self, functions: Dict[str, Callable[..., Any]], attribute: str
+        ) -> Dict[str, Callable[..., Any]]:
             return {
                 name: func
                 for name, func in functions.items()
                 if hasattr(func, attribute)
             }
 
-        def _map_pipeline_components(self):
+        def _map_pipeline_components(self) -> None:
             all_functions = self._get_all_functions()
             crew_functions = self._filter_functions(all_functions, "is_crew")
             router_functions = self._filter_functions(all_functions, "is_router")

src/crewai/routers/router.py

@@ -1,32 +1,26 @@
 from copy import deepcopy
-from typing import Any, Callable, Dict, Generic, Tuple, TypeVar
+from typing import Any, Callable, Dict, Tuple
 
 from pydantic import BaseModel, Field, PrivateAttr
 
-T = TypeVar("T", bound=Dict[str, Any])
-U = TypeVar("U")
+
+class Route(BaseModel):
+    condition: Callable[[Dict[str, Any]], bool]
+    pipeline: Any
 
 
-class Route(Generic[T, U]):
-    condition: Callable[[T], bool]
-    pipeline: U
-
-    def __init__(self, condition: Callable[[T], bool], pipeline: U):
-        self.condition = condition
-        self.pipeline = pipeline
-
-
-class Router(BaseModel, Generic[T, U]):
-    routes: Dict[str, Route[T, U]] = Field(
+class Router(BaseModel):
+    routes: Dict[str, Route] = Field(
         default_factory=dict,
         description="Dictionary of route names to (condition, pipeline) tuples",
     )
-    default: U = Field(..., description="Default pipeline if no conditions are met")
+    default: Any = Field(..., description="Default pipeline if no conditions are met")
     _route_types: Dict[str, type] = PrivateAttr(default_factory=dict)
 
-    model_config = {"arbitrary_types_allowed": True}
+    class Config:
+        arbitrary_types_allowed = True
 
-    def __init__(self, routes: Dict[str, Route[T, U]], default: U, **data):
+    def __init__(self, routes: Dict[str, Route], default: Any, **data):
         super().__init__(routes=routes, default=default, **data)
         self._check_copyable(default)
         for name, route in routes.items():
@@ -34,16 +28,16 @@ class Router(BaseModel, Generic[T, U]):
             self._route_types[name] = type(route.pipeline)
 
     @staticmethod
-    def _check_copyable(obj):
+    def _check_copyable(obj: Any) -> None:
         if not hasattr(obj, "copy") or not callable(getattr(obj, "copy")):
             raise ValueError(f"Object of type {type(obj)} must have a 'copy' method")
 
     def add_route(
         self,
         name: str,
-        condition: Callable[[T], bool],
-        pipeline: U,
-    ) -> "Router[T, U]":
+        condition: Callable[[Dict[str, Any]], bool],
+        pipeline: Any,
+    ) -> "Router":
         """
         Add a named route with its condition and corresponding pipeline to the router.
 
@@ -60,7 +54,7 @@ class Router(BaseModel, Generic[T, U]):
         self._route_types[name] = type(pipeline)
         return self
 
-    def route(self, input_data: T) -> Tuple[U, str]:
+    def route(self, input_data: Dict[str, Any]) -> Tuple[Any, str]:
         """
         Evaluate the input against the conditions and return the appropriate pipeline.
 
@@ -76,15 +70,15 @@ class Router(BaseModel, Generic[T, U]):
 
         return self.default, "default"
 
-    def copy(self) -> "Router[T, U]":
+    def copy(self) -> "Router":
         """Create a deep copy of the Router."""
         new_routes = {
             name: Route(
                 condition=deepcopy(route.condition),
-                pipeline=route.pipeline.copy(),  # type: ignore
+                pipeline=route.pipeline.copy(),
             )
             for name, route in self.routes.items()
         }
-        new_default = self.default.copy()  # type: ignore
+        new_default = self.default.copy()
 
         return Router(routes=new_routes, default=new_default)

src/crewai/task.py

@@ -9,7 +9,14 @@ from hashlib import md5
 from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 from opentelemetry.trace import Span
-from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
+from pydantic import (
+    UUID4,
+    BaseModel,
+    Field,
+    PrivateAttr,
+    field_validator,
+    model_validator,
+)
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -39,9 +46,6 @@ class Task(BaseModel):
         tools: List of tools/resources limited for task execution.
     """
 
-    class Config:
-        arbitrary_types_allowed = True
-
     __hash__ = object.__hash__  # type: ignore
     used_tools: int = 0
     tools_errors: int = 0
@@ -104,16 +108,12 @@ class Task(BaseModel):
         default=None,
     )
 
-    _telemetry: Telemetry
-    _execution_span: Span | None = None
-    _original_description: str | None = None
-    _original_expected_output: str | None = None
-    _thread: threading.Thread | None = None
-    _execution_time: float | None = None
-
-    def __init__(__pydantic_self__, **data):
-        config = data.pop("config", {})
-        super().__init__(**config, **data)
+    _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
+    _execution_span: Optional[Span] = PrivateAttr(default=None)
+    _original_description: Optional[str] = PrivateAttr(default=None)
+    _original_expected_output: Optional[str] = PrivateAttr(default=None)
+    _thread: Optional[threading.Thread] = PrivateAttr(default=None)
+    _execution_time: Optional[float] = PrivateAttr(default=None)
 
     @field_validator("id", mode="before")
     @classmethod
@@ -137,12 +137,6 @@ class Task(BaseModel):
             return value[1:]
         return value
 
-    @model_validator(mode="after")
-    def set_private_attrs(self) -> "Task":
-        """Set private attributes."""
-        self._telemetry = Telemetry()
-        return self
-
     @model_validator(mode="after")
     def set_attributes_based_on_config(self) -> "Task":
         """Set attributes based on the agent configuration."""
@@ -185,7 +179,7 @@ class Task(BaseModel):
         expected_output = self._original_expected_output or self.expected_output
         source = [description, expected_output]
 
-        return md5("|".join(source).encode()).hexdigest()
+        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
 
     def execute_async(
         self,
@@ -240,7 +234,9 @@ class Task(BaseModel):
         pydantic_output, json_output = self._export_output(result)
 
         task_output = TaskOutput(
+            name=self.name,
             description=self.description,
+            expected_output=self.expected_output,
             raw=result,
             pydantic=pydantic_output,
             json_dict=json_output,
@@ -261,9 +257,7 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
+                else pydantic_output.model_dump_json() if pydantic_output else result
             )
             self._save_file(content)
 

src/crewai/tasks/task_output.py

@@ -10,6 +10,10 @@ class TaskOutput(BaseModel):
     """Class that represents the result of a task."""
 
     description: str = Field(description="Description of the task")
+    name: Optional[str] = Field(description="Name of the task", default=None)
+    expected_output: Optional[str] = Field(
+        description="Expected output of the task", default=None
+    )
     summary: Optional[str] = Field(description="Summary of the task", default=None)
     raw: str = Field(description="Raw output of the task", default="")
     pydantic: Optional[BaseModel] = Field(

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: float, 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/cache_tools.py

@@ -1,5 +1,5 @@
 from langchain.tools import StructuredTool
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field
 
 from crewai.agents.cache import CacheHandler
 
@@ -7,11 +7,10 @@ from crewai.agents.cache import CacheHandler
 class CacheTools(BaseModel):
     """Default tools to hit the cache."""
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
     name: str = "Hit Cache"
     cache_handler: CacheHandler = Field(
         description="Cache Handler for the crew",
-        default=CacheHandler(),
+        default_factory=CacheHandler,
     )
 
     def tool(self):

src/crewai/tools/tool_usage.py

@@ -1,4 +1,5 @@
 import ast
+import os
 from difflib import SequenceMatcher
 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  # type: ignore
+    except ImportError:
+        pass
 
 OPENAI_BIGGER_MODELS = ["gpt-4o"]
 
@@ -69,6 +72,13 @@ class ToolUsage:
         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
@@ -108,7 +118,7 @@ class ToolUsage:
         tool: BaseTool,
         calling: Union[ToolCalling, InstructorToolCalling],
     ) -> str:  # TODO: Fix this return type
-        tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None
+        tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None  # type: ignore
         if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
             try:
                 result = self._i18n.errors("task_repeated_usage").format(

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/logger.py

@@ -1,13 +1,13 @@
 from datetime import datetime
 
+from pydantic import BaseModel, Field, PrivateAttr
+
 from crewai.utilities.printer import Printer
 
 
-class Logger:
-    _printer = Printer()
-
-    def __init__(self, verbose=False):
-        self.verbose = verbose
+class Logger(BaseModel):
+    verbose: bool = Field(default=False)
+    _printer: Printer = PrivateAttr(default_factory=Printer)
 
     def log(self, level, message, color="bold_green"):
         if self.verbose:

src/crewai/utilities/planning_handler.py

@@ -1,14 +1,25 @@
 from typing import Any, List, Optional
 
 from langchain_openai import ChatOpenAI
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from crewai.agent import Agent
 from crewai.task import Task
 
 
+class PlanPerTask(BaseModel):
+    task: str = Field(..., description="The task for which the plan is created")
+    plan: str = Field(
+        ...,
+        description="The step by step plan on how the agents can execute their tasks using the available tools with mastery",
+    )
+
+
 class PlannerTaskPydanticOutput(BaseModel):
-    list_of_plans_per_task: List[str]
+    list_of_plans_per_task: List[PlanPerTask] = Field(
+        ...,
+        description="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
+    )
 
 
 class CrewPlanner:

src/crewai/utilities/rpm_controller.py

@@ -1,44 +1,50 @@
 import threading
 import time
-from typing import Union
+from typing import Optional
 
-from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, model_validator
+from pydantic import BaseModel, Field, PrivateAttr, model_validator
 
 from crewai.utilities.logger import Logger
 
 
 class RPMController(BaseModel):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-    max_rpm: Union[int, None] = Field(default=None)
-    logger: Logger = Field(default=None)
+    max_rpm: Optional[int] = Field(default=None)
+    logger: Logger = Field(default_factory=lambda: Logger(verbose=False))
     _current_rpm: int = PrivateAttr(default=0)
-    _timer: threading.Timer | None = PrivateAttr(default=None)
-    _lock: threading.Lock = PrivateAttr(default=None)
-    _shutdown_flag = False
+    _timer: Optional[threading.Timer] = PrivateAttr(default=None)
+    _lock: Optional[threading.Lock] = PrivateAttr(default=None)
+    _shutdown_flag: bool = PrivateAttr(default=False)
 
     @model_validator(mode="after")
     def reset_counter(self):
-        if self.max_rpm:
+        if self.max_rpm is not None:
             if not self._shutdown_flag:
                 self._lock = threading.Lock()
                 self._reset_request_count()
         return self
 
     def check_or_wait(self):
-        if not self.max_rpm:
+        if self.max_rpm is None:
             return True
 
-        with self._lock:
-            if self._current_rpm < self.max_rpm:
+        def _check_and_increment():
+            if self.max_rpm is not None and self._current_rpm < self.max_rpm:
                 self._current_rpm += 1
                 return True
-            else:
+            elif self.max_rpm is not None:
                 self.logger.log(
                     "info", "Max RPM reached, waiting for next minute to start."
                 )
                 self._wait_for_next_minute()
                 self._current_rpm = 1
                 return True
+            return True
+
+        if self._lock:
+            with self._lock:
+                return _check_and_increment()
+        else:
+            return _check_and_increment()
 
     def stop_rpm_counter(self):
         if self._timer:
@@ -50,10 +56,18 @@ class RPMController(BaseModel):
         self._current_rpm = 0
 
     def _reset_request_count(self):
-        with self._lock:
+        def _reset():
             self._current_rpm = 0
+            if not self._shutdown_flag:
+                self._timer = threading.Timer(60.0, self._reset_request_count)
+                self._timer.start()
+
+        if self._lock:
+            with self._lock:
+                _reset()
+        else:
+            _reset()
+
         if self._timer:
             self._shutdown_flag = True
             self._timer.cancel()
-        self._timer = threading.Timer(60.0, self._reset_request_count)
-        self._timer.start()

tests/agent_test.py

@@ -4,11 +4,6 @@ from unittest import mock
 from unittest.mock import patch
 
 import pytest
-from langchain.tools import tool
-from langchain_core.exceptions import OutputParserException
-from langchain_openai import ChatOpenAI
-from langchain.schema import AgentAction
-
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.executor import CrewAgentExecutor
@@ -16,6 +11,10 @@ from crewai.agents.parser import CrewAgentParser
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities import RPMController
+from langchain.schema import AgentAction
+from langchain.tools import tool
+from langchain_core.exceptions import OutputParserException
+from langchain_openai import ChatOpenAI
 
 
 def test_agent_creation():
@@ -817,7 +816,7 @@ def test_agent_definition_based_on_dict():
         "verbose": True,
     }
 
-    agent = Agent(config=config)
+    agent = Agent(**config)
 
     assert agent.role == "test role"
     assert agent.goal == "test goal"
@@ -837,7 +836,7 @@ def test_agent_human_input():
         "backstory": "test backstory",
     }
 
-    agent = Agent(config=config)
+    agent = Agent(**config)
 
     task = Task(
         agent=agent,

tests/cassettes/test_json_property_without_output_json.yaml

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tests/crew_test.py

@@ -8,7 +8,6 @@ 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
@@ -622,7 +621,7 @@ def test_sequential_async_task_execution_completion():
 
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     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**"
     )
 
 

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tests/pipeline/test_pipeline.py

@@ -25,14 +25,20 @@ def mock_crew_factory():
         MockCrewClass = type("MockCrew", (MagicMock, Crew), {})
 
         class MockCrew(MockCrewClass):
-            def __deepcopy__(self, memo):
+            def __deepcopy__(self):
                 result = MockCrewClass()
                 result.kickoff_async = self.kickoff_async
                 result.name = self.name
                 return result
 
+            def copy(
+                self,
+            ):
+                return self
+
         crew = MockCrew()
         crew.name = name
+
         task_output = TaskOutput(
             description="Test task", raw="Task output", agent="Test Agent"
         )
@@ -44,9 +50,15 @@ def mock_crew_factory():
             pydantic=pydantic_output,
         )
 
-        async def async_kickoff(inputs=None):
+        async def kickoff_async(inputs=None):
             return crew_output
 
+        # Create an AsyncMock for kickoff_async
+        crew.kickoff_async = AsyncMock(side_effect=kickoff_async)
+
+        # Mock the synchronous kickoff method
+        crew.kickoff = MagicMock(return_value=crew_output)
+
         # Add more attributes that Procedure might be expecting
         crew.verbose = False
         crew.output_log_file = None
@@ -56,30 +68,16 @@ def mock_crew_factory():
         crew.config = None
         crew.cache = True
 
-        # # Create a valid Agent instance
-        mock_agent = Agent(
-            name="Mock Agent",
-            role="Mock Role",
-            goal="Mock Goal",
-            backstory="Mock Backstory",
-            allow_delegation=False,
-            verbose=False,
-        )
-
-        # Create a valid Task instance
-        mock_task = Task(
-            description="Return: Test output",
-            expected_output="Test output",
-            agent=mock_agent,
-            async_execution=False,
-            context=None,
-        )
+        # Add non-empty agents and tasks
+        mock_agent = MagicMock(spec=Agent)
+        mock_task = MagicMock(spec=Task)
+        mock_task.agent = mock_agent
+        mock_task.async_execution = False
+        mock_task.context = None
 
         crew.agents = [mock_agent]
         crew.tasks = [mock_task]
 
-        crew.kickoff_async = AsyncMock(side_effect=async_kickoff)
-
         return crew
 
     return _create_mock_crew
@@ -115,9 +113,7 @@ def mock_router_factory(mock_crew_factory):
                 (
                     "route1"
                     if x.get("score", 0) > 80
-                    else "route2"
-                    if x.get("score", 0) > 50
-                    else "default"
+                    else "route2" if x.get("score", 0) > 50 else "default"
                 ),
             )
         )
@@ -477,31 +473,17 @@ async def test_pipeline_with_parallel_stages_end_in_single_stage(mock_crew_facto
     """
     Test that Pipeline correctly handles parallel stages.
     """
-    crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent])
-    crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent])
-    crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent])
-    crew4 = Crew(name="Crew 4", tasks=[task], agents=[agent])
+    crew1 = mock_crew_factory(name="Crew 1")
+    crew2 = mock_crew_factory(name="Crew 2")
+    crew3 = mock_crew_factory(name="Crew 3")
+    crew4 = mock_crew_factory(name="Crew 4")
 
     pipeline = Pipeline(stages=[crew1, [crew2, crew3], crew4])
     input_data = [{"initial": "data"}]
 
     pipeline_result = await pipeline.kickoff(input_data)
 
-    with patch.object(Crew, "kickoff_async") as mock_kickoff:
-        mock_kickoff.return_value = CrewOutput(
-            raw="Test output",
-            tasks_output=[
-                TaskOutput(
-                    description="Test task", raw="Task output", agent="Test Agent"
-                )
-            ],
-            token_usage=DEFAULT_TOKEN_USAGE,
-            json_dict=None,
-            pydantic=None,
-        )
-        pipeline_result = await pipeline.kickoff(input_data)
-
-        mock_kickoff.assert_called_with(inputs={"initial": "data"})
+    crew1.kickoff_async.assert_called_once_with(inputs={"initial": "data"})
 
     assert len(pipeline_result) == 1
     pipeline_result_1 = pipeline_result[0]
@@ -649,33 +631,21 @@ Options:
 
 
 @pytest.mark.asyncio
-async def test_pipeline_data_accumulation():
-    crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent])
-    crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent])
+async def test_pipeline_data_accumulation(mock_crew_factory):
+    crew1 = mock_crew_factory(name="Crew 1", output_json_dict={"key1": "value1"})
+    crew2 = mock_crew_factory(name="Crew 2", output_json_dict={"key2": "value2"})
 
     pipeline = Pipeline(stages=[crew1, crew2])
     input_data = [{"initial": "data"}]
     results = await pipeline.kickoff(input_data)
 
-    with patch.object(Crew, "kickoff_async") as mock_kickoff:
-        mock_kickoff.side_effect = [
-            CrewOutput(
-                raw="Test output from Crew 1",
-                tasks_output=[],
-                token_usage=DEFAULT_TOKEN_USAGE,
-                json_dict={"key1": "value1"},
-                pydantic=None,
-            ),
-            CrewOutput(
-                raw="Test output from Crew 2",
-                tasks_output=[],
-                token_usage=DEFAULT_TOKEN_USAGE,
-                json_dict={"key2": "value2"},
-                pydantic=None,
-            ),
-        ]
+    # Check that crew1 was called with only the initial input
+    crew1.kickoff_async.assert_called_once_with(inputs={"initial": "data"})
 
-        results = await pipeline.kickoff(input_data)
+    # Check that crew2 was called with the combined input from the initial data and crew1's output
+    crew2.kickoff_async.assert_called_once_with(
+        inputs={"initial": "data", "key1": "value1"}
+    )
 
     # Check the final output
     assert len(results) == 1

tests/project_test.py

@@ -14,6 +14,14 @@ class SimpleCrew:
     def simple_task(self):
         return Task(description="Simple Description", expected_output="Simple Output")
 
+    @task
+    def custom_named_task(self):
+        return Task(
+            description="Simple Description",
+            expected_output="Simple Output",
+            name="Custom",
+        )
+
 
 def test_agent_memoization():
     crew = SimpleCrew()
@@ -33,3 +41,15 @@ def test_task_memoization():
     assert (
         first_call_result is second_call_result
     ), "Task memoization is not working as expected"
+
+
+def test_task_name():
+    simple_task = SimpleCrew().simple_task()
+    assert (
+        simple_task.name == "simple_task"
+    ), "Task name is not inferred from function name as expected"
+
+    custom_named_task = SimpleCrew().custom_named_task()
+    assert (
+        custom_named_task.name == "Custom"
+    ), "Custom task name is not being set as expected"

tests/task_test.py

@@ -2,6 +2,7 @@
 
 import hashlib
 import json
+import os
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -97,6 +98,7 @@ def test_task_callback():
     task_completed = MagicMock(return_value="done")
 
     task = Task(
+        name="Brainstorm",
         description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
         expected_output="Bullet point list of 5 interesting ideas.",
         agent=researcher,
@@ -108,6 +110,10 @@ def test_task_callback():
         task.execute_sync(agent=researcher)
         task_completed.assert_called_once_with(task.output)
 
+        assert task.output.description == task.description
+        assert task.output.expected_output == task.expected_output
+        assert task.output.name == task.name
+
 
 def test_task_callback_returns_task_output():
     from crewai.tasks.output_format import OutputFormat
@@ -148,6 +154,8 @@ def test_task_callback_returns_task_output():
             "json_dict": None,
             "agent": researcher.role,
             "summary": "Give me a list of 5 interesting ideas to explore...",
+            "name": None,
+            "expected_output": "Bullet point list of 5 interesting ideas.",
             "output_format": OutputFormat.RAW,
         }
         assert output_dict == expected_output
@@ -258,7 +266,7 @@ 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 +285,7 @@ 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 +323,8 @@ 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 +408,8 @@ 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 +480,7 @@ 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 +528,13 @@ 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 +558,13 @@ 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"])
@@ -690,7 +703,7 @@ def test_task_definition_based_on_dict():
         "expected_output": "The score of the title.",
     }
 
-    task = Task(config=config)
+    task = Task(**config)
 
     assert task.description == config["description"]
     assert task.expected_output == config["expected_output"]
@@ -703,7 +716,7 @@ def test_conditional_task_definition_based_on_dict():
         "expected_output": "The score of the title.",
     }
 
-    task = ConditionalTask(config=config, condition=lambda x: True)
+    task = ConditionalTask(**config, condition=lambda x: True)
 
     assert task.description == config["description"]
     assert task.expected_output == config["expected_output"]

tests/utilities/test_planning_handler.py

@@ -6,7 +6,11 @@ from langchain_openai import ChatOpenAI
 from crewai.agent import Agent
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
-from crewai.utilities.planning_handler import CrewPlanner, PlannerTaskPydanticOutput
+from crewai.utilities.planning_handler import (
+    CrewPlanner,
+    PlannerTaskPydanticOutput,
+    PlanPerTask,
+)
 
 
 class TestCrewPlanner:
@@ -44,12 +48,17 @@ class TestCrewPlanner:
         return CrewPlanner(tasks, planning_agent_llm)
 
     def test_handle_crew_planning(self, crew_planner):
+        list_of_plans_per_task = [
+            PlanPerTask(task="Task1", plan="Plan 1"),
+            PlanPerTask(task="Task2", plan="Plan 2"),
+            PlanPerTask(task="Task3", plan="Plan 3"),
+        ]
         with patch.object(Task, "execute_sync") as execute:
             execute.return_value = TaskOutput(
                 description="Description",
                 agent="agent",
                 pydantic=PlannerTaskPydanticOutput(
-                    list_of_plans_per_task=["Plan 1", "Plan 2", "Plan 3"]
+                    list_of_plans_per_task=list_of_plans_per_task
                 ),
             )
             result = crew_planner._handle_crew_planning()
@@ -91,7 +100,9 @@ class TestCrewPlanner:
             execute.return_value = TaskOutput(
                 description="Description",
                 agent="agent",
-                pydantic=PlannerTaskPydanticOutput(list_of_plans_per_task=["Plan 1"]),
+                pydantic=PlannerTaskPydanticOutput(
+                    list_of_plans_per_task=[PlanPerTask(task="Task1", plan="Plan 1")]
+                ),
             )
             result = crew_planner_different_llm._handle_crew_planning()