Create codeql.yml

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,116 +0,0 @@
-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/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.md

@@ -0,0 +1,21 @@
+---
+name: Feature request
+about: Suggest a Feature to improve CrewAI
+title: "[FEAT]"
+labels: feature-request, improvement
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+If possible attach the Issue related to it
+
+**Describe the solution you'd like / Use-case**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,65 +0,0 @@
-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/codeql.yml

@@ -0,0 +1,93 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL"
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+  schedule:
+    - cron: '28 20 * * 1'
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    # Runner size impacts CodeQL analysis time. To learn more, please see:
+    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
+    #   - https://gh.io/supported-runners-and-hardware-resources
+    #   - https://gh.io/using-larger-runners (GitHub.com only)
+    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }}
+    permissions:
+      # required for all workflows
+      security-events: write
+
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+      # only required for workflows in private repositories
+      actions: read
+      contents: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        - language: python
+          build-mode: none
+        # CodeQL supports the following values keywords for 'language': 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift'
+        # Use `c-cpp` to analyze code written in C, C++ or both
+        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
+        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
+        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
+        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
+        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
+        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"

.github/workflows/stale.yml

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

README.md

@@ -126,7 +126,7 @@ task2 = Task(
 crew = Crew(
   agents=[researcher, writer],
   tasks=[task1, task2],
-  verbose=True,
+  verbose=2, # You can set it to 1 or 2 to different logging levels
   process = Process.sequential
 )
 

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`.                                                               |
@@ -34,8 +34,6 @@ 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
 
@@ -74,8 +72,7 @@ agent = Agent(
   tools_handler=my_tools_handler,  # Optional
   cache_handler=my_cache_handler,  # Optional
   callbacks=[callback1, callback2],  # Optional
-  allow_code_execution=True,  # Optiona
-  max_retry_limit=2,  # Optional
+  agent_executor=my_agent_executor  # Optional
 )
 ```
 
@@ -147,5 +144,6 @@ my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
 crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
 ```
 
+
 ## Conclusion
-Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.
+Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

docs/core-concepts/Collaboration.md

@@ -28,8 +28,6 @@ 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.
@@ -134,7 +134,7 @@ Once a crew has been executed, its output can be accessed through the `output` a
 crew = Crew(
     agents=[research_agent, writer_agent],
     tasks=[research_task, write_article_task],
-    verbose=True
+    verbose=2
 )
 
 crew_output = crew.kickoff()
@@ -183,14 +183,14 @@ result = my_crew.kickoff()
 print(result)
 ```
 
-### Different Ways to Kick Off a Crew
+### Different ways to Kicking Off a Crew
 
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 
-- `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
@@ -215,34 +215,33 @@ 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
 
-You can now replay from a specific task using our CLI command `replay`.
+### Replaying from specific task:
+You can now replay from a specific task using our cli command replay.
 
 The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
 
 Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
 
-### Replaying from 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 the latest kickoff task IDs, use:
+To view 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": "your_embedding_model_deployment_name"
+				"deployment_name": "you_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
 			}
 		}
 )
@@ -197,10 +197,12 @@ 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

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

docs/core-concepts/Planning.md

@@ -41,11 +41,13 @@ 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**
 
@@ -131,4 +133,6 @@ 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

@@ -55,5 +55,10 @@ 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

@@ -17,17 +17,16 @@ 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. 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.    |
+| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task.                                             |
+| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion.                       |
 | **Context** _(optional)_         | `context`         | Specifies tasks whose outputs are used as context for this task.                                                     |
-| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
+| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization.                   |
 | **Output JSON** _(optional)_     | `output_json`     | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
 | **Output Pydantic** _(optional)_ | `output_pydantic` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
 | **Output File** _(optional)_     | `output_file`     | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Output** _(optional)_          | `output`          | 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.                                                |
+| **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.                  |
 
 ## Creating a Task
 
@@ -57,7 +56,7 @@ 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 first 10 words of the description.            |
+| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the description.                                  |
 | **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
 | **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
 | **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
@@ -91,7 +90,7 @@ task = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task],
-    verbose=True
+    verbose=2
 )
 
 result = crew.kickoff()
@@ -143,7 +142,7 @@ task = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task],
-    verbose=True
+    verbose=2
 )
 
 result = crew.kickoff()
@@ -265,7 +264,7 @@ task1 = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task1, task2, task3],
-    verbose=True
+    verbose=2
 )
 
 result = crew.kickoff()
@@ -312,4 +311,4 @@ 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

@@ -5,11 +5,12 @@ 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. 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. And with crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
 
 ### Using the Testing Feature
 
-We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
+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
@@ -21,10 +22,9 @@ If you want to run more iterations or use a different model, you can specify the
 crewai test --n_iterations 5 --model gpt-4o
 ```
 
-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.
+What happens when you run the `crewai test` command is that the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
 
 A table of scores at the end will show the performance of the crew in terms of the following metrics:
-
 ```
                 Task Scores
           (1-10 Higher is better)
@@ -38,3 +38,4 @@ 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

@@ -80,12 +80,11 @@ write = Task(
     output_file='blog-posts/new_post.md'  # The final blog post will be saved here
 )
 
-# Assemble a crew with planning enabled
+# Assemble a crew
 crew = Crew(
     agents=[researcher, writer],
     tasks=[research, write],
-    verbose=True,
-    planning=True,  # Enable planning feature
+    verbose=2
 )
 
 # Execute tasks
@@ -106,7 +105,6 @@ 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.                |
@@ -123,7 +121,6 @@ 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.                 |
@@ -198,5 +195,6 @@ 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

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

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

@@ -7,10 +7,10 @@ description: A comprehensive guide to starting a new CrewAI project, including t
 
 Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
 
-Before we start, there are a couple of things to note:
+Beforre we start there are a couple of things to note:
 
 1. CrewAI is a Python package and requires Python >=3.10 and <=3.13 to run.
-2. The preferred way of setting up CrewAI is using the `crewai create crew` 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` 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 preferred tool for package / dependency management in CrewAI.
+   Poetry is CrewAI's prefered tool for package / dependancy management in CrewAI.
 
 ### Code IDEs
 
-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):
+Most users of CrewAI a Code Editor / Integrated Development Environment (IDE) for building there Crews. You can use any code IDE of your choice. Seee below for some popular options for Code Editors / Integrated Development Environments (IDE):
 
 - [Visual Studio Code](https://code.visualstudio.com/) - Most popular
 - [PyCharm](https://www.jetbrains.com/pycharm/)
@@ -48,13 +48,24 @@ Most users of CrewAI use a Code Editor / Integrated Development Environment (IDE
 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 set up a virtual environment, run the following CLI command:
-To create a new CrewAI project, run the following CLI command:
+To setup a virtual environment, run the following CLI command:
 
 ```shell
-$ crewai create crew <project_name>
+$ python3 -m venv <venv-name>
+```
+
+Activate your virtual environment by running the following CLI command:
+
+```shell
+$ source <venv-name>/bin/activate
+```
+
+Now, to create a new CrewAI project, run the following CLI command:
+
+```shell
+$ crewai create <project_name>
 ```
 
 This command will create a new project folder with the following structure:
@@ -117,13 +128,13 @@ 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 won't 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 wont recognize the reference properly.
 
 #### Example References
 agent.yaml
@@ -151,22 +162,23 @@ email_summarizer_task:
       - research_task
 ```
 
-Use the annotations to properly reference the agent and task in the crew.py file.
+Use the annotations are used to properly reference the agent and task in the crew.py file.
 
 ### Annotations include:
-* [@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)
+* @agent
+* @task
+* @crew
+* @llm
+* @tool
+* @callback
+* @output_json
+* @output_pydantic
+* @cache_handler
+
 
 crew.py
 ```py
-# ...
+...
     @llm
     def mixtal_llm(self):
         return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
@@ -182,9 +194,11 @@ 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:
@@ -230,36 +244,12 @@ def run():
 
 To run your project, use the following command:
 
-```shell
-$ crewai run
-```
-or
 ```shell
 $ poetry run my_project
 ```
 
 This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
 
-### Replay Tasks from Latest Crew Kickoff
-
-CrewAI now includes a replay feature that allows you to list the tasks from the last run and replay from a specific one. To use this feature, run:
-
-```shell
-$ crewai replay <task_id>
-```
-
-Replace `<task_id>` with the ID of the task you want to replay.
-
-### Reset Crew Memory
-
-If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature:
-
-```shell
-$ crewai reset-memory
-```
-
-This will clear the crew's memory, allowing for a fresh start.
-
 ## Deploying Your Project
 
 The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

docs/how-to/AgentOps-Observability.md

@@ -36,7 +36,7 @@ Additionally, AgentOps provides session drilldowns for viewing Crew agent intera
 ### Using AgentOps
 
 1. **Create an API Key:**
-   Create a user API key here: [Create API Key](https://app.agentops.ai/account)
+   Create a user API key here: [Create API Key](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

@@ -22,13 +22,11 @@ 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. The `max_retry_limit` parameter, which defaults to 2, controls the maximum number of retries for a task.
+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.
 
 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."
 
@@ -75,4 +73,4 @@ 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

@@ -7,10 +7,9 @@ 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
@@ -18,10 +17,11 @@ 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(
@@ -46,9 +46,11 @@ 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",
@@ -62,7 +64,7 @@ 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,
 )
@@ -77,11 +79,9 @@ task3 = Task(
 crew = Crew(
     agents=[data_fetcher_agent, data_processor_agent, summary_generator_agent],
     tasks=[task1, conditional_task, task3],
-    verbose=True,
-    planning=True  # Enable planning feature
+    verbose=2,
 )
 
-# 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
@@ -13,20 +13,19 @@ 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="Produce amazing reports on AI",
+        goal="Product 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 may reflect and take learnings from the tool but the output is not modified.
+3. **Agent Interaction**: The agent my reflect and take learnings from the tool but the output is not modified.
 4. **Result Return**: The tool output is returned as the task result without any modifications.

docs/how-to/Hierarchical.md

@@ -56,7 +56,6 @@ 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

@@ -81,9 +81,8 @@ task2 = Task(
 crew = Crew(
     agents=[researcher, writer],
     tasks=[task1, task2],
-    verbose=True,
+    verbose=2,
     memory=True,
-    planning=True  # Enable planning feature for the crew
 )
 
 # Get your crew to work!

docs/how-to/Kickoff-async.md

@@ -9,21 +9,6 @@ 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
@@ -49,6 +34,7 @@ analysis_crew = Crew(
     tasks=[data_analysis_task]
 )
 
-# Execute the crew asynchronously
+# Execute the crew
 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:
 
@@ -37,7 +37,6 @@ 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.
 
@@ -48,8 +47,8 @@ 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
@@ -57,7 +56,7 @@ from langchain_ollama import ChatOllama
 import os
 os.environ["OPENAI_API_KEY"] = "NA"
 
-llm = ChatOllama(
+llm = Ollama(
     model = "llama3.1",
     base_url = "http://localhost:11434")
 
@@ -75,7 +74,7 @@ task = Task(description="""what is 3 + 5""",
 crew = Crew(
             agents=[general_agent],
             tasks=[task],
-            verbose=True
+            verbose=2
         )
 
 result = crew.kickoff()
@@ -166,7 +165,7 @@ llm = ChatCohere()
 For Azure OpenAI API integration, set the following environment variables:
 ```sh
 
-os.environ[AZURE_OPENAI_DEPLOYMENT] = "Your deployment"
+os.environ[AZURE_OPENAI_DEPLOYMENT] = "You deployment"
 os.environ["OPENAI_API_VERSION"] = "2023-12-01-preview"
 os.environ["AZURE_OPENAI_ENDPOINT"] = "Your Endpoint"
 os.environ["AZURE_OPENAI_API_KEY"] = "<Your API Key>"
@@ -192,6 +191,5 @@ 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

@@ -11,13 +11,14 @@ 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 the latest kickoff task_ids use:
+
+To view latest kickoff task_ids use:
 ```shell
 crewai log-tasks-outputs
 ```
@@ -28,25 +29,21 @@ 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, it uses the previous kickoff's inputs.
+    inputs = {"topic": "CrewAI Training"} # this is optional, you can pass in the inputs you want to replay otherwise uses the previous kickoffs inputs
     try:
         YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
 
-    except subprocess.CalledProcessError as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")
-
     except Exception as e:
-        raise Exception(f"An unexpected error occurred: {e}")
-```
+        raise Exception(f"An error occurred while replaying the crew: {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, TaskOutput, CrewOutput
+from crewai import Crew, Process, Agent, Task
 
 # Define your agents
 researcher = Agent(
@@ -37,7 +37,6 @@ 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')
@@ -51,10 +50,6 @@ 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
@@ -87,4 +82,4 @@ 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

@@ -46,11 +46,6 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Crews
                 </a>
             </li>
-            <li>
-                <a href="./core-concepts/Pipeline">
-                    Pipeline
-                </a>
-            </li>
             <li>
                 <a href="./core-concepts/Training-Crew">
                     Training
@@ -66,11 +61,6 @@ 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

@@ -1,41 +0,0 @@
-# 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

@@ -1,33 +0,0 @@
-# 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

@@ -1,42 +0,0 @@
-# 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

@@ -1,38 +0,0 @@
-# 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

@@ -1,35 +0,0 @@
-# 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

@@ -1,56 +0,0 @@
-# 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

@@ -1,74 +0,0 @@
-# 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

@@ -1,32 +0,0 @@
-# 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.
-
-Supported filetypes are JPG, PNG, WEBP and GIF
-
-
-## 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(image_path_url="/path/to/your/local/image.jpg")
-
-@agent
-def researcher(self) -> Agent:
-    return Agent(
-        config=self.agents_config["researcher"],
-        allow_delegation=False,
-        tools=[vision_tool]
-    )
-```

mkdocs.yml

@@ -136,6 +136,9 @@ 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'
@@ -152,37 +155,29 @@ nav:
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
   - Tools Docs:
-    - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
-    - Code Docs RAG Search: 'tools/CodeDocsSearchTool.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'
+    - 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'
-    - Vision Tool: 'tools/VisionTool.md'
-    - Website RAG Search: 'tools/WebsiteSearchTool.md'
+    - CSV RAG Search: 'tools/CSVSearchTool.md'
     - XML RAG Search: 'tools/XMLSearchTool.md'
-    - Youtube Channel RAG Search: 'tools/YoutubeChannelSearchTool.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'
+    - Youtube Channel RAG Search: 'tools/YoutubeChannelSearchTool.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 = "0.51.1"
+version = "0.46.0"
 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 = "^0.8.3", optional = true }
+crewai-tools = { version = "^0.4.26", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -46,13 +46,12 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.8.3"
+crewai-tools = "^0.4.26"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"
 pytest-vcr = "^1.0.2"
 python-dotenv = "1.0.0"
-pytest-asyncio = "^0.23.7"
 
 [tool.poetry.scripts]
 crewai = "crewai.cli.cli:crewai"
@@ -60,7 +59,7 @@ crewai = "crewai.cli.cli:crewai"
 [tool.mypy]
 ignore_missing_imports = true
 disable_error_code = 'import-untyped'
-exclude = ["cli/templates"]
+exclude = ["cli/templates/main.py", "cli/templates/crew.py"]
 
 [build-system]
 requires = ["poetry-core"]

src/crewai/__init__.py

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

src/crewai/agent.py

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

src/crewai/agents/agent_builder/base_agent.py

@@ -158,7 +158,7 @@ class BaseAgent(ABC, BaseModel):
     @model_validator(mode="after")
     def set_private_attrs(self):
         """Set private attributes."""
-        self._logger = Logger(verbose=self.verbose)
+        self._logger = Logger(self.verbose)
         if self.max_rpm and not self._rpm_controller:
             self._rpm_controller = RPMController(
                 max_rpm=self.max_rpm, logger=self._logger

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

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

src/crewai/agents/executor.py

@@ -51,7 +51,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
     response_template: Optional[str] = None
-    _logger: Logger = Logger()
+    _logger: Logger = Logger(verbose_level=2)
     _fit_context_window_strategy: Optional[Literal["summarize"]] = "summarize"
 
     def _call(
@@ -69,7 +69,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
         )
         intermediate_steps: List[Tuple[AgentAction, str]] = []
         # Allowing human input given task setting
-        if self.task and self.task.human_input:
+        if self.task.human_input:
             self.should_ask_for_human_input = True
 
         # Let's start tracking the number of iterations and time elapsed

src/crewai/cli/cli.py

@@ -1,16 +1,14 @@
 import click
 import pkg_resources
 
-from crewai.cli.create_crew import create_crew
-from crewai.cli.create_pipeline import create_pipeline
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
 
+from .create_crew import create_crew
 from .evaluate_crew import evaluate_crew
 from .replay_from_task import replay_task_command
 from .reset_memories_command import reset_memories_command
-from .run_crew import run_crew
 from .train_crew import train_crew
 
 
@@ -20,19 +18,10 @@ def crewai():
 
 
 @crewai.command()
-@click.argument("type", type=click.Choice(["crew", "pipeline"]))
-@click.argument("name")
-@click.option(
-    "--router", is_flag=True, help="Create a pipeline with router functionality"
-)
-def create(type, name, router):
-    """Create a new crew or pipeline."""
-    if type == "crew":
-        create_crew(name)
-    elif type == "pipeline":
-        create_pipeline(name, router)
-    else:
-        click.secho("Error: Invalid type. Must be 'crew' or 'pipeline'.", fg="red")
+@click.argument("project_name")
+def create(project_name):
+    """Create a new crew."""
+    create_crew(project_name)
 
 
 @crewai.command()
@@ -60,17 +49,10 @@ def version(tools):
     default=5,
     help="Number of iterations to train the crew",
 )
-@click.option(
-    "-f",
-    "--filename",
-    type=str,
-    default="trained_agents_data.pkl",
-    help="Path to a custom file for training",
-)
-def train(n_iterations: int, filename: str):
+def train(n_iterations: int):
     """Train the crew."""
-    click.echo(f"Training the Crew for {n_iterations} iterations")
-    train_crew(n_iterations, filename)
+    click.echo(f"Training the crew for {n_iterations} iterations")
+    train_crew(n_iterations)
 
 
 @crewai.command()
@@ -165,12 +147,5 @@ def test(n_iterations: int, model: str):
     evaluate_crew(n_iterations, model)
 
 
-@crewai.command()
-def run():
-    """Run the crew."""
-    click.echo("Running the crew")
-    run_crew()
-
-
 if __name__ == "__main__":
     crewai()

src/crewai/cli/create_crew.py

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

src/crewai/cli/create_pipeline.py

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

src/crewai/cli/run_crew.py

@@ -1,23 +0,0 @@
-import subprocess
-
-import click
-
-
-def run_crew() -> None:
-    """
-    Run the crew by running a command in the Poetry environment.
-    """
-    command = ["poetry", "run", "run_crew"]
-
-    try:
-        result = subprocess.run(command, capture_output=False, text=True, check=True)
-
-        if result.stderr:
-            click.echo(result.stderr, err=True)
-
-    except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while running the crew: {e}", err=True)
-        click.echo(e.output, err=True)
-
-    except Exception as e:
-        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/templates/crew.py

@@ -48,6 +48,6 @@ class {{crew_name}}Crew():
 			agents=self.agents, # Automatically created by the @agent decorator
 			tasks=self.tasks, # Automatically created by the @task decorator
 			process=Process.sequential,
-			verbose=True,
+			verbose=2,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
 		)

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

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

src/crewai/cli/templates/main.py

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,17 +0,0 @@
-[tool.poetry]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-
-[tool.poetry.dependencies]
-python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.51.0" }
-asyncio = "*"
-
-[tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

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

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

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

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

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

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

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

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

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

@@ -1,17 +0,0 @@
-research_task:
-  description: >
-    Conduct a thorough research about {topic}
-    Make sure you find any interesting and relevant information given
-    the current year is 2024.
-  expected_output: >
-    A list with 10 bullet points of the most relevant information about {topic}
-  agent: researcher
-
-reporting_task:
-  description: >
-    Review the context you got and expand each topic into a full section for a report.
-    Make sure the report is detailed and contains any and all relevant information.
-  expected_output: >
-    A fully fledge reports with the mains topics, each with a full section of information.
-    Formatted as markdown without '```'
-  agent: reporting_analyst

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,19 +0,0 @@
-[tool.poetry]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-
-[tool.poetry.dependencies]
-python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.51.0" }
-
-[tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-train = "{{folder_name}}.main:train"
-replay = "{{folder_name}}.main:replay"
-test = "{{folder_name}}.main:test"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

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

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

src/crewai/cli/templates/pyproject.toml

@@ -6,11 +6,10 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.51.0" }
+crewai = { extras = ["tools"], version = "^0.46.0" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"
-run_crew = "{{folder_name}}.main:run"
 train = "{{folder_name}}.main:train"
 replay = "{{folder_name}}.main:replay"
 test = "{{folder_name}}.main:test"