Merge pull request #1315 from crewAIInc/docs_update

update readme.md

.github/ISSUE_TEMPLATE/bug_report.yml

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

.github/ISSUE_TEMPLATE/config.yml

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

.github/ISSUE_TEMPLATE/feature_request.yml

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

.github/workflows/mkdocs.yml

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

.github/workflows/security-checker.yml

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

.github/workflows/stale.yml

@@ -0,0 +1,27 @@
+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
+        operations-per-run: 1200

.github/workflows/tests.yml

@@ -11,6 +11,7 @@ env:
 jobs:
   deploy:
     runs-on: ubuntu-latest
+    timeout-minutes: 15
 
     steps:
       - name: Checkout code

README.md

@@ -8,11 +8,11 @@
 
 <h3>
 
-[Homepage](https://www.crewai.io/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/joaomdmoura/crewai-examples) | [Discord](https://discord.com/invite/X4JWnZnxPb)
+[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/crewAIInc/crewAI-examples) | [Discourse](https://community.crewai.com)
 
 </h3>
 
-[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/joaomdmoura/crewAI)
+[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/crewAIInc/crewAI)
 [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 
 </div>
@@ -73,6 +73,7 @@ os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 # You can pass an optional llm attribute specifying what model you wanna use.
 # It can be a local model through Ollama / LM Studio or a remote
 # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
+# If you don't specify a model, the default is OpenAI gpt-4o
 #
 # import os
 # os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
@@ -126,7 +127,7 @@ task2 = Task(
 crew = Crew(
   agents=[researcher, writer],
   tasks=[task1, task2],
-  verbose=2, # You can set it to 1 or 2 to different logging levels
+  verbose=True,
   process = Process.sequential
 )
 
@@ -153,12 +154,12 @@ In addition to the sequential process, you can use the hierarchical process, whi
 
 ## Examples
 
-You can test different real life examples of AI crews in the [crewAI-examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file):
+You can test different real life examples of AI crews in the [crewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
 
-- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
 
 ### Quick Tutorial
 
@@ -166,19 +167,19 @@ You can test different real life examples of AI crews in the [crewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
@@ -190,13 +191,12 @@ Please refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-
 
 ## How CrewAI Compares
 
+**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
+
 - **Autogen**: While Autogen does good in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
 
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
-**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
-
-
 ## Contribution
 
 CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
@@ -254,7 +254,7 @@ pip install dist/*.tar.gz
 
 CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
 
-There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
+It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. We don't offer a way to disable it now, but we will in the future.
 
 Data collected includes:
 
@@ -279,8 +279,44 @@ Data collected includes:
 - Tools names available
   - Understand out of the publically available tools, which ones are being used the most so we can improve them
 
-Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.
+Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
 
 ## License
 
 CrewAI is released under the MIT License.
+
+## Frequently Asked Questions (FAQ)
+
+### Q: What is CrewAI?
+A: CrewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. It enables agents to work together seamlessly, tackling complex tasks through collaborative intelligence.
+
+### Q: How do I install CrewAI?
+A: You can install CrewAI using pip:
+```shell
+pip install crewai
+```
+For additional tools, use:
+```shell
+pip install 'crewai[tools]'
+```
+
+### Q: Can I use CrewAI with local models?
+A: Yes, CrewAI supports various LLMs, including local models. You can configure your agents to use local models via tools like Ollama & LM Studio. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
+
+### Q: What are the key features of CrewAI?
+A: Key features include role-based agent design, autonomous inter-agent delegation, flexible task management, process-driven execution, output saving as files, and compatibility with both open-source and proprietary models.
+
+### Q: How does CrewAI compare to other AI orchestration tools?
+A: CrewAI is designed with production in mind, offering flexibility similar to Autogen's conversational agents and structured processes like ChatDev, but with more adaptability for real-world applications.
+
+### Q: Is CrewAI open-source?
+A: Yes, CrewAI is open-source and welcomes contributions from the community.
+
+### Q: Does CrewAI collect any data?
+A: CrewAI uses anonymous telemetry to collect usage data for improvement purposes. No sensitive data (like prompts, task descriptions, or API calls) is collected. Users can opt-in to share more detailed data by setting `share_crew=True` on their Crews.
+
+### Q: Where can I find examples of CrewAI in action?
+A: You can find various real-life examples in the [crewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
+
+### Q: How can I contribute to CrewAI?
+A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.

docs/core-concepts/Agents.md

@@ -26,7 +26,7 @@ description: What are crewAI Agents and how to use them.
 | **Function Calling LLM** *(optional)* | `function_calling_llm`  | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
 | **Max Iter** *(optional)*  | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
 | **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
-| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the Maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
+| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
 | **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
 | **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
 | **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
@@ -34,6 +34,8 @@ description: What are crewAI Agents and how to use them.
 | **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
+| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.                                                                                                                                                                  |
 
 ## Creating an Agent
 
@@ -72,7 +74,8 @@ agent = Agent(
   tools_handler=my_tools_handler,  # Optional
   cache_handler=my_cache_handler,  # Optional
   callbacks=[callback1, callback2],  # Optional
-  agent_executor=my_agent_executor  # Optional
+  allow_code_execution=True,  # Optiona
+  max_retry_limit=2,  # Optional
 )
 ```
 
@@ -114,7 +117,7 @@ from langchain.agents import load_tools
 langchain_tools = load_tools(["google-serper"], llm=llm)
 
 agent1 = CustomAgent(
-    role="backstory agent",
+    role="agent role",
     goal="who is {input}?",
     backstory="agent backstory",
     verbose=True,
@@ -127,7 +130,7 @@ task1 = Task(
 )
 
 agent2 = Agent(
-    role="bio agent",
+    role="agent role",
     goal="summarize the short bio for {input} and if needed do more research",
     backstory="agent backstory",
     verbose=True,
@@ -144,6 +147,5 @@ my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
 crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
 ```
 
-
 ## Conclusion
-Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.
+Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

docs/core-concepts/Cli.md

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

docs/core-concepts/Collaboration.md

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

docs/core-concepts/Crews.md

@@ -4,36 +4,39 @@ description: Understanding and utilizing crews in the crewAI framework with comp
 ---
 
 ## What is a Crew?
+
 A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
 
 ## Crew Attributes
 
-| Attribute                   | Parameters          | Description                                                                                              |
-| :-------------------------- | :------------------ | :------------------------------------------------------------------------------------------------------- |
-| **Tasks**                   | `tasks`               | A list of tasks assigned to the crew.                                                                    |
-| **Agents**                  | `agents`              | A list of agents that are part of the crew.                                                              |
-| **Process** *(optional)*    | `process`             | The process flow (e.g., sequential, hierarchical) the crew follows.                                      |
-| **Verbose** *(optional)*    | `verbose`             | The verbosity level for logging during execution.                                                        |
-| **Manager LLM** *(optional)*| `manager_llm`         | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
-| **Function Calling LLM** *(optional)* | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
-| **Config** *(optional)*     | `config`              | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                       |
-| **Max RPM** *(optional)*    | `max_rpm`             | Maximum requests per minute the crew adheres to during execution.                                         |
-| **Language**  *(optional)*  | `language`            | Language used for the crew, defaults to English.                                                          |
-| **Language File** *(optional)* | `language_file`       | Path to the language file to be used for the crew.                                                        |
-| **Memory** *(optional)*     | `memory`              | Utilized for storing execution memories (short-term, long-term, entity memory).                           |
-| **Cache** *(optional)*      | `cache`               | Specifies whether to use a cache for storing the results of tools' execution.                             |
-| **Embedder** *(optional)*   | `embedder`            | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                     |
-| **Full Output** *(optional)*| `full_output`         | Whether the crew should return the full output with all tasks outputs or just the final output.           |
-| **Step Callback** *(optional)* | `step_callback`       | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
-| **Task Callback** *(optional)* | `task_callback`       | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
-| **Share Crew** *(optional)* | `share_crew`          | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
-| **Output Log File** *(optional)* | `output_log_file`    | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
-| **Manager Agent** *(optional)* | `manager_agent`      | `manager` sets a custom agent that will be used as a manager.                                             |
-| **Manager Callbacks** *(optional)* | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
-| **Prompt File** *(optional)* | `prompt_file`         | Path to the prompt JSON file to be used for the crew.                                                     |
+| Attribute                             | Parameters             | Description                                                                                                                                                                                                                                               |
+| :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Tasks**                             | `tasks`                | A list of tasks assigned to the crew.                                                                                                                                                                                                                     |
+| **Agents**                            | `agents`               | A list of agents that are part of the crew.                                                                                                                                                                                                               |
+| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows.                                                                                                                                                                                       |
+| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution.                                                                                                                                                                                                         |
+| **Manager LLM** _(optional)_          | `manager_llm`          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.**                                                                                                                                   |
+| **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
+| **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
+| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution.                                                                                                                                                                                         |
+| **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
+| **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution.                                                                                                                                                                             |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                                                                                                                                                                     |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output.                                                                                                                                                           |
+| **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
+| **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
+| **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
+| **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
+| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
+| **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
+| **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
+| **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.
+The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
 
 ## Creating a Crew
 
@@ -44,6 +47,12 @@ When assembling a crew, you combine agents with complementary roles and tools, a
 ```python
 from crewai import Crew, Agent, Task, Process
 from langchain_community.tools import DuckDuckGoSearchRun
+from crewai_tools import tool
+
+@tool('DuckDuckGoSearch')
+def search(search_query: str):
+    """Search the web for information on a given topic"""
+    return DuckDuckGoSearchRun().run(search_query)
 
 # Define agents with specific roles and tools
 researcher = Agent(
@@ -54,7 +63,7 @@ researcher = Agent(
         to the business.
         You're currently working on a project to analyze the
         trends and innovations in the space of artificial intelligence.""",
-    tools=[DuckDuckGoSearchRun()]
+    tools=[search]
 )
 
 writer = Agent(
@@ -89,6 +98,57 @@ my_crew = Crew(
 )
 ```
 
+## Crew Output
+
+!!! note "Understanding Crew Outputs"
+The output of a crew in the crewAI framework is encapsulated within the `CrewOutput` class.
+This class provides a structured way to access results of the crew's execution, including various formats such as raw strings, JSON, and Pydantic models.
+The `CrewOutput` includes the results from the final task output, token usage, and individual task outputs.
+
+### Crew Output Attributes
+
+| Attribute        | Parameters     | Type                       | Description                                                                                          |
+| :--------------- | :------------- | :------------------------- | :--------------------------------------------------------------------------------------------------- |
+| **Raw**          | `raw`          | `str`                      | The raw output of the crew. This is the default format for the output.                               |
+| **Pydantic**     | `pydantic`     | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the crew.                              |
+| **JSON Dict**    | `json_dict`    | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the crew.                                               |
+| **Tasks Output** | `tasks_output` | `List[TaskOutput]`         | A list of `TaskOutput` objects, each representing the output of a task in the crew.                  |
+| **Token Usage**  | `token_usage`  | `Dict[str, Any]`           | A summary of token usage, providing insights into the language model's performance during execution. |
+
+### Crew Output Methods and Properties
+
+| Method/Property | Description                                                                                       |
+| :-------------- | :------------------------------------------------------------------------------------------------ |
+| **json**        | Returns the JSON string representation of the crew output if the output format is JSON.           |
+| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
+| \***\*str\*\*** | Returns the string representation of the crew output, prioritizing Pydantic, then JSON, then raw. |
+
+### Accessing Crew Outputs
+
+Once a crew has been executed, its output can be accessed through the `output` attribute of the `Crew` object. The `CrewOutput` class provides various ways to interact with and present this output.
+
+#### Example
+
+```python
+# Example crew execution
+crew = Crew(
+    agents=[research_agent, writer_agent],
+    tasks=[research_task, write_article_task],
+    verbose=True
+)
+
+crew_output = crew.kickoff()
+
+# Accessing the crew output
+print(f"Raw Output: {crew_output.raw}")
+if crew_output.json_dict:
+    print(f"JSON Output: {json.dumps(crew_output.json_dict, indent=2)}")
+if crew_output.pydantic:
+    print(f"Pydantic Output: {crew_output.pydantic}")
+print(f"Tasks Output: {crew_output.tasks_output}")
+print(f"Token Usage: {crew_output.token_usage}")
+```
+
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
@@ -123,14 +183,14 @@ result = my_crew.kickoff()
 print(result)
 ```
 
-### Different ways to Kicking Off a Crew
+### Different Ways to Kick Off a Crew
 
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 
-`kickoff()`: Starts the execution process according to the defined process flow.
-`kickoff_for_each()`: Executes tasks for each agent individually.
-`kickoff_async()`: Initiates the workflow asynchronously.
-`kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
+- `kickoff()`: Starts the execution process according to the defined process flow.
+- `kickoff_for_each()`: Executes tasks for each agent individually.
+- `kickoff_async()`: Initiates the workflow asynchronously.
+- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
 
 ```python
 # Start the crew's task execution
@@ -155,30 +215,31 @@ for async_result in async_results:
     print(async_result)
 ```
 
-These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs
+These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
 
+### Replaying from a Specific Task
 
-### Replaying from specific task:
-You can now replay from a specific task using our cli command replay.
+You can now replay from a specific task using our CLI command `replay`.
 
-The replay_from_tasks 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.
+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. 
+Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
 
+### Replaying from a Specific Task Using the CLI
 
-### Replaying from specific task Using the CLI
 To use the replay feature, follow these steps:
 
 1. Open your terminal or command prompt.
 2. Navigate to the directory where your CrewAI project is located.
 3. Run the following command:
 
-To view latest kickoff task_ids use:
+To view the latest kickoff task IDs, use:
 
 ```shell
 crewai log-tasks-outputs
 ```
 
+Then, to replay from a specific task, use:
 
 ```shell
 crewai replay -t <task_id>

docs/core-concepts/Memory.md

@@ -29,6 +29,11 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
 When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
 By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
 
+The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.
+The **Long-Term Memory** uses SQLLite3 to store task results.  Currently, there is no way to override these storage implementations.
+The data storage files are saved into a platform specific location found using the appdirs package
+and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
+
 ### Example: Configuring Memory for a Crew
 
 ```python
@@ -100,7 +105,7 @@ my_crew = Crew(
 			"provider": "azure_openai",
 			"config":{
 				"model": 'text-embedding-ada-002',
-				"deployment_name": "you_embedding_model_deployment_name"
+				"deployment_name": "your_embedding_model_deployment_name"
 			}
 		}
 )
@@ -154,13 +159,44 @@ my_crew = Crew(
 		embedder={
 			"provider": "cohere",
 			"config":{
-				"model": "embed-english-v3.0"
-    		"vector_dimension": 1024
+				"model": "embed-english-v3.0",
+        "vector_dimension": 1024
 			}
 		}
 )
 ```
 
+### Resetting Memory
+```sh
+crewai reset_memories [OPTIONS]
+```
+
+#### Resetting Memory Options
+- **`-l, --long`**
+  - **Description:** Reset LONG TERM memory.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+- **`-s, --short`**
+  - **Description:** Reset SHORT TERM memory.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+- **`-e, --entities`**
+  - **Description:** Reset ENTITIES memory.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+- **`-k, --kickoff-outputs`**
+  - **Description:** Reset LATEST KICKOFF TASK OUTPUTS.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+- **`-a, --all`**
+  - **Description:** Reset ALL memories.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
 ## Benefits of Using crewAI's Memory System
 - **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
 - **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.

docs/core-concepts/Pipeline.md

@@ -0,0 +1,267 @@
+---
+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 normal 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

@@ -0,0 +1,134 @@
+---
+title: crewAI Planning
+description: Learn how to add planning to your crewAI Crew and improve their performance.
+---
+
+## Introduction
+The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
+
+### Using the Planning Feature
+Getting started with the planning feature is very easy, the only step required is to add `planning=True` to your Crew:
+
+```python
+from crewai import Crew, Agent, Task, Process
+
+# Assemble your crew with planning capabilities
+my_crew = Crew(
+    agents=self.agents,
+    tasks=self.tasks,
+    process=Process.sequential,
+    planning=True,
+)
+```
+
+From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
+
+#### Planning LLM
+
+Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available.
+
+```python
+from crewai import Crew, Agent, Task, Process
+from langchain_openai import ChatOpenAI
+
+# Assemble your crew with planning capabilities and custom LLM
+my_crew = Crew(
+    agents=self.agents,
+    tasks=self.tasks,
+    process=Process.sequential,
+    planning=True,
+    planning_llm=ChatOpenAI(model="gpt-4o")
+)
+```
+
+### Example
+
+When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
+
+```
+[2024-07-15 16:49:11][INFO]: Planning the crew execution
+**Step-by-Step Plan for Task Execution**
+
+**Task Number 1: Conduct a thorough research about AI LLMs**
+
+**Agent:** AI LLMs Senior Data Researcher
+
+**Agent Goal:** Uncover cutting-edge developments in AI LLMs
+
+**Task Expected Output:** A list with 10 bullet points of the most relevant information about AI LLMs
+
+**Task Tools:** None specified
+
+**Agent Tools:** None specified
+
+**Step-by-Step Plan:**
+
+1. **Define Research Scope:**
+   - Determine the specific areas of AI LLMs to focus on, such as advancements in architecture, use cases, ethical considerations, and performance metrics.
+
+2. **Identify Reliable Sources:**
+   - List reputable sources for AI research, including academic journals, industry reports, conferences (e.g., NeurIPS, ACL), AI research labs (e.g., OpenAI, Google AI), and online databases (e.g., IEEE Xplore, arXiv).
+
+3. **Collect Data:**
+   - Search for the latest papers, articles, and reports published in 2023 and early 2024.
+   - Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
+
+4. **Analyze Findings:**
+   - Read and summarize the key points from each source.
+   - Highlight new techniques, models, and applications introduced in the past year.
+
+5. **Organize Information:**
+   - Categorize the information into relevant topics (e.g., new architectures, ethical implications, real-world applications).
+   - Ensure each bullet point is concise but informative.
+
+6. **Create the List:**
+   - Compile the 10 most relevant pieces of information into a bullet point list.
+   - Review the list to ensure clarity and relevance.
+
+**Expected Output:**
+A list with 10 bullet points of the most relevant information about AI LLMs.
+
+---
+
+**Task Number 2: Review the context you got and expand each topic into a full section for a report**
+
+**Agent:** AI LLMs Reporting Analyst
+
+**Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings
+
+**Task Expected Output:** A fully fledge report with the main topics, each with a full section of information. Formatted as markdown without '```'
+
+**Task Tools:** None specified
+
+**Agent Tools:** None specified
+
+**Step-by-Step Plan:**
+
+1. **Review the Bullet Points:**
+   - Carefully read through the list of 10 bullet points provided by the AI LLMs Senior Data Researcher.
+
+2. **Outline the Report:**
+   - Create an outline with each bullet point as a main section heading.
+   - Plan sub-sections under each main heading to cover different aspects of the topic.
+
+3. **Research Further Details:**
+   - For each bullet point, conduct additional research if necessary to gather more detailed information.
+   - Look for case studies, examples, and statistical data to support each section.
+
+4. **Write Detailed Sections:**
+   - Expand each bullet point into a comprehensive section.
+   - Ensure each section includes an introduction, detailed explanation, examples, and a conclusion.
+   - Use markdown formatting for headings, subheadings, lists, and emphasis.
+
+5. **Review and Edit:**
+   - Proofread the report for clarity, coherence, and correctness.
+   - Make sure the report flows logically from one section to the next.
+   - Format the report according to markdown standards.
+
+6. **Finalize the Report:**
+   - Ensure the report is complete with all sections expanded and detailed.
+   - Double-check formatting and make any necessary adjustments.
+
+**Expected Output:**
+A fully-fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
+```

docs/core-concepts/Processes.md

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

docs/core-concepts/Tasks.md

@@ -4,27 +4,30 @@ description: Detailed guide on managing and creating tasks within the crewAI fra
 ---
 
 ## Overview of a Task
+
 !!! note "What is a Task?"
-    In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
+In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
 
 Tasks within crewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
 ## Task Attributes
 
-| Attribute              | Parameters           | Description                                                                                   |
-| :----------------------| :------------------- | :-------------------------------------------------------------------------------------------- |
-| **Description**        | `description`          | A clear, concise statement of what the task entails.                                          |
-| **Agent**              | `agent`                | The agent responsible for the task, assigned either directly or by the crew's process.        |
-| **Expected Output**    | `expected_output`      | A detailed description of what the task's completion looks like.                              |
-| **Tools** *(optional)* | `tools`                | The functions or capabilities the agent can utilize to perform the task.                      |
-| **Async Execution** *(optional)* | `async_execution`      | If set, the task executes asynchronously, allowing progression without waiting for completion.|
-| **Context**  *(optional)* | `context`              | Specifies tasks whose outputs are used as context for this task.                              |
-| **Config** *(optional)* | `config`               | Additional configuration details for the agent executing the task, allowing further customization. |
-| **Output JSON**  *(optional)* | `output_json`          | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.         |
-| **Output Pydantic**  *(optional)* | `output_pydantic`      | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
-| **Output File**  *(optional)* | `output_file`          | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Callback**  *(optional)* | `callback`             | A Python callable that is executed with the task's output upon completion.                    |
-| **Human Input** *(optional)* | `human_input`         | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
+| Attribute                        | Parameters        | Description                                                                                                          |
+| :------------------------------- | :---------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Description**                  | `description`     | A clear, concise statement of what the task entails.                                                                 |
+| **Agent**                        | `agent`           | The agent responsible for the task, assigned either directly or by the crew's process.                               |
+| **Expected Output**              | `expected_output` | A detailed description of what the task's completion looks like.                                                     |
+| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list.                  |
+| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False.    |
+| **Context** _(optional)_         | `context`         | Specifies tasks whose outputs are used as context for this task.                                                     |
+| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
+| **Output JSON** _(optional)_     | `output_json`     | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
+| **Output Pydantic** _(optional)_ | `output_pydantic` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
+| **Output File** _(optional)_     | `output_file`     | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
+| **Output** _(optional)_          | `output`          | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details.                  |
+| **Callback** _(optional)_        | `callback`        | A callable that is executed with the task's output upon completion.                                                  |
+| **Human Input** _(optional)_     | `human_input`     | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. Defaults to False.|
+| **Converter Class** _(optional)_ | `converter_cls`   | A converter class used to export structured output. Defaults to None.                                                |
 
 ## Creating a Task
 
@@ -35,12 +38,75 @@ from crewai import Task
 
 task = Task(
     description='Find and summarize the latest and most relevant news on AI',
-    agent=sales_agent
+    agent=sales_agent,
+    expected_output='A bullet list summary of the top 5 most important AI news',
 )
 ```
 
 !!! note "Task Assignment"
-    Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
+Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
+
+## Task Output
+
+!!! note "Understanding Task Outputs"
+The output of a task in the crewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw strings, JSON, and Pydantic models.
+By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
+
+### Task Output Attributes
+
+| Attribute         | Parameters      | Type                       | Description                                                                                        |
+| :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
+| **Description**   | `description`   | `str`                      | A brief description of the task.                                                                   |
+| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the first 10 words of the description.            |
+| **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
+| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
+| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
+| **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
+| **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
+
+### Task Output Methods and Properties
+
+| Method/Property | Description                                                                                       |
+| :-------------- | :------------------------------------------------------------------------------------------------ |
+| **json**        | Returns the JSON string representation of the task output if the output format is JSON.           |
+| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
+| \***\*str\*\*** | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
+
+### Accessing Task Outputs
+
+Once a task has been executed, its output can be accessed through the `output` attribute of the `Task` object. The `TaskOutput` class provides various ways to interact with and present this output.
+
+#### Example
+
+```python
+# Example task
+task = Task(
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=research_agent,
+    tools=[search_tool]
+)
+
+# Execute the crew
+crew = Crew(
+    agents=[research_agent],
+    tasks=[task],
+    verbose=True
+)
+
+result = crew.kickoff()
+
+# Accessing the task output
+task_output = task.output
+
+print(f"Task Description: {task_output.description}")
+print(f"Task Summary: {task_output.summary}")
+print(f"Raw Output: {task_output.raw}")
+if task_output.json_dict:
+    print(f"JSON Output: {json.dumps(task_output.json_dict, indent=2)}")
+if task_output.pydantic:
+    print(f"Pydantic Output: {task_output.pydantic}")
+```
 
 ## Integrating Tools with Tasks
 
@@ -65,6 +131,7 @@ research_agent = Agent(
   verbose=True
 )
 
+# to perform a semantic search for a specified query from a text's content across the internet
 search_tool = SerperDevTool()
 
 task = Task(
@@ -77,7 +144,7 @@ task = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task],
-    verbose=2
+    verbose=True
 )
 
 result = crew.kickoff()
@@ -199,7 +266,7 @@ task1 = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task1, task2, task3],
-    verbose=2
+    verbose=True
 )
 
 result = crew.kickoff()

docs/core-concepts/Testing.md

@@ -0,0 +1,40 @@
+---
+title: crewAI Testing
+description: Learn how to test your crewAI Crew and evaluate their performance.
+---
+
+## Introduction
+
+Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. With crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
+
+### Using the Testing Feature
+
+We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
+
+```bash
+crewai test
+```
+
+If you want to run more iterations or use a different model, you can specify the parameters like this:
+
+```bash
+crewai test --n_iterations 5 --model gpt-4o
+```
+
+When you run the `crewai test` command, the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
+
+A table of scores at the end will show the performance of the crew in terms of the following metrics:
+
+```
+                Task Scores
+          (1-10 Higher is better)
+┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
+┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
+┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
+│ Task 1     │ 10.0  │ 9.0   │ 9.5        │
+│ Task 2     │ 9.0   │ 9.0   │ 9.0        │
+│ Crew       │ 9.5   │ 9.0   │ 9.2        │
+└────────────┴───────┴───────┴────────────┘
+```
+
+The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.

docs/core-concepts/Tools.md

@@ -80,11 +80,12 @@ write = Task(
     output_file='blog-posts/new_post.md'  # The final blog post will be saved here
 )
 
-# Assemble a crew
+# Assemble a crew with planning enabled
 crew = Crew(
     agents=[researcher, writer],
     tasks=[research, write],
-    verbose=2
+    verbose=True,
+    planning=True,  # Enable planning feature
 )
 
 # Execute tasks
@@ -100,19 +101,29 @@ Here is a list of the available tools and their descriptions:
 
 | Tool                        | Description                                                                                   |
 | :-------------------------- | :-------------------------------------------------------------------------------------------- |
+| **BrowserbaseLoadTool**     | A tool for interacting with and extracting data from web browsers.                            |
 | **CodeDocsSearchTool**      | A RAG tool optimized for searching through code documentation and related technical documents. |
+| **CodeInterpreterTool**     | A tool for interpreting python code.                                                          |
+| **ComposioTool**            | Enables use of Composio tools.                                                                |
 | **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
+| **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.                |
+| **EXASearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
 | **FileReadTool**            | Enables reading and extracting data from files, supporting various file formats.              |
+| **FirecrawlSearchTool**     | A tool to search webpages using Firecrawl and return the results.                             |
+| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl.                                               |
+| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages url using Firecrawl and returning its contents.               |
 | **GithubSearchTool**        | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
 | **SerperDevTool**           | A specialized tool for development purposes, with specific functionalities under development. |
 | **TXTSearchTool**           | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.     |
 | **JSONSearchTool**          | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
+| **LlamaIndexTool**          | Enables the use of LlamaIndex tools.                                                          |
 | **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
 | **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
 | **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
+| **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.                 |
@@ -120,8 +131,6 @@ Here is a list of the available tools and their descriptions:
 | **XMLSearchTool**           | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
 | **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis.           |
 | **YoutubeVideoSearchTool**  | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
-| **BrowserbaseTool**         | A tool for interacting with and extracting data from web browsers.                            |
-| **ExaSearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
 
 ## Creating your own Tools
 
@@ -189,6 +198,5 @@ writer1 = Agent(
     #...
 ```
 
-
 ## Conclusion
 Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/core-concepts/Training-Crew.md

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

docs/getting-started/Create-a-New-CrewAI-Pipeline-Template-Method.md

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

docs/getting-started/Installing-CrewAI.md

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

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

@@ -0,0 +1,231 @@
+---
+title: Starting a New CrewAI Project - Using Template
+description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
+---
+
+# Starting Your CrewAI Project
+
+Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
+
+Before we start, there are a couple of things to note:
+
+1. CrewAI is a Python package and requires Python >=3.10 and <=3.13 to run.
+2. The preferred way of setting up CrewAI is using the `crewai create crew` command. This will create a new project folder and install a skeleton template for you to work on.
+
+## Prerequisites
+
+Before getting started with CrewAI, make sure that you have installed it via pip:
+
+```shell
+$ pip install 'crewai[tools]'
+```
+
+## Creating a New Project
+In this example, we will be using poetry as our virtual environment manager.
+
+To create a new CrewAI project, run the following CLI command:
+
+```shell
+$ crewai create crew <project_name>
+```
+
+This command will create a new project folder with the following structure:
+
+```shell
+my_project/
+├── .gitignore
+├── pyproject.toml
+├── README.md
+└── src/
+    └── my_project/
+        ├── __init__.py
+        ├── main.py
+        ├── crew.py
+        ├── tools/
+        │   ├── custom_tool.py
+        │   └── __init__.py
+        └── config/
+            ├── agents.yaml
+            └── tasks.yaml
+```
+
+You can now start developing your project by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of your project, and the `crew.py` file is where you define your agents and tasks.
+
+## Customizing Your Project
+
+To customize your project, you can:
+- Modify `src/my_project/config/agents.yaml` to define your agents.
+- Modify `src/my_project/config/tasks.yaml` to define your tasks.
+- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
+- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
+- Add your environment variables into the `.env` file.
+
+### Example: Defining Agents and Tasks
+
+#### agents.yaml
+
+```yaml
+researcher:
+  role: >
+    Job Candidate Researcher
+  goal: >
+    Find potential candidates for the job
+  backstory: >
+    You are adept at finding the right candidates by exploring various online
+    resources. Your skill in identifying suitable candidates ensures the best
+    match for job positions.
+```
+
+#### tasks.yaml
+
+```yaml
+research_candidates_task:
+  description: >
+    Conduct thorough research to find potential candidates for the specified job.
+    Utilize various online resources and databases to gather a comprehensive list of potential candidates.
+    Ensure that the candidates meet the job requirements provided.
+
+    Job Requirements:
+    {job_requirements}
+  expected_output: >
+    A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
+  agent: researcher # THIS NEEDS TO MATCH THE AGENT NAME IN THE AGENTS.YAML FILE AND THE AGENT DEFINED IN THE crew.py FILE
+  context: # THESE NEED TO MATCH THE TASK NAMES DEFINED ABOVE AND THE TASKS.YAML FILE AND THE TASK DEFINED IN THE crew.py FILE
+    - researcher
+```
+
+### Referencing Variables:
+Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from task.yaml file. Ensure your annotated agent and function name is the same otherwise your task won't recognize the reference properly.
+
+#### Example References
+agent.yaml
+```yaml
+email_summarizer:
+    role: >
+      Email Summarizer
+    goal: >
+      Summarize emails into a concise and clear summary
+    backstory: >
+      You will create a 5 bullet point summary of the report
+    llm: mixtal_llm
+```
+
+task.yaml
+```yaml
+email_summarizer_task:
+    description: >
+      Summarize the email into a 5 bullet point summary
+    expected_output: >
+      A 5 bullet point summary of the email
+    agent: email_summarizer
+    context:
+      - reporting_task
+      - research_task
+```
+
+Use the annotations 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)
+
+crew.py
+```py
+# ...
+    @llm
+    def mixtal_llm(self):
+        return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
+
+    @agent
+    def email_summarizer(self) -> Agent:
+        return Agent(
+            config=self.agents_config["email_summarizer"],
+        )
+    ## ...other tasks defined
+    @task
+    def email_summarizer_task(self) -> Task:
+        return Task(
+            config=self.tasks_config["email_summarizer_task"],
+        )
+# ...
+```
+
+## Installing Dependencies
+
+To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
+
+```shell
+$ cd my_project
+$ crewai install
+```
+
+This will install the dependencies specified in the `pyproject.toml` file.
+
+## Interpolating Variables
+
+Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
+
+#### agents.yaml
+
+```yaml
+research_task:
+  description: >
+    Conduct a thorough research about the customer and competitors in the context
+    of {customer_domain}.
+    Make sure you find any interesting and relevant information given the
+    current year is 2024.
+  expected_output: >
+    A complete report on the customer and their customers and competitors,
+    including their demographics, preferences, market positioning and audience engagement.
+```
+
+#### main.py
+
+```python
+# main.py
+def run():
+    inputs = {
+        "customer_domain": "crewai.com"
+    }
+    MyProjectCrew(inputs).crew().kickoff(inputs=inputs)
+```
+
+## Running Your Project
+
+To run your project, use the following command:
+
+```shell
+$ crewai run
+```
+This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
+
+### Replay Tasks from Latest Crew Kickoff
+
+CrewAI now includes a replay feature that allows you to list the tasks from the last run and replay from a specific one. To use this feature, run:
+
+```shell
+$ crewai replay <task_id>
+```
+
+Replace `<task_id>` with the ID of the task you want to replay.
+
+### Reset Crew Memory
+
+If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature:
+
+```shell
+$ crewai reset-memory
+```
+
+This will clear the crew's memory, allowing for a fresh start.
+
+## Deploying Your Project
+
+The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

docs/how-to/AgentOps-Observability.md

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

docs/how-to/Coding-Agents.md

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

docs/how-to/Conditional-Tasks.md

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

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

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

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

@@ -1,82 +0,0 @@
----
-title: Assembling and Activating Your CrewAI Team
-description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, asynchronous execution, output customization, language model configuration, code execution, integration with third-party agents, and improved task management.
----
-
-## Introduction
-Embark on your CrewAI journey by setting up your environment and initiating your AI crew with the latest features. This guide ensures a smooth start, incorporating all recent updates for an enhanced experience, including code execution capabilities, integration with third-party agents, and advanced task management.
-
-## Step 0: Installation
-Install CrewAI and any necessary packages for your project. CrewAI is compatible with Python >=3.10,<=3.13.
-
-```shell
-pip install crewai
-pip install 'crewai[tools]'
-```
-
-## Step 1: Assemble Your Agents
-Define your agents with distinct roles, backstories, and enhanced capabilities. The Agent class now supports a wide range of attributes for fine-tuned control over agent behavior and interactions, including code execution and integration with third-party agents.
-
-```python
-import os
-from langchain.llms import OpenAI
-from crewai import Agent
-from crewai_tools import SerperDevTool, BrowserbaseTool, ExaSearchTool
-
-os.environ["OPENAI_API_KEY"] = "Your OpenAI Key"
-os.environ["SERPER_API_KEY"] = "Your Serper Key"
-
-search_tool = SerperDevTool()
-browser_tool = BrowserbaseTool()
-exa_search_tool = ExaSearchTool()
-
-# Creating a senior researcher agent with advanced configurations
-researcher = Agent(
-    role='Senior Researcher',
-    goal='Uncover groundbreaking technologies in {topic}',
-    backstory=("Driven by curiosity, you're at the forefront of innovation, "
-               "eager to explore and share knowledge that could change the world."),
-    memory=True,
-    verbose=True,
-    allow_delegation=False,
-    tools=[search_tool, browser_tool],
-    allow_code_execution=False,  # New attribute for enabling code execution
-    max_iter=15,  # Maximum number of iterations for task execution
-    max_rpm=100,  # Maximum requests per minute
-    max_execution_time=3600,  # Maximum execution time in seconds
-    system_template="Your custom system template here",  # Custom system template
-    prompt_template="Your custom prompt template here",  # Custom prompt template
-    response_template="Your custom response template here",  # Custom response template
-)
-
-# Creating a writer agent with custom tools and specific configurations
-writer = Agent(
-    role='Writer',
-    goal='Narrate compelling tech stories about {topic}',
-    backstory=("With a flair for simplifying complex topics, you craft engaging "
-               "narratives that captivate and educate, bringing new discoveries to light."),
-    verbose=True,
-    allow_delegation=False,
-    memory=True,
-    tools=[exa_search_tool],
-    function_calling_llm=OpenAI(model_name="gpt-3.5-turbo"),  # Separate LLM for function calling
-)
-
-# Setting a specific manager agent
-manager = Agent(
-  role='Manager',
-  goal='Ensure the smooth operation and coordination of the team',
-  verbose=True,
-  backstory=(
-    "As a seasoned project manager, you excel in organizing "
-    "tasks, managing timelines, and ensuring the team stays on track."
-  ),
-  allow_code_execution=True,  # Enable code execution for the manager
-)
-```
-
-### New Agent Attributes and Features
-
-1. `allow_code_execution`: Enable or disable code execution capabilities for the agent (default is False).
-2. `max_execution_time`: Set a maximum execution time (in seconds) for the agent to complete a task.
-3. `function_calling_llm`: Specify a separate language model for function calling.

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

@@ -1,31 +1,32 @@
 ---
 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
 In CrewAI, you can force the output of a tool as the result of an agent's task. This feature is useful when you want to ensure that the tool output is captured and returned as the task result, and avoid the agent modifying the output during the task execution.
 
 ## Forcing Tool Output as Result
-To force the tool output as the result of an agent's task, you can set the `force_tool_output` parameter to `True` when creating the task. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
+To force the tool output as the result of an agent's task, you can set the `result_as_answer` parameter to `True` when creating the agent. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
 
 Here's an example of how to force the tool output as the result of an agent's task:
 
 ```python
 # ...
+from crewai.agent import Agent
+
 # Define a custom tool that returns the result as the answer
-coding_agent =Agent(
+coding_agent = Agent(
         role="Data Scientist",
-        goal="Product amazing resports on AI",
+        goal="Produce amazing reports on AI",
         backstory="You work with data and AI",
         tools=[MyCustomTool(result_as_answer=True)],
     )
-# ...
 ```
 
-### Workflow in Action
+## Workflow in Action
 
 1. **Task Execution**: The agent executes the task using the tool provided.
 2. **Tool Output**: The tool generates the output, which is captured as the task result.
-3. **Agent Interaction**: The agent my reflect and take learnings from the tool but the output is not modified.
+3. **Agent Interaction**: The agent may reflect and take learnings from the tool but the output is not modified.
 4. **Result Return**: The tool output is returned as the task result without any modifications.

docs/how-to/Hierarchical.md

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

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

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

docs/how-to/Kickoff-async.md

@@ -4,14 +4,41 @@ description: Kickoff a Crew Asynchronously
 ---
 
 ## Introduction
+
 CrewAI provides the ability to kickoff a crew asynchronously, allowing you to start the crew execution in a non-blocking manner. This feature is particularly useful when you want to run multiple crews concurrently or when you need to perform other tasks while the crew is executing.
 
 ## 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.
 
-Here's an example of how to kickoff a crew asynchronously:
+### 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.
+
+## Potential Use Cases
+
+- **Parallel Content Generation**: Kickoff multiple independent crews asynchronously, each responsible for generating content on different topics. For example, one crew might research and draft an article on AI trends, while another crew generates social media posts about a new product launch. Each crew operates independently, allowing content production to scale efficiently.
+
+- **Concurrent Market Research Tasks**: Launch multiple crews asynchronously to conduct market research in parallel. One crew might analyze industry trends, while another examines competitor strategies, and yet another evaluates consumer sentiment. Each crew independently completes its task, enabling faster and more comprehensive insights.
+
+- **Independent Travel Planning Modules**: Execute separate crews to independently plan different aspects of a trip. One crew might handle flight options, another handles accommodation, and a third plans activities. Each crew works asynchronously, allowing various components of the trip to be planned simultaneously and independently for faster results.
+
+## Example: Single Asynchronous Crew Execution
+
+Here's an example of how to kickoff a crew asynchronously using asyncio and awaiting the result:
+
+```python
+import asyncio
 from crewai import Crew, Agent, Task
 
 # Create an agent with code execution enabled
@@ -34,7 +61,57 @@ analysis_crew = Crew(
     tasks=[data_analysis_task]
 )
 
-# Execute the crew
-result = analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
+# Async function to kickoff the crew asynchronously
+async def async_crew_execution():
+    result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
+    print("Crew Result:", result)
+
+# Run the async function
+asyncio.run(async_crew_execution())
 ```
 
+## Example: Multiple Asynchronous Crew Executions
+
+In this example, we'll show how to kickoff multiple crews asynchronously and wait for all of them to complete using asyncio.gather():
+
+```python
+import asyncio
+from crewai import Crew, Agent, Task
+
+# Create an agent with code execution enabled
+coding_agent = Agent(
+    role="Python Data Analyst",
+    goal="Analyze data and provide insights using Python",
+    backstory="You are an experienced data analyst with strong Python skills.",
+    allow_code_execution=True
+)
+
+# Create tasks that require code execution
+task_1 = Task(
+    description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
+    agent=coding_agent
+)
+
+task_2 = Task(
+    description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
+    agent=coding_agent
+)
+
+# Create two crews and add tasks
+crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
+crew_2 = Crew(agents=[coding_agent], tasks=[task_2])
+
+# Async function to kickoff multiple crews asynchronously and wait for all to finish
+async def async_multiple_crews():
+    result_1 = crew_1.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
+    result_2 = crew_2.kickoff_async(inputs={"ages": [20, 22, 24, 28, 30]})
+
+    # Wait for both crews to finish
+    results = await asyncio.gather(result_1, result_2)
+
+    for i, result in enumerate(results, 1):
+        print(f"Crew {i} Result:", result)
+
+# Run the async function
+asyncio.run(async_multiple_crews())
+```

docs/how-to/LLM-Connections.md

@@ -6,33 +6,25 @@ description: Comprehensive guide on integrating CrewAI with various Large Langua
 ## Connect CrewAI to LLMs
 
 !!! note "Default LLM"
-    By default, CrewAI uses OpenAI's GPT-4 model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4o") for language processing. You can configure your agents to use a different model or API as described in this guide.
+    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 offers flexibility in connecting to various LLMs, including local models via [Ollama](https://ollama.ai) and different APIs like Azure. It's compatible with all [LangChain LLM](https://python.langchain.com/docs/integrations/llms/) components, enabling diverse integrations for tailored AI solutions.
+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 Agent Overview
+The platform supports connections to an array of Generative AI models, including:
 
-The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. Here's a comprehensive overview of the Agent class attributes and methods:
+ - OpenAI's suite of advanced language models
+ - Anthropic's cutting-edge AI offerings
+ - Ollama's diverse range of locally-hosted generative model & embeddings
+ - LM Studio's diverse range of locally hosted generative models & embeddings
+ - Groq's Super Fast LLM offerings
+ - Azures' generative AI offerings
+ - HuggingFace's generative AI offerings
 
-- **Attributes**:
-    - `role`: Defines the agent's role within the solution.
-    - `goal`: Specifies the agent's objective.
-    - `backstory`: Provides a background story to the agent.
-    - `cache` *Optional*: Determines whether the agent should use a cache for tool usage. Default is `True`.
-    - `max_rpm` *Optional*: Maximum number of requests per minute the agent's execution should respect. Optional.
-    - `verbose` *Optional*: Enables detailed logging of the agent's execution. Default is `False`.
-    - `allow_delegation` *Optional*: Allows the agent to delegate tasks to other agents, default is `True`.
-    - `tools`: Specifies the tools available to the agent for task execution. Optional.
-    - `max_iter` *Optional*: Maximum number of iterations for an agent to execute a task, default is 25.
-    - `max_execution_time` *Optional*: Maximum execution time for an agent to execute a task. Optional.
-    - `step_callback` *Optional*: Provides a callback function to be executed after each step. Optional.
-    - `llm` *Optional*: Indicates the Large Language Model the agent uses. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
-    - `function_calling_llm` *Optional* :  Will turn the ReAct CrewAI agent into a function-calling agent.
-    - `callbacks` *Optional*: A list of callback functions from the LangChain library that are triggered during the agent's execution process.
-    - `system_template` *Optional*: Optional string to define the system format for the agent.
-    - `prompt_template` *Optional*: Optional string to define the prompt format for the agent.
-    - `response_template` *Optional*: Optional string to define the response format for the agent.
+This broad spectrum of LLM options enables users to select the most suitable model for their specific needs, whether prioritizing local deployment, specialized capabilities, or cloud-based scalability.
 
+## Changing the default LLM
+The default LLM is provided through the `langchain openai` package, which is installed by default when you install CrewAI. You can change this default LLM to a different model or API by setting the `OPENAI_MODEL_NAME` environment variable. This straightforward process allows you to harness the power of different OpenAI models, enhancing the flexibility and capabilities of your CrewAI implementation.
 ```python
 # Required
 os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
@@ -46,29 +38,27 @@ example_agent = Agent(
 )
 ```
 
-## Ollama Integration
-Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below.
+## 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.
 
-### Setting Up Ollama
-- **Environment Variables Configuration**: To integrate Ollama, set the following environment variables:
 ```sh
-OPENAI_API_BASE='http://localhost:11434'
-OPENAI_MODEL_NAME='llama2'  # Adjust based on available model
-OPENAI_API_KEY=''
+os.environ[OPENAI_API_BASE]='http://localhost:11434'
+os.environ[OPENAI_MODEL_NAME]='llama2'  # Adjust based on available model
+os.environ[OPENAI_API_KEY]='' # No API Key required for Ollama
 ```
 
-## Ollama Integration (ex. for using Llama 2 locally)
-1. [Download Ollama](https://ollama.com/download).   
-2. After setting up the Ollama, Pull the Llama2 by typing following lines into the terminal ```ollama pull llama2```.   
-3. Enjoy your free Llama2 model that powered up by excellent agents from crewai.   
+## Ollama Integration Step by Step (ex. for using Llama 3.1 8B locally)
+1. [Download and install Ollama](https://ollama.com/download).
+2. After setting up the Ollama, Pull the Llama3.1 8B model by typing following lines into your terminal ```ollama run llama3.1```.
+3. Llama3.1 should now be served locally on `http://localhost:11434`
 ```
 from crewai import Agent, Task, Crew
-from langchain.llms import Ollama
+from langchain_ollama import ChatOllama
 import os
 os.environ["OPENAI_API_KEY"] = "NA"
 
-llm = Ollama(
-    model = "llama2",
+llm = ChatOllama(
+    model = "llama3.1",
     base_url = "http://localhost:11434")
 
 general_agent = Agent(role = "Math Professor",
@@ -85,7 +75,7 @@ task = Task(description="""what is 3 + 5""",
 crew = Crew(
             agents=[general_agent],
             tasks=[task],
-            verbose=2
+            verbose=True
         )
 
 result = crew.kickoff()
@@ -98,13 +88,14 @@ There are a couple of different ways you can use HuggingFace to host your LLM.
 
 ### Your own HuggingFace endpoint
 ```python
-from langchain_community.llms import HuggingFaceEndpoint
+from langchain_huggingface import HuggingFaceEndpoint
 
 llm = HuggingFaceEndpoint(
-    endpoint_url="<YOUR_ENDPOINT_URL_HERE>",
-    huggingfacehub_api_token="<HF_TOKEN_HERE>",
+    repo_id="microsoft/Phi-3-mini-4k-instruct",
     task="text-generation",
-    max_new_tokens=512
+    max_new_tokens=512,
+    do_sample=False,
+    repetition_penalty=1.03,
 )
 
 agent = Agent(
@@ -115,72 +106,55 @@ agent = Agent(
 )
 ```
 
-### From HuggingFaceHub endpoint
-```python
-from langchain_community.llms import HuggingFaceHub
-
-llm = HuggingFaceHub(
-    repo_id="HuggingFaceH4/zephyr-7b-beta",
-    huggingfacehub_api_token="<HF_TOKEN_HERE>",
-    task="text-generation",
-)
-```
-
 ## OpenAI Compatible API Endpoints
 Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, Groq, and Mistral AI.
 
 ### Configuration Examples
 #### FastChat
 ```sh
-OPENAI_API_BASE="http://localhost:8001/v1"
-OPENAI_MODEL_NAME='oh-2.5m7b-q51'
-OPENAI_API_KEY=NA
+os.environ["OPENAI_API_BASE"]='http://localhost:8001/v1'
+os.environ["OPENAI_MODEL_NAME"]='oh-2.5m7b-q51'
+os.environ[OPENAI_API_KEY]='NA'
 ```
 
 #### LM Studio
 Launch [LM Studio](https://lmstudio.ai) and go to the Server tab. Then select a model from the dropdown menu and wait for it to load. Once it's loaded, click the green Start Server button and use the URL, port, and API key that's shown (you can modify them). Below is an example of the default settings as of LM Studio 0.2.19:
 ```sh
-OPENAI_API_BASE="http://localhost:1234/v1"
-OPENAI_API_KEY="lm-studio"
+os.environ["OPENAI_API_BASE"]='http://localhost:1234/v1'
+os.environ["OPENAI_API_KEY"]='lm-studio'
 ```
 
 #### Groq API
 ```sh
-OPENAI_API_KEY=your-groq-api-key
-OPENAI_MODEL_NAME='llama3-8b-8192'
-OPENAI_API_BASE=https://api.groq.com/openai/v1
+os.environ["OPENAI_API_KEY"]='your-groq-api-key'
+os.environ["OPENAI_MODEL_NAME"]='llama3-8b-8192'
+os.environ["OPENAI_API_BASE"]='https://api.groq.com/openai/v1'
 ```
 
 #### Mistral API
 ```sh
-OPENAI_API_KEY=your-mistral-api-key
-OPENAI_API_BASE=https://api.mistral.ai/v1
-OPENAI_MODEL_NAME="mistral-small"
+os.environ["OPENAI_API_KEY"]='your-mistral-api-key'
+os.environ["OPENAI_API_BASE"]='https://api.mistral.ai/v1'
+os.environ["OPENAI_MODEL_NAME"]='mistral-small'
 ```
 
 ### Solar
-```python
+```sh
 from langchain_community.chat_models.solar import SolarChat
-# Initialize language model
-os.environ["SOLAR_API_KEY"] = "your-solar-api-key"
-llm = SolarChat(max_tokens=1024)
+```
+```sh
+os.environ[SOLAR_API_BASE]="https://api.upstage.ai/v1/solar"
+os.environ[SOLAR_API_KEY]="your-solar-api-key"
 
 # Free developer API key available here: https://console.upstage.ai/services/solar
 # Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
 ```
 
-### text-gen-web-ui
-```sh
-OPENAI_API_BASE=http://localhost:5000/v1
-OPENAI_MODEL_NAME=NA
-OPENAI_API_KEY=NA
-```
-
 ### Cohere
 ```python
 from langchain_cohere import ChatCohere
 # Initialize language model
-os.environ["COHERE_API_KEY"] = "your-cohere-api-key"
+os.environ["COHERE_API_KEY"]='your-cohere-api-key'
 llm = ChatCohere()
 
 # Free developer API key available here: https://cohere.com/
@@ -190,10 +164,11 @@ llm = ChatCohere()
 ### Azure Open AI Configuration
 For Azure OpenAI API integration, set the following environment variables:
 ```sh
-AZURE_OPENAI_VERSION="2022-12-01"
-AZURE_OPENAI_DEPLOYMENT=""
-AZURE_OPENAI_ENDPOINT=""
-AZURE_OPENAI_KEY=""
+
+os.environ["AZURE_OPENAI_DEPLOYMENT"]='Your deployment'
+os.environ["OPENAI_API_VERSION"]='2023-12-01-preview'
+os.environ["AZURE_OPENAI_ENDPOINT"]='Your Endpoint'
+os.environ["AZURE_OPENAI_API_KEY"]='Your API Key'
 ```
 
 ### Example Agent with Azure LLM

docs/how-to/Langtrace-Observability.md

@@ -7,10 +7,14 @@ description: How to monitor cost, latency, and performance of CrewAI Agents usin
 
 Langtrace is an open-source, external tool that helps you set up observability and evaluations for Large Language Models (LLMs), LLM frameworks, and Vector Databases. While not built directly into CrewAI, Langtrace can be used alongside CrewAI to gain deep visibility into the cost, latency, and performance of your CrewAI Agents. This integration allows you to log hyperparameters, monitor performance regressions, and establish a process for continuous improvement of your Agents.
 
+![Overview of a select series of agent session runs](..%2Fassets%2Flangtrace1.png)
+![Overview of agent traces](..%2Fassets%2Flangtrace2.png)
+![Overview of llm traces in details](..%2Fassets%2Flangtrace3.png)
+
 ## Setup Instructions
 
 1. Sign up for [Langtrace](https://langtrace.ai/) by visiting [https://langtrace.ai/signup](https://langtrace.ai/signup).
-2. Create a project and generate an API key.
+2. Create a project, set the project type to crewAI & generate an API key.
 3. Install Langtrace in your CrewAI project using the following commands:
 
 ```bash
@@ -32,58 +36,29 @@ langtrace.init(api_key='<LANGTRACE_API_KEY>')
 from crewai import Agent, Task, Crew
 ```
 
-2. Create your CrewAI agents and tasks as usual.
-
-3. Use Langtrace's tracking functions to monitor your CrewAI operations. For example:
-
-```python
-with langtrace.trace("CrewAI Task Execution"):
-    result = crew.kickoff()
-```
-
 ### Features and Their Application to CrewAI
 
 1. **LLM Token and Cost Tracking**
+
    - Monitor the token usage and associated costs for each CrewAI agent interaction.
-   - Example:
-     ```python
-     with langtrace.trace("Agent Interaction"):
-         agent_response = agent.execute(task)
-     ```
 
 2. **Trace Graph for Execution Steps**
+
    - Visualize the execution flow of your CrewAI tasks, including latency and logs.
    - Useful for identifying bottlenecks in your agent workflows.
 
 3. **Dataset Curation with Manual Annotation**
+
    - Create datasets from your CrewAI task outputs for future training or evaluation.
-   - Example:
-     ```python
-     langtrace.log_dataset_item(task_input, agent_output, {"task_type": "research"})
-     ```
 
 4. **Prompt Versioning and Management**
+
    - Keep track of different versions of prompts used in your CrewAI agents.
    - Useful for A/B testing and optimizing agent performance.
 
 5. **Prompt Playground with Model Comparisons**
+
    - Test and compare different prompts and models for your CrewAI agents before deployment.
 
 6. **Testing and Evaluations**
    - Set up automated tests for your CrewAI agents and tasks.
-   - Example:
-     ```python
-     langtrace.evaluate(agent_output, expected_output, "accuracy")
-     ```
-
-## Monitoring New CrewAI Features
-
-CrewAI has introduced several new features that can be monitored using Langtrace:
-
-1. **Code Execution**: Monitor the performance and output of code executed by agents.
-   ```python
-   with langtrace.trace("Agent Code Execution"):
-       code_output = agent.execute_code(code_snippet)
-   ```
-
-2. **Third-party Agent Integration**: Track interactions with LlamaIndex, LangChain, and Autogen agents.

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

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

docs/how-to/Sequential.md

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

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

@@ -1,137 +0,0 @@
----
-title: Starting a New CrewAI Project
-description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
----
-
-# Starting Your CrewAI Project
-
-Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
-
-## Prerequisites
-
-We assume you have already installed CrewAI. If not, please refer to the [installation guide](how-to/Installing-CrewAI.md) to install CrewAI and its dependencies.
-
-## Creating a New Project
-
-To create a new project, run the following CLI command:
-
-```shell
-$ crewai create my_project
-```
-
-This command will create a new project folder with the following structure:
-
-```shell
-my_project/
-├── .gitignore
-├── pyproject.toml
-├── README.md
-└── src/
-    └── my_project/
-        ├── __init__.py
-        ├── main.py
-        ├── crew.py
-        ├── tools/
-        │   ├── custom_tool.py
-        │   └── __init__.py
-        └── config/
-            ├── agents.yaml
-            └── tasks.yaml
-```
-
-You can now start developing your project by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of your project, and the `crew.py` file is where you define your agents and tasks.
-
-## Customizing Your Project
-
-To customize your project, you can:
-- Modify `src/my_project/config/agents.yaml` to define your agents.
-- Modify `src/my_project/config/tasks.yaml` to define your tasks.
-- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
-- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
-- Add your environment variables into the `.env` file.
-
-### Example: Defining Agents and Tasks
-
-#### agents.yaml
-
-```yaml
-researcher:
-  role: >
-    Job Candidate Researcher
-  goal: >
-    Find potential candidates for the job
-  backstory: >
-    You are adept at finding the right candidates by exploring various online
-    resources. Your skill in identifying suitable candidates ensures the best
-    match for job positions.
-```
-
-#### tasks.yaml
-
-```yaml
-research_candidates_task:
-  description: >
-    Conduct thorough research to find potential candidates for the specified job.
-    Utilize various online resources and databases to gather a comprehensive list of potential candidates.
-    Ensure that the candidates meet the job requirements provided.
-
-    Job Requirements:
-    {job_requirements}
-  expected_output: >
-    A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
-```
-
-## Installing Dependencies
-
-To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
-
-```shell
-$ cd my_project
-$ poetry lock
-$ poetry install
-```
-
-This will install the dependencies specified in the `pyproject.toml` file.
-
-## Interpolating Variables
-
-Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
-
-#### agents.yaml
-
-```yaml
-research_task:
-  description: >
-    Conduct a thorough research about the customer and competitors in the context
-    of {customer_domain}.
-    Make sure you find any interesting and relevant information given the
-    current year is 2024.
-  expected_output: >
-    A complete report on the customer and their customers and competitors,
-    including their demographics, preferences, market positioning and audience engagement.
-```
-
-#### main.py
-
-```python
-# main.py
-def run():
-    inputs = {
-        "customer_domain": "crewai.com"
-    }
-    MyProjectCrew(inputs).crew().kickoff(inputs=inputs)
-```
-
-## Running Your Project
-
-To run your project, use the following command:
-
-```shell
-$ poetry run my_project
-```
-
-This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
-
-## Deploying Your Project
-
-The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

docs/index.md

@@ -5,6 +5,26 @@
 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
 
 <div style="display:flex; margin:0 auto; justify-content: center;">
+    <div style="width:25%">
+        <h2>Getting Started</h2>
+        <ul>
+            <li>
+                <a href='./getting-started/Installing-CrewAI'>
+                   Installing CrewAI
+                </a>
+            </li>
+            <li>
+                <a href='./getting-started/Start-a-New-CrewAI-Project-Template-Method'>
+                   Start a New CrewAI Project: Template Method
+                </a>
+            </li>
+            <li>
+                <a href='./getting-started/Create-a-New-CrewAI-Pipeline-Template-Method'>
+                    Create a New CrewAI Pipeline: Template Method
+                </a>
+            </li>
+        </ul>
+    </div>
     <div style="width:25%">
         <h2>Core Concepts</h2>
         <ul>
@@ -33,6 +53,11 @@ 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
@@ -43,26 +68,21 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Memory
                 </a>
             </li>
+            <li>
+                <a href="./core-concepts/Planning">
+                    Planning
+                </a>
+            </li>
+            <li>
+                <a href="./core-concepts/Testing">
+                    Testing
+                </a>
+            </li>
         </ul>
     </div>
     <div style="width:30%">
         <h2>How-To Guides</h2>
         <ul>
-            <li>
-                <a href="./how-to/Start-a-New-CrewAI-Project">
-                    Starting Your crewAI Project
-                </a>
-            </li>
-            <li>
-                <a href="./how-to/Installing-CrewAI">
-                    Installing crewAI
-                </a>
-            </li>
-            <li>
-                <a href="./how-to/Creating-a-Crew-and-kick-it-off">
-                    Getting Started
-                </a>
-            </li>
             <li>
                 <a href="./how-to/Create-Custom-Tools">
                     Create Custom Tools
@@ -118,6 +138,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Replay from a Task
                 </a>
             </li>
+            <li>
+                <a href="./how-to/Conditional-Tasks">
+                    Conditional Tasks
+                </a>
+            </li>
             <li>
                 <a href="./how-to/AgentOps-Observability">
                     Agent Monitoring with AgentOps

docs/telemetry/Telemetry.md

@@ -5,24 +5,39 @@ description: Understanding the telemetry data collected by CrewAI and how it con
 
 ## Telemetry
 
+!!! note "Personal Information"
+    By default, we collect no data that would be considered personal information under GDPR and other privacy regulations.
+    We do collect Tool's names and Agent's roles, so be advised not to include any personal information in the tool's names or the Agent's roles.
+		Because no personal information is collected, it's not necessary to worry about data residency.
+		When `share_crew` is enabled, additional data is collected which may contain personal information if included by the user. Users should exercise caution when enabling this feature to ensure compliance with privacy regulations.
+
 CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
 
-It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy.
+It's pivotal to understand that by default, **NO personal data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables.
+When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights. This expanded data collection may include personal information if users have incorporated it into their crews or tasks. Users should carefully consider the content of their crews and tasks before enabling `share_crew`. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true.
 
-### Data Collected Includes:
-- **Version of CrewAI**: Assessing the adoption rate of our latest version helps us understand user needs and guide our updates.
-- **Python Version**: Identifying the Python versions our users operate with assists in prioritizing our support efforts for these versions.
-- **General OS Information**: Details like the number of CPUs and the operating system type (macOS, Windows, Linux) enable us to focus our development on the most used operating systems and explore the potential for OS-specific features.
-- **Number of Agents and Tasks in a Crew**: Ensures our internal testing mirrors real-world scenarios, helping us guide users towards best practices.
-- **Crew Process Utilization**: Understanding how crews are utilized aids in directing our development focus.
-- **Memory and Delegation Use by Agents**: Insights into how these features are used help evaluate their effectiveness and future.
-- **Task Execution Mode**: Knowing whether tasks are executed in parallel or sequentially influences our emphasis on enhancing parallel execution capabilities.
-- **Language Model Utilization**: Supports our goal to improve support for the most popular languages among our users.
-- **Roles of Agents within a Crew**: Understanding the various roles agents play aids in crafting better tools, integrations, and examples.
-- **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas.
+### Data Explanation:
+| Defaulted | Data                                      | Reason and Specifics                                                                                                       |
+|-----------|-------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|
+| Yes       | CrewAI and Python Version                 | Tracks software versions. Example: CrewAI v1.2.3, Python 3.8.10. No personal data. |
+| Yes       | Crew Metadata | Includes: randomly generated key and ID, process type (e.g., 'sequential', 'parallel'), boolean flag for memory usage (true/false), count of tasks, count of agents. All non-personal. |
+| Yes       | Agent Data | Includes: randomly generated key and ID, role name (should not include personal info), boolean settings (verbose, delegation enabled, code execution allowed), max iterations, max RPM, max retry limit, LLM info (see LLM Attributes), list of tool names (should not include personal info). No personal data. |
+| Yes       | Task Metadata | Includes: randomly generated key and ID, boolean execution settings (async_execution, human_input), associated agent's role and key, list of tool names. All non-personal. |
+| Yes       | Tool Usage Statistics | Includes: tool name (should not include personal info), number of usage attempts (integer), LLM attributes used. No personal data. |
+| Yes       | Test Execution Data | Includes: crew's randomly generated key and ID, number of iterations, model name used, quality score (float), execution time (in seconds). All non-personal. |
+| Yes       | Task Lifecycle Data | Includes: creation and execution start/end times, crew and task identifiers. Stored as spans with timestamps. No personal data. |
+| Yes       | LLM Attributes | Includes: name, model_name, model, top_k, temperature, and class name of the LLM. All technical, non-personal data. |
+| Yes       | Crew Deployment attempt using crewAI CLI | Includes: The fact a deploy is being made and crew id, and if it's trying to pull logs, no other data. |
+| No        | Agent's Expanded Data | Includes: goal description, backstory text, i18n prompt file identifier. Users should ensure no personal info is included in text fields. |
+| No        | Detailed Task Information | Includes: task description, expected output description, context references. Users should ensure no personal info is included in these fields. |
+| No        | Environment Information | Includes: platform, release, system, version, and CPU count. Example: 'Windows 10', 'x86_64'. No personal data. |
+| No        | Crew and Task Inputs and Outputs | Includes: input parameters and output results as non-identifiable data. Users should ensure no personal info is included. |
+| No        | Comprehensive Crew Execution Data | Includes: detailed logs of crew operations, all agents and tasks data, final output. All non-personal and technical in nature. |
+
+Note: "No" in the "Defaulted" column indicates that this data is only collected when `share_crew` is set to `true`.
 
 ### Opt-In Further Telemetry Sharing
-Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. This opt-in approach respects user privacy and aligns with data protection standards by ensuring users have control over their data sharing preferences. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
+Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns.
 
-### Updates and Revisions
-We are committed to maintaining the accuracy and transparency of our documentation. Regular reviews and updates are performed to ensure our documentation accurately reflects the latest developments of our codebase and telemetry practices. Users are encouraged to review this section for the most current information on our data collection practices and how they contribute to the improvement of CrewAI.
+!!! warning "Potential Personal Information"
+    If you enable `share_crew`, the collected data may include personal information if it has been incorporated into crew configurations, task descriptions, or outputs. Users should carefully review their data and ensure compliance with GDPR and other applicable privacy regulations before enabling this feature.

docs/tools/CodeInterpreterTool.md

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

docs/tools/ComposioTool.md

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

docs/tools/DALL-ETool.md

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

docs/tools/FileWriteTool.md

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

docs/tools/FirecrawlCrawlWebsiteTool.md

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

docs/tools/FirecrawlScrapeWebsiteTool.md

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

docs/tools/FirecrawlSearchTool.md

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

docs/tools/MySQLTool.md

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

docs/tools/NL2SQLTool.md

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

docs/tools/SerperDevTool.md

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

docs/tools/SpiderTool.md

@@ -0,0 +1,81 @@
+# SpiderTool
+
+## Description
+
+[Spider](https://spider.cloud/?ref=crewai) is the [fastest](https://github.com/spider-rs/spider/blob/main/benches/BENCHMARKS.md#benchmark-results) open source scraper and crawler that returns LLM-ready data. It converts any website into pure HTML, markdown, metadata or text while enabling you to crawl with custom actions using AI.
+
+## Installation
+
+To use the Spider API you need to download the [Spider SDK](https://pypi.org/project/spider-client/) and the crewai[tools] SDK too:
+
+```python
+pip install spider-client 'crewai[tools]'
+```
+
+## Example
+
+This example shows you how you can use the Spider tool to enable your agent to scrape and crawl websites. The data returned from the Spider API is already LLM-ready, so no need to do any cleaning there.
+
+```python
+from crewai_tools import SpiderTool
+
+def main():
+    spider_tool = SpiderTool()
+
+    searcher = Agent(
+        role="Web Research Expert",
+        goal="Find related information from specific URL's",
+        backstory="An expert web researcher that uses the web extremely well",
+        tools=[spider_tool],
+        verbose=True,
+    )
+
+    return_metadata = Task(
+        description="Scrape https://spider.cloud with a limit of 1 and enable metadata",
+        expected_output="Metadata and 10 word summary of spider.cloud",
+        agent=searcher
+    )
+
+    crew = Crew(
+        agents=[searcher],
+        tasks=[
+            return_metadata,
+        ],
+        verbose=2
+    )
+
+    crew.kickoff()
+
+if __name__ == "__main__":
+    main()
+```
+
+## Arguments
+
+- `api_key` (string, optional): Specifies Spider API key. If not specified, it looks for `SPIDER_API_KEY` in environment variables.
+- `params` (object, optional): Optional parameters for the request. Defaults to `{"return_format": "markdown"}` to return the website's content in a format that fits LLMs better.
+    - `request` (string): The request type to perform. Possible values are `http`, `chrome`, and `smart`. Use `smart` to perform an HTTP request by default until JavaScript rendering is needed for the HTML.
+    - `limit` (int): The maximum number of pages allowed to crawl per website. Remove the value or set it to `0` to crawl all pages.
+    - `depth` (int): The crawl limit for maximum depth. If `0`, no limit will be applied.
+    - `cache` (bool): Use HTTP caching for the crawl to speed up repeated runs. Default is `true`.
+    - `budget` (object): Object that has paths with a counter for limiting the amount of pages example `{"*":1}` for only crawling the root page.
+    - `locale` (string): The locale to use for request, example `en-US`.
+    - `cookies` (string): Add HTTP cookies to use for request.
+    - `stealth` (bool): Use stealth mode for headless chrome request to help prevent being blocked. The default is `true` on chrome.
+    - `headers` (object): Forward HTTP headers to use for all request. The object is expected to be a map of key value pairs.
+    - `metadata` (bool): Boolean to store metadata about the pages and content found. This could help improve AI interopt. Defaults to `false` unless you have the website already stored with the configuration enabled.
+    - `viewport` (object): Configure the viewport for chrome. Defaults to `800x600`.
+    - `encoding` (string): The type of encoding to use like `UTF-8`, `SHIFT_JIS`, or etc.
+    - `subdomains` (bool): Allow subdomains to be included. Default is `false`.
+    - `user_agent` (string): Add a custom HTTP user agent to the request. By default this is set to a random agent.
+    - `store_data` (bool): Boolean to determine if storage should be used. If set this takes precedence over `storageless`. Defaults to `false`.
+    - `gpt_config` (object): Use AI to generate actions to perform during the crawl. You can pass an array for the `"prompt"` to chain steps.
+    - `fingerprint` (bool): Use advanced fingerprint for chrome.
+    - `storageless` (bool): Boolean to prevent storing any type of data for the request including storage and AI vectors embedding. Defaults to `false` unless you have the website already stored.
+    - `readability` (bool): Use [readability](https://github.com/mozilla/readability) to pre-process the content for reading. This may drastically improve the content for LLM usage.
+    `return_format` (string): The format to return the data in. Possible values are `markdown`, `raw`, `text`, and `html2text`. Use `raw` to return the default format of the page like HTML etc.
+    - `proxy_enabled` (bool): Enable high performance premium proxies for the request to prevent being blocked at the network level.
+    - `query_selector` (string): The CSS query selector to use when extracting content from the markup.
+    - `full_resources` (bool): Crawl and download all the resources for a website.
+    - `request_timeout` (int): The timeout to use for request. Timeouts can be from `5-60`. The default is `30` seconds.
+    - `run_in_background` (bool): Run the request in the background. Useful if storing data and wanting to trigger crawls to the dashboard. This has no effect if storageless is set.

docs/tools/VisionTool.md

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

mkdocs.yml

@@ -2,8 +2,8 @@ site_name: crewAI
 site_author: crewAI, Inc
 site_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.
 repo_name: crewAI
-repo_url: https://github.com/joaomdmoura/crewai/
-site_url: https://crewai.com
+repo_url: https://github.com/crewAIInc/crewAI
+site_url: https://docs.crewai.com
 edit_uri: edit/main/docs/
 copyright: Copyright © 2024 crewAI, Inc
 
@@ -119,6 +119,9 @@ theme:
 
 nav:
   - Home: '/'
+  - Getting Started:
+    - Installing CrewAI: 'getting-started/Installing-CrewAI.md'
+    - Starting a new CrewAI project: 'getting-started/Start-a-New-CrewAI-Project-Template-Method.md'
   - Core Concepts:
     - Agents: 'core-concepts/Agents.md'
     - Tasks: 'core-concepts/Tasks.md'
@@ -126,14 +129,14 @@ nav:
     - Processes: 'core-concepts/Processes.md'
     - Crews: 'core-concepts/Crews.md'
     - Collaboration: 'core-concepts/Collaboration.md'
+    - Pipeline: 'core-concepts/Pipeline.md'
     - Training: 'core-concepts/Training-Crew.md'
     - Memory: 'core-concepts/Memory.md'
+    - Planning: 'core-concepts/Planning.md'
+    - Testing: 'core-concepts/Testing.md'
     - Using LangChain Tools: 'core-concepts/Using-LangChain-Tools.md'
     - Using LlamaIndex Tools: 'core-concepts/Using-LlamaIndex-Tools.md'
   - How to Guides:
-    - Starting Your crewAI Project: 'how-to/Start-a-New-CrewAI-Project.md'
-    - Installing CrewAI: 'how-to/Installing-CrewAI.md'
-    - Getting Started: 'how-to/Creating-a-Crew-and-kick-it-off.md'
     - Create Custom Tools: 'how-to/Create-Custom-Tools.md'
     - Using Sequential Process: 'how-to/Sequential.md'
     - Using Hierarchical Process: 'how-to/Hierarchical.md'
@@ -146,32 +149,42 @@ nav:
     - Kickoff a Crew Asynchronously: 'how-to/Kickoff-async.md'
     - Kickoff a Crew for a List: 'how-to/Kickoff-for-each.md'
     - Replay from a specific task from a kickoff: 'how-to/Replay-tasks-from-latest-Crew-Kickoff.md'
+    - Conditional Tasks: 'how-to/Conditional-Tasks.md'
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
   - Tools Docs:
-    - Google Serper Search: 'tools/SerperDevTool.md'
     - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
-    - Composio Tools: 'tools/ComposioTool.md'
-    - Code Interpreter: 'tools/CodeInterpreterTool.md'
-    - Scrape Website: 'tools/ScrapeWebsiteTool.md'
-    - Directory Read: 'tools/DirectoryReadTool.md'
-    - Exa Serch Web Loader: 'tools/EXASearchTool.md'
-    - File Read: 'tools/FileReadTool.md'
-    - Selenium Scraper: 'tools/SeleniumScrapingTool.md'
-    - Directory RAG Search: 'tools/DirectorySearchTool.md'
-    - PDF RAG Search: 'tools/PDFSearchTool.md'
-    - TXT RAG Search: 'tools/TXTSearchTool.md'
-    - CSV RAG Search: 'tools/CSVSearchTool.md'
-    - XML RAG Search: 'tools/XMLSearchTool.md'
-    - JSON RAG Search: 'tools/JSONSearchTool.md'
-    - Docx Rag Search: 'tools/DOCXSearchTool.md'
-    - MDX RAG Search: 'tools/MDXSearchTool.md'
-    - PG RAG Search: 'tools/PGSearchTool.md'
-    - Website RAG Search: 'tools/WebsiteSearchTool.md'
-    - Github RAG Search: 'tools/GitHubSearchTool.md'
     - Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'
-    - Youtube Video RAG Search: 'tools/YoutubeVideoSearchTool.md'
+    - Code Interpreter: 'tools/CodeInterpreterTool.md'
+    - Composio Tools: 'tools/ComposioTool.md'
+    - CSV RAG Search: 'tools/CSVSearchTool.md'
+    - DALL-E Tool: 'tools/DALL-ETool.md'
+    - Directory RAG Search: 'tools/DirectorySearchTool.md'
+    - Directory Read: 'tools/DirectoryReadTool.md'
+    - Docx Rag Search: 'tools/DOCXSearchTool.md'
+    - EXA Serch Web Loader: 'tools/EXASearchTool.md'
+    - File Read: 'tools/FileReadTool.md'
+    - File Write: 'tools/FileWriteTool.md'
+    - Firecrawl Crawl Website Tool: 'tools/FirecrawlCrawlWebsiteTool.md'
+    - Firecrawl Scrape Website Tool: 'tools/FirecrawlScrapeWebsiteTool.md'
+    - Firecrawl Search Tool: 'tools/FirecrgstawlSearchTool.md'
+    - Github RAG Search: 'tools/GitHubSearchTool.md'
+    - Google Serper Search: 'tools/SerperDevTool.md'
+    - JSON RAG Search: 'tools/JSONSearchTool.md'
+    - MDX RAG Search: 'tools/MDXSearchTool.md'
+    - MySQL Tool: 'tools/MySQLTool.md'
+    - NL2SQL Tool: 'tools/NL2SQLTool.md'
+    - PDF RAG Search: 'tools/PDFSearchTool.md'
+    - PG RAG Search: 'tools/PGSearchTool.md'
+    - Scrape Website: 'tools/ScrapeWebsiteTool.md'
+    - Selenium Scraper: 'tools/SeleniumScrapingTool.md'
+    - Spider Scraper: 'tools/SpiderTool.md'
+    - TXT RAG Search: 'tools/TXTSearchTool.md'
+    - Vision Tool: 'tools/VisionTool.md'
+    - Website RAG Search: 'tools/WebsiteSearchTool.md'
+    - XML RAG Search: 'tools/XMLSearchTool.md'
     - Youtube Channel RAG Search: 'tools/YoutubeChannelSearchTool.md'
+    - Youtube Video RAG Search: 'tools/YoutubeVideoSearchTool.md'
   - Examples:
     - Trip Planner Crew: https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner"
     - Create Instagram Post: https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"
@@ -181,6 +194,7 @@ nav:
     - Landing Page Generator: https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator"
     - Prepare for meetings: https://github.com/joaomdmoura/crewAI-examples/tree/main/prep-for-a-meeting"
   - Telemetry: 'telemetry/Telemetry.md'
+  - Change Log: 'https://github.com/crewAIInc/crewAI/releases'
 
 extra_css:
   - stylesheets/output.css

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.36.0"
+version = "0.55.2"
 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"
@@ -8,8 +8,8 @@ packages = [{ include = "crewai", from = "src" }]
 
 [tool.poetry.urls]
 Homepage = "https://crewai.com"
-Documentation = "https://github.com/joaomdmoura/CrewAI/wiki/Index"
-Repository = "https://github.com/joaomdmoura/crewai"
+Documentation = "https://docs.crewai.com"
+Repository = "https://github.com/crewAIInc/crewAI"
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
@@ -20,15 +20,16 @@ opentelemetry-api = "^1.22.0"
 opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
-regex = "^2023.12.25"
-crewai-tools = { version = "^0.4.8", optional = true }
+regex = "^2024.7.24"
+crewai-tools = { version = "^0.12.0", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
 jsonref = "^1.1.0"
-agentops = { version = "^0.1.9", optional = true }
+agentops = { version = "^0.3.0", optional = true }
 embedchain = "^0.1.114"
 json-repair = "^0.25.2"
+auth0-python = "^4.7.1"
 
 [tool.poetry.extras]
 tools = ["crewai-tools"]
@@ -46,12 +47,13 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.4.8"
+crewai-tools = "^0.12.0"
 
 [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"
@@ -59,7 +61,10 @@ crewai = "crewai.cli.cli:crewai"
 [tool.mypy]
 ignore_missing_imports = true
 disable_error_code = 'import-untyped'
-exclude = ["cli/templates/main.py", "cli/templates/crew.py"]
+exclude = ["cli/templates"]
+
+[tool.bandit]
+exclude_dirs = ["src/crewai/cli/templates"]
 
 [build-system]
 requires = ["poetry-core"]

src/crewai/__init__.py

@@ -1,6 +1,8 @@
 from crewai.agent import Agent
 from crewai.crew import Crew
+from crewai.pipeline import Pipeline
 from crewai.process import Process
+from crewai.routers import Router
 from crewai.task import Task
 
-__all__ = ["Agent", "Crew", "Process", "Task"]
+__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router"]

src/crewai/agent.py

@@ -19,18 +19,28 @@ from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_F
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-agentops = None
-try:
-    import agentops  # type: ignore # Name "agentops" already defined on line 21
-    from agentops import track_agent
-except ImportError:
 
-    def track_agent():
+def mock_agent_ops_provider():
+    def track_agent(*args, **kwargs):
         def noop(f):
             return f
 
         return noop
 
+    return track_agent
+
+
+agentops = None
+
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        import agentops  # type: ignore # Name "agentops" already defined on line 21
+        from agentops import track_agent
+    except ImportError:
+        track_agent = mock_agent_ops_provider()
+else:
+    track_agent = mock_agent_ops_provider()
+
 
 @track_agent()
 class Agent(BaseAgent):
@@ -55,8 +65,6 @@ class Agent(BaseAgent):
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
             callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
-            allow_code_execution: Enable code execution for the agent.
-            max_retry_limit: Maximum number of retries for an agent to execute a task when an error occurs.
     """
 
     _times_executed: int = PrivateAttr(default=0)
@@ -105,40 +113,46 @@ class Agent(BaseAgent):
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
     )
 
-    def __init__(__pydantic_self__, **data):
-        config = data.pop("config", {})
-        super().__init__(**config, **data)
-        __pydantic_self__.agent_ops_agent_name = __pydantic_self__.role
-
     @model_validator(mode="after")
-    def set_agent_executor(self) -> "Agent":
-        """Ensure agent executor and token process are set."""
-        if hasattr(self.llm, "model_name"):
-            token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)
+    def post_init_setup(self):
+        self.agent_ops_agent_name = self.role
 
-            # Ensure self.llm.callbacks is a list
-            if not isinstance(self.llm.callbacks, list):
-                self.llm.callbacks = []
+        # Different llms store the model name in different attributes
+        model_name = getattr(self.llm, "model_name", None) or getattr(
+            self.llm, "deployment_name", None
+        )
 
-            # Check if an instance of TokenCalcHandler already exists in the list
-            if not any(
-                isinstance(handler, TokenCalcHandler) for handler in self.llm.callbacks
-            ):
-                self.llm.callbacks.append(token_handler)
-
-            if agentops and not any(
-                isinstance(handler, agentops.LangchainCallbackHandler)
-                for handler in self.llm.callbacks
-            ):
-                agentops.stop_instrumenting()
-                self.llm.callbacks.append(agentops.LangchainCallbackHandler())
+        if model_name:
+            self._setup_llm_callbacks(model_name)
 
         if not self.agent_executor:
-            if not self.cache_handler:
-                self.cache_handler = CacheHandler()
-            self.set_cache_handler(self.cache_handler)
+            self._setup_agent_executor()
+
         return self
 
+    def _setup_llm_callbacks(self, model_name: str):
+        token_handler = TokenCalcHandler(model_name, self._token_process)
+
+        if not isinstance(self.llm.callbacks, list):
+            self.llm.callbacks = []
+
+        if not any(
+            isinstance(handler, TokenCalcHandler) for handler in self.llm.callbacks
+        ):
+            self.llm.callbacks.append(token_handler)
+
+        if agentops and not any(
+            isinstance(handler, agentops.LangchainCallbackHandler)
+            for handler in self.llm.callbacks
+        ):
+            agentops.stop_instrumenting()
+            self.llm.callbacks.append(agentops.LangchainCallbackHandler())
+
+    def _setup_agent_executor(self):
+        if not self.cache_handler:
+            self.cache_handler = CacheHandler()
+        self.set_cache_handler(self.cache_handler)
+
     def execute_task(
         self,
         task: Any,
@@ -181,7 +195,6 @@ class Agent(BaseAgent):
         self.agent_executor.tools = parsed_tools
         self.agent_executor.task = task
 
-        # TODO: COMPARE WITH ARGS AND WITHOUT ARGS
         self.agent_executor.tools_description = self._render_text_description_and_args(
             parsed_tools
         )
@@ -204,9 +217,9 @@ class Agent(BaseAgent):
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 raise e
-            self.execute_task(task, context, tools)
+            result = self.execute_task(task, context, tools)
 
-        if self.max_rpm:
+        if self.max_rpm and self._rpm_controller:
             self._rpm_controller.stop_rpm_counter()
 
         # If there was any tool in self.tools_results that had result_as_answer
@@ -263,6 +276,7 @@ class Agent(BaseAgent):
             "tools_handler": self.tools_handler,
             "function_calling_llm": self.function_calling_llm,
             "callbacks": self.callbacks,
+            "max_tokens": self.max_tokens,
         }
 
         if self._rpm_controller:

src/crewai/agents/__init__.py

@@ -2,3 +2,5 @@ from .cache.cache_handler import CacheHandler
 from .executor import CrewAgentExecutor
 from .parser import CrewAgentParser
 from .tools_handler import ToolsHandler
+
+__all__ = ["CacheHandler", "CrewAgentExecutor", "CrewAgentParser", "ToolsHandler"]

src/crewai/agents/agent_builder/base_agent.py

@@ -1,12 +1,12 @@
 import uuid
 from abc import ABC, abstractmethod
 from copy import copy as shallow_copy
+from hashlib import md5
 from typing import Any, Dict, List, Optional, TypeVar
 
 from pydantic import (
     UUID4,
     BaseModel,
-    ConfigDict,
     Field,
     InstanceOf,
     PrivateAttr,
@@ -19,6 +19,7 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.utilities import I18N, Logger, RPMController
+from crewai.utilities.config import process_config
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -44,6 +45,7 @@ class BaseAgent(ABC, BaseModel):
         i18n (I18N): Internationalization settings.
         cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class.
         tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class.
+        max_tokens: Maximum number of tokens for the agent to generate in a response.
 
 
     Methods:
@@ -72,21 +74,26 @@ class BaseAgent(ABC, BaseModel):
     """
 
     __hash__ = object.__hash__  # type: ignore
-    _logger: Logger = PrivateAttr()
-    _rpm_controller: RPMController = PrivateAttr(default=None)
+    _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
+    _rpm_controller: Optional[RPMController] = PrivateAttr(default=None)
     _request_within_rpm_limit: Any = PrivateAttr(default=None)
-    formatting_errors: int = 0
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    _original_role: Optional[str] = PrivateAttr(default=None)
+    _original_goal: Optional[str] = PrivateAttr(default=None)
+    _original_backstory: Optional[str] = PrivateAttr(default=None)
+    _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
+    formatting_errors: int = Field(
+        default=0, description="Number of formatting errors."
+    )
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Objective of the agent")
     backstory: str = Field(description="Backstory of the agent")
+    config: Optional[Dict[str, Any]] = Field(
+        description="Configuration for the agent", default=None, exclude=True
+    )
     cache: bool = Field(
         default=True, description="Whether the agent should use a cache for tool usage."
     )
-    config: Optional[Dict[str, Any]] = Field(
-        description="Configuration for the agent", default=None
-    )
     verbose: bool = Field(
         default=False, description="Verbose mode for the Agent Execution"
     )
@@ -117,21 +124,33 @@ class BaseAgent(ABC, BaseModel):
     tools_handler: InstanceOf[ToolsHandler] = Field(
         default=None, description="An instance of the ToolsHandler class."
     )
+    max_tokens: Optional[int] = Field(
+        default=None, description="Maximum number of tokens for the agent's execution."
+    )
 
-    _original_role: str | None = None
-    _original_goal: str | None = None
-    _original_backstory: str | None = None
-    _token_process: TokenProcess = TokenProcess()
-
-    def __init__(__pydantic_self__, **data):
-        config = data.pop("config", {})
-        super().__init__(**config, **data)
+    @model_validator(mode="before")
+    @classmethod
+    def process_model_config(cls, values):
+        return process_config(values, cls)
 
     @model_validator(mode="after")
-    def set_config_attributes(self):
-        if self.config:
-            for key, value in self.config.items():
-                setattr(self, key, value)
+    def validate_and_set_attributes(self):
+        # Validate required fields
+        for field in ["role", "goal", "backstory"]:
+            if getattr(self, field) is None:
+                raise ValueError(
+                    f"{field} must be provided either directly or through config"
+                )
+
+        # Set private attributes
+        self._logger = Logger(verbose=self.verbose)
+        if self.max_rpm and not self._rpm_controller:
+            self._rpm_controller = RPMController(
+                max_rpm=self.max_rpm, logger=self._logger
+            )
+        if not self._token_process:
+            self._token_process = TokenProcess()
+
         return self
 
     @field_validator("id", mode="before")
@@ -142,18 +161,10 @@ class BaseAgent(ABC, BaseModel):
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
 
-    @model_validator(mode="after")
-    def set_attributes_based_on_config(self) -> "BaseAgent":
-        """Set attributes based on the agent configuration."""
-        if self.config:
-            for key, value in self.config.items():
-                setattr(self, key, value)
-        return self
-
     @model_validator(mode="after")
     def set_private_attrs(self):
         """Set private attributes."""
-        self._logger = Logger(self.verbose)
+        self._logger = Logger(verbose=self.verbose)
         if self.max_rpm and not self._rpm_controller:
             self._rpm_controller = RPMController(
                 max_rpm=self.max_rpm, logger=self._logger
@@ -162,6 +173,11 @@ class BaseAgent(ABC, BaseModel):
             self._token_process = TokenProcess()
         return self
 
+    @property
+    def key(self):
+        source = [self.role, self.goal, self.backstory]
+        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
+
     @abstractmethod
     def execute_task(
         self,

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

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

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

@@ -1,4 +1,4 @@
-from typing import Any, Dict
+from crewai.types.usage_metrics import UsageMetrics
 
 
 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) -> Dict[str, Any]:
-        return {
-            "total_tokens": self.total_tokens,
-            "prompt_tokens": self.prompt_tokens,
-            "completion_tokens": self.completion_tokens,
-            "successful_requests": self.successful_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,
+        )

src/crewai/agents/cache/__init__.py

@@ -1 +1,3 @@
 from .cache_handler import CacheHandler
+
+__all__ = ["CacheHandler"]

src/crewai/agents/cache/cache_handler.py

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

src/crewai/agents/executor.py

@@ -1,10 +1,13 @@
 import threading
 import time
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
 
+import click
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
 from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.chains.summarize import load_summarize_chain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_core.agents import AgentAction, AgentFinish, AgentStep
 from langchain_core.exceptions import OutputParserException
 from langchain_core.tools import BaseTool
@@ -16,6 +19,10 @@ from crewai.agents.tools_handler import ToolsHandler
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N
 from crewai.utilities.constants import TRAINING_DATA_FILE
+from crewai.utilities.exceptions.context_window_exceeding_exception import (
+    LLMContextLengthExceededException,
+)
+from crewai.utilities.logger import Logger
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
@@ -40,6 +47,8 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
     response_template: Optional[str] = None
+    _logger: Logger = Logger()
+    _fit_context_window_strategy: Optional[Literal["summarize"]] = "summarize"
 
     def _call(
         self,
@@ -56,7 +65,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
         )
         intermediate_steps: List[Tuple[AgentAction, str]] = []
         # Allowing human input given task setting
-        if self.task.human_input:
+        if self.task and self.task.human_input:
             self.should_ask_for_human_input = True
 
         # Let's start tracking the number of iterations and time elapsed
@@ -131,7 +140,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
 
             # Call the LLM to see what to do.
-            output = self.agent.plan(  # type: ignore #  Incompatible types in assignment (expression has type "AgentAction | AgentFinish | list[AgentAction]", variable has type "AgentAction")
+            output = self.agent.plan(
                 intermediate_steps,
                 callbacks=run_manager.get_child() if run_manager else None,
                 **inputs,
@@ -185,6 +194,23 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             yield AgentStep(action=output, observation=observation)
             return
 
+        except Exception as e:
+            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
+                str(e)
+            ):
+                output = self._handle_context_length_error(
+                    intermediate_steps, run_manager, inputs
+                )
+
+                if isinstance(output, AgentFinish):
+                    yield output
+                elif isinstance(output, list):
+                    for step in output:
+                        yield step
+                return
+
+            raise e
+
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):
             if self.should_ask_for_human_input:
@@ -235,6 +261,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                 agent=self.crew_agent,
                 action=agent_action,
             )
+
             tool_calling = tool_usage.parse(agent_action.log)
 
             if isinstance(tool_calling, ToolUsageErrorException):
@@ -242,6 +269,8 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             else:
                 if tool_calling.tool_name.casefold().strip() in [
                     name.casefold().strip() for name in name_to_tool_map
+                ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+                    name.casefold().strip() for name in name_to_tool_map
                 ]:
                     observation = tool_usage.use(tool_calling, agent_action.log)
                 else:
@@ -278,3 +307,91 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             CrewTrainingHandler(TRAINING_DATA_FILE).append(
                 self.crew._train_iteration, agent_id, training_data
             )
+
+    def _handle_context_length(
+        self, intermediate_steps: List[Tuple[AgentAction, str]]
+    ) -> List[Tuple[AgentAction, str]]:
+        text = intermediate_steps[0][1]
+        original_action = intermediate_steps[0][0]
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            separators=["\n\n", "\n"],
+            chunk_size=8000,
+            chunk_overlap=500,
+        )
+
+        if self._fit_context_window_strategy == "summarize":
+            docs = text_splitter.create_documents([text])
+            self._logger.log(
+                "debug",
+                "Summarizing Content, it is recommended to use a RAG tool",
+                color="bold_blue",
+            )
+            summarize_chain = load_summarize_chain(
+                self.llm, chain_type="map_reduce", verbose=True
+            )
+            summarized_docs = []
+            for doc in docs:
+                summary = summarize_chain.invoke(
+                    {"input_documents": [doc]}, return_only_outputs=True
+                )
+
+                summarized_docs.append(summary["output_text"])
+
+            formatted_results = "\n\n".join(summarized_docs)
+            summary_step = AgentStep(
+                action=AgentAction(
+                    tool=original_action.tool,
+                    tool_input=original_action.tool_input,
+                    log=original_action.log,
+                ),
+                observation=formatted_results,
+            )
+            summary_tuple = (summary_step.action, summary_step.observation)
+            return [summary_tuple]
+
+        return intermediate_steps
+
+    def _handle_context_length_error(
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        run_manager: Optional[CallbackManagerForChainRun],
+        inputs: Dict[str, str],
+    ) -> Union[AgentFinish, List[AgentStep]]:
+        self._logger.log(
+            "debug",
+            "Context length exceeded. Asking user if they want to use summarize prompt to fit, this will reduce context length.",
+            color="yellow",
+        )
+        user_choice = click.confirm(
+            "Context length exceeded. Do you want to summarize the text to fit models context window?"
+        )
+        if user_choice:
+            self._logger.log(
+                "debug",
+                "Context length exceeded. Using summarize prompt to fit, this will reduce context length.",
+                color="bold_blue",
+            )
+            intermediate_steps = self._handle_context_length(intermediate_steps)
+
+            output = self.agent.plan(
+                intermediate_steps,
+                callbacks=run_manager.get_child() if run_manager else None,
+                **inputs,
+            )
+
+            if isinstance(output, AgentFinish):
+                return output
+            elif isinstance(output, AgentAction):
+                return [AgentStep(action=output, observation=None)]
+            else:
+                return [AgentStep(action=action, observation=None) for action in output]
+        else:
+            self._logger.log(
+                "debug",
+                "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
+                color="red",
+            )
+            raise SystemExit(
+                "Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
+            )

src/crewai/cli/authentication/__init__.py

@@ -0,0 +1,3 @@
+from .main import AuthenticationCommand
+
+__all__ = ["AuthenticationCommand"]

src/crewai/cli/authentication/constants.py

@@ -0,0 +1,4 @@
+ALGORITHMS = ["RS256"]
+AUTH0_DOMAIN = "crewai.us.auth0.com"
+AUTH0_CLIENT_ID = "DEVC5Fw6NlRoSzmDCcOhVq85EfLBjKa8"
+AUTH0_AUDIENCE = "https://crewai.us.auth0.com/api/v2/"

src/crewai/cli/authentication/main.py

@@ -0,0 +1,75 @@
+import time
+import webbrowser
+from typing import Any, Dict
+
+import requests
+from rich.console import Console
+
+from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN
+from .utils import TokenManager, validate_token
+
+console = Console()
+
+
+class AuthenticationCommand:
+    DEVICE_CODE_URL = f"https://{AUTH0_DOMAIN}/oauth/device/code"
+    TOKEN_URL = f"https://{AUTH0_DOMAIN}/oauth/token"
+
+    def __init__(self):
+        self.token_manager = TokenManager()
+
+    def signup(self) -> None:
+        """Sign up to CrewAI+"""
+        console.print("Signing Up to CrewAI+ \n", style="bold blue")
+        device_code_data = self._get_device_code()
+        self._display_auth_instructions(device_code_data)
+
+        return self._poll_for_token(device_code_data)
+
+    def _get_device_code(self) -> Dict[str, Any]:
+        """Get the device code to authenticate the user."""
+
+        device_code_payload = {
+            "client_id": AUTH0_CLIENT_ID,
+            "scope": "openid",
+            "audience": AUTH0_AUDIENCE,
+        }
+        response = requests.post(url=self.DEVICE_CODE_URL, data=device_code_payload)
+        response.raise_for_status()
+        return response.json()
+
+    def _display_auth_instructions(self, device_code_data: Dict[str, str]) -> None:
+        """Display the authentication instructions to the user."""
+        console.print("1. Navigate to: ", device_code_data["verification_uri_complete"])
+        console.print("2. Enter the following code: ", device_code_data["user_code"])
+        webbrowser.open(device_code_data["verification_uri_complete"])
+
+    def _poll_for_token(self, device_code_data: Dict[str, Any]) -> None:
+        """Poll the server for the token."""
+        token_payload = {
+            "grant_type": "urn:ietf:params:oauth:grant-type:device_code",
+            "device_code": device_code_data["device_code"],
+            "client_id": AUTH0_CLIENT_ID,
+        }
+
+        attempts = 0
+        while True and attempts < 5:
+            response = requests.post(self.TOKEN_URL, data=token_payload)
+            token_data = response.json()
+
+            if response.status_code == 200:
+                validate_token(token_data["id_token"])
+                expires_in = 360000  # Token expiration time in seconds
+                self.token_manager.save_tokens(token_data["access_token"], expires_in)
+                console.print("\nWelcome to CrewAI+ !!", style="green")
+                return
+
+            if token_data["error"] not in ("authorization_pending", "slow_down"):
+                raise requests.HTTPError(token_data["error_description"])
+
+            time.sleep(device_code_data["interval"])
+            attempts += 1
+
+        console.print(
+            "Timeout: Failed to get the token. Please try again.", style="bold red"
+        )

src/crewai/cli/authentication/utils.py

@@ -0,0 +1,144 @@
+import json
+import os
+import sys
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Optional
+
+from auth0.authentication.token_verifier import (
+    AsymmetricSignatureVerifier,
+    TokenVerifier,
+)
+from cryptography.fernet import Fernet
+
+from .constants import AUTH0_CLIENT_ID, AUTH0_DOMAIN
+
+
+def validate_token(id_token: str) -> None:
+    """
+    Verify the token and its precedence
+
+    :param id_token:
+    """
+    jwks_url = f"https://{AUTH0_DOMAIN}/.well-known/jwks.json"
+    issuer = f"https://{AUTH0_DOMAIN}/"
+    signature_verifier = AsymmetricSignatureVerifier(jwks_url)
+    token_verifier = TokenVerifier(
+        signature_verifier=signature_verifier, issuer=issuer, audience=AUTH0_CLIENT_ID
+    )
+    token_verifier.verify(id_token)
+
+
+class TokenManager:
+    def __init__(self, file_path: str = "tokens.enc") -> None:
+        """
+        Initialize the TokenManager class.
+
+        :param file_path: The file path to store the encrypted tokens. Default is "tokens.enc".
+        """
+        self.file_path = file_path
+        self.key = self._get_or_create_key()
+        self.fernet = Fernet(self.key)
+
+    def _get_or_create_key(self) -> bytes:
+        """
+        Get or create the encryption key.
+
+        :return: The encryption key.
+        """
+        key_filename = "secret.key"
+        key = self.read_secure_file(key_filename)
+
+        if key is not None:
+            return key
+
+        new_key = Fernet.generate_key()
+        self.save_secure_file(key_filename, new_key)
+        return new_key
+
+    def save_tokens(self, access_token: str, expires_in: int) -> None:
+        """
+        Save the access token and its expiration time.
+
+        :param access_token: The access token to save.
+        :param expires_in: The expiration time of the access token in seconds.
+        """
+        expiration_time = datetime.now() + timedelta(seconds=expires_in)
+        data = {
+            "access_token": access_token,
+            "expiration": expiration_time.isoformat(),
+        }
+        encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
+        self.save_secure_file(self.file_path, encrypted_data)
+
+    def get_token(self) -> Optional[str]:
+        """
+        Get the access token if it is valid and not expired.
+
+        :return: The access token if valid and not expired, otherwise None.
+        """
+        encrypted_data = self.read_secure_file(self.file_path)
+
+        decrypted_data = self.fernet.decrypt(encrypted_data)
+        data = json.loads(decrypted_data)
+
+        expiration = datetime.fromisoformat(data["expiration"])
+        if expiration <= datetime.now():
+            return None
+
+        return data["access_token"]
+
+    def get_secure_storage_path(self) -> Path:
+        """
+        Get the secure storage path based on the operating system.
+
+        :return: The secure storage path.
+        """
+        if sys.platform == "win32":
+            # Windows: Use %LOCALAPPDATA%
+            base_path = os.environ.get("LOCALAPPDATA")
+        elif sys.platform == "darwin":
+            # macOS: Use ~/Library/Application Support
+            base_path = os.path.expanduser("~/Library/Application Support")
+        else:
+            # Linux and other Unix-like: Use ~/.local/share
+            base_path = os.path.expanduser("~/.local/share")
+
+        app_name = "crewai/credentials"
+        storage_path = Path(base_path) / app_name
+
+        storage_path.mkdir(parents=True, exist_ok=True)
+
+        return storage_path
+
+    def save_secure_file(self, filename: str, content: bytes) -> None:
+        """
+        Save the content to a secure file.
+
+        :param filename: The name of the file.
+        :param content: The content to save.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+
+        with open(file_path, "wb") as f:
+            f.write(content)
+
+        # Set appropriate permissions (read/write for owner only)
+        os.chmod(file_path, 0o600)
+
+    def read_secure_file(self, filename: str) -> Optional[bytes]:
+        """
+        Read the content of a secure file.
+
+        :param filename: The name of the file.
+        :return: The content of the file if it exists, otherwise None.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+
+        if not file_path.exists():
+            return None
+
+        with open(file_path, "rb") as f:
+            return f.read()

src/crewai/cli/cli.py

@@ -1,14 +1,22 @@
+from typing import Optional
+
 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 .train_crew import train_crew
+from .authentication.main import AuthenticationCommand
+from .deploy.main import DeployCommand
+from .evaluate_crew import evaluate_crew
+from .install_crew import install_crew
 from .replay_from_task import replay_task_command
+from .reset_memories_command import reset_memories_command
+from .run_crew import run_crew
+from .train_crew import train_crew
 
 
 @click.group()
@@ -17,10 +25,19 @@ def crewai():
 
 
 @crewai.command()
-@click.argument("project_name")
-def create(project_name):
-    """Create a new crew."""
-    create_crew(project_name)
+@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")
 
 
 @crewai.command()
@@ -48,10 +65,17 @@ def version(tools):
     default=5,
     help="Number of iterations to train the crew",
 )
-def train(n_iterations: int):
+@click.option(
+    "-f",
+    "--filename",
+    type=str,
+    default="trained_agents_data.pkl",
+    help="Path to a custom file for training",
+)
+def train(n_iterations: int, filename: str):
     """Train the crew."""
-    click.echo(f"Training the crew for {n_iterations} iterations")
-    train_crew(n_iterations)
+    click.echo(f"Training the Crew for {n_iterations} iterations")
+    train_crew(n_iterations, filename)
 
 
 @crewai.command()
@@ -99,5 +123,131 @@ def log_tasks_outputs() -> None:
         click.echo(f"An error occurred while logging task outputs: {e}", err=True)
 
 
+@crewai.command()
+@click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory")
+@click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
+@click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
+@click.option(
+    "-k",
+    "--kickoff-outputs",
+    is_flag=True,
+    help="Reset LATEST KICKOFF TASK OUTPUTS",
+)
+@click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
+def reset_memories(long, short, entities, kickoff_outputs, all):
+    """
+    Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
+    """
+    try:
+        if not all and not (long or short or entities or kickoff_outputs):
+            click.echo(
+                "Please specify at least one memory type to reset using the appropriate flags."
+            )
+            return
+        reset_memories_command(long, short, entities, kickoff_outputs, all)
+    except Exception as e:
+        click.echo(f"An error occurred while resetting memories: {e}", err=True)
+
+
+@crewai.command()
+@click.option(
+    "-n",
+    "--n_iterations",
+    type=int,
+    default=3,
+    help="Number of iterations to Test the crew",
+)
+@click.option(
+    "-m",
+    "--model",
+    type=str,
+    default="gpt-4o-mini",
+    help="LLM Model to run the tests on the Crew. For now only accepting only OpenAI models.",
+)
+def test(n_iterations: int, model: str):
+    """Test the crew and evaluate the results."""
+    click.echo(f"Testing the crew for {n_iterations} iterations with model {model}")
+    evaluate_crew(n_iterations, model)
+
+
+@crewai.command()
+def install():
+    """Install the Crew."""
+    install_crew()
+
+
+@crewai.command()
+def run():
+    """Run the Crew."""
+    click.echo("Running the Crew")
+    run_crew()
+
+
+@crewai.command()
+def signup():
+    """Sign Up/Login to CrewAI+."""
+    AuthenticationCommand().signup()
+
+
+@crewai.command()
+def login():
+    """Sign Up/Login to CrewAI+."""
+    AuthenticationCommand().signup()
+
+
+# DEPLOY CREWAI+ COMMANDS
+@crewai.group()
+def deploy():
+    """Deploy the Crew CLI group."""
+    pass
+
+
+@deploy.command(name="create")
+@click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
+def deploy_create(yes: bool):
+    """Create a Crew deployment."""
+    deploy_cmd = DeployCommand()
+    deploy_cmd.create_crew(yes)
+
+
+@deploy.command(name="list")
+def deploy_list():
+    """List all deployments."""
+    deploy_cmd = DeployCommand()
+    deploy_cmd.list_crews()
+
+
+@deploy.command(name="push")
+@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
+def deploy_push(uuid: Optional[str]):
+    """Deploy the Crew."""
+    deploy_cmd = DeployCommand()
+    deploy_cmd.deploy(uuid=uuid)
+
+
+@deploy.command(name="status")
+@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
+def deply_status(uuid: Optional[str]):
+    """Get the status of a deployment."""
+    deploy_cmd = DeployCommand()
+    deploy_cmd.get_crew_status(uuid=uuid)
+
+
+@deploy.command(name="logs")
+@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
+def deploy_logs(uuid: Optional[str]):
+    """Get the logs of a deployment."""
+    deploy_cmd = DeployCommand()
+    deploy_cmd.get_crew_logs(uuid=uuid)
+
+
+@deploy.command(name="remove")
+@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
+def deploy_remove(uuid: Optional[str]):
+    """Remove a deployment."""
+    deploy_cmd = DeployCommand()
+    deploy_cmd.remove_crew(uuid=uuid)
+
+
 if __name__ == "__main__":
     crewai()

src/crewai/cli/create_crew.py

@@ -1,25 +1,35 @@
-import os
 from pathlib import Path
 
 import click
 
+from crewai.cli.utils import copy_template
 
-def create_crew(name):
+
+def create_crew(name, parent_folder=None):
     """Create a new crew."""
     folder_name = name.replace(" ", "_").replace("-", "_").lower()
     class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
 
-    click.secho(f"Creating folder {folder_name}...", fg="green", bold=True)
+    if parent_folder:
+        folder_path = Path(parent_folder) / folder_name
+    else:
+        folder_path = Path(folder_name)
 
-    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")
+    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")
     else:
         click.secho(
             f"\tFolder {folder_name} already exists. Please choose a different name.",
@@ -28,53 +38,34 @@ def create_crew(name):
         return
 
     package_dir = Path(__file__).parent
-    templates_dir = package_dir / "templates"
+    templates_dir = package_dir / "templates" / "crew"
 
     # List of template files to copy
-    root_template_files = [
-        ".gitignore",
-        "pyproject.toml",
-        "README.md",
-    ]
+    root_template_files = (
+        [".gitignore", "pyproject.toml", "README.md"] if not parent_folder else []
+    )
     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"]
+    src_template_files = (
+        ["__init__.py", "main.py", "crew.py"] if not parent_folder else ["crew.py"]
+    )
 
     for file_name in root_template_files:
         src_file = templates_dir / file_name
-        dst_file = Path(folder_name) / file_name
+        dst_file = folder_path / 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 = Path(folder_name) / "src" / folder_name / 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)
+    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)
 
     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

@@ -0,0 +1,107 @@
+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/deploy/api.py

@@ -0,0 +1,66 @@
+from os import getenv
+
+import requests
+
+from crewai.cli.deploy.utils import get_crewai_version
+
+
+class CrewAPI:
+    """
+    CrewAPI class to interact with the crewAI+ API.
+    """
+
+    def __init__(self, api_key: str) -> None:
+        self.api_key = api_key
+        self.headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+            "User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
+        }
+        self.base_url = getenv(
+            "CREWAI_BASE_URL", "https://crewai.com/crewai_plus/api/v1/crews"
+        )
+
+    def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
+        url = f"{self.base_url}/{endpoint}"
+        return requests.request(method, url, headers=self.headers, **kwargs)
+
+    # Deploy
+    def deploy_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request("POST", f"by-name/{project_name}/deploy")
+
+    def deploy_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("POST", f"{uuid}/deploy")
+
+    # Status
+    def status_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request("GET", f"by-name/{project_name}/status")
+
+    def status_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("GET", f"{uuid}/status")
+
+    # Logs
+    def logs_by_name(
+        self, project_name: str, log_type: str = "deployment"
+    ) -> requests.Response:
+        return self._make_request("GET", f"by-name/{project_name}/logs/{log_type}")
+
+    def logs_by_uuid(
+        self, uuid: str, log_type: str = "deployment"
+    ) -> requests.Response:
+        return self._make_request("GET", f"{uuid}/logs/{log_type}")
+
+    # Delete
+    def delete_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request("DELETE", f"by-name/{project_name}")
+
+    def delete_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("DELETE", f"{uuid}")
+
+    # List
+    def list_crews(self) -> requests.Response:
+        return self._make_request("GET", "")
+
+    # Create
+    def create_crew(self, payload) -> requests.Response:
+        return self._make_request("POST", "", json=payload)

src/crewai/cli/deploy/main.py

@@ -0,0 +1,318 @@
+from typing import Any, Dict, List, Optional
+
+from rich.console import Console
+
+from crewai.telemetry import Telemetry
+from .api import CrewAPI
+from .utils import (
+    fetch_and_json_env_file,
+    get_auth_token,
+    get_git_remote_url,
+    get_project_name,
+)
+
+console = Console()
+
+
+class DeployCommand:
+    """
+    A class to handle deployment-related operations for CrewAI projects.
+    """
+
+    def __init__(self):
+        """
+        Initialize the DeployCommand with project name and API client.
+        """
+        try:
+            self._telemetry = Telemetry()
+            self._telemetry.set_tracer()
+            access_token = get_auth_token()
+        except Exception:
+            self._deploy_signup_error_span = self._telemetry.deploy_signup_error_span()
+            console.print(
+                "Please sign up/login to CrewAI+ before using the CLI.",
+                style="bold red",
+            )
+            console.print("Run 'crewai signup' to sign up/login.", style="bold green")
+            raise SystemExit
+
+        self.project_name = get_project_name()
+        if self.project_name is None:
+            console.print(
+                "No project name found. Please ensure your project has a valid pyproject.toml file.",
+                style="bold red",
+            )
+            raise SystemExit
+
+        self.client = CrewAPI(api_key=access_token)
+
+    def _handle_error(self, json_response: Dict[str, Any]) -> None:
+        """
+        Handle and display error messages from API responses.
+
+        Args:
+            json_response (Dict[str, Any]): The JSON response containing error information.
+        """
+        error = json_response.get("error", "Unknown error")
+        message = json_response.get("message", "No message provided")
+        console.print(f"Error: {error}", style="bold red")
+        console.print(f"Message: {message}", style="bold red")
+
+    def _standard_no_param_error_message(self) -> None:
+        """
+        Display a standard error message when no UUID or project name is available.
+        """
+        console.print(
+            "No UUID provided, project pyproject.toml not found or with error.",
+            style="bold red",
+        )
+
+    def _display_deployment_info(self, json_response: Dict[str, Any]) -> None:
+        """
+        Display deployment information.
+
+        Args:
+            json_response (Dict[str, Any]): The deployment information to display.
+        """
+        console.print("Deploying the crew...\n", style="bold blue")
+        for key, value in json_response.items():
+            console.print(f"{key.title()}: [green]{value}[/green]")
+        console.print("\nTo check the status of the deployment, run:")
+        console.print("crewai deploy status")
+        console.print(" or")
+        console.print(f"crewai deploy status --uuid \"{json_response['uuid']}\"")
+
+    def _display_logs(self, log_messages: List[Dict[str, Any]]) -> None:
+        """
+        Display log messages.
+
+        Args:
+            log_messages (List[Dict[str, Any]]): The log messages to display.
+        """
+        for log_message in log_messages:
+            console.print(
+                f"{log_message['timestamp']} - {log_message['level']}: {log_message['message']}"
+            )
+
+    def deploy(self, uuid: Optional[str] = None) -> None:
+        """
+        Deploy a crew using either UUID or project name.
+
+        Args:
+            uuid (Optional[str]): The UUID of the crew to deploy.
+        """
+        self._start_deployment_span = self._telemetry.start_deployment_span(uuid)
+        console.print("Starting deployment...", style="bold blue")
+        if uuid:
+            response = self.client.deploy_by_uuid(uuid)
+        elif self.project_name:
+            response = self.client.deploy_by_name(self.project_name)
+        else:
+            self._standard_no_param_error_message()
+            return
+
+        json_response = response.json()
+        if response.status_code == 200:
+            self._display_deployment_info(json_response)
+        else:
+            self._handle_error(json_response)
+
+    def create_crew(self, confirm: bool) -> None:
+        """
+        Create a new crew deployment.
+        """
+        self._create_crew_deployment_span = (
+            self._telemetry.create_crew_deployment_span()
+        )
+        console.print("Creating deployment...", style="bold blue")
+        env_vars = fetch_and_json_env_file()
+        remote_repo_url = get_git_remote_url()
+
+        if remote_repo_url is None:
+            console.print("No remote repository URL found.", style="bold red")
+            console.print(
+                "Please ensure your project has a valid remote repository.",
+                style="yellow",
+            )
+            return
+
+        self._confirm_input(env_vars, remote_repo_url, confirm)
+        payload = self._create_payload(env_vars, remote_repo_url)
+
+        response = self.client.create_crew(payload)
+        if response.status_code == 201:
+            self._display_creation_success(response.json())
+        else:
+            self._handle_error(response.json())
+
+    def _confirm_input(
+        self, env_vars: Dict[str, str], remote_repo_url: str, confirm: bool
+    ) -> None:
+        """
+        Confirm input parameters with the user.
+
+        Args:
+            env_vars (Dict[str, str]): Environment variables.
+            remote_repo_url (str): Remote repository URL.
+            confirm (bool): Whether to confirm input.
+        """
+        if not confirm:
+            input(f"Press Enter to continue with the following Env vars: {env_vars}")
+            input(
+                f"Press Enter to continue with the following remote repository: {remote_repo_url}\n"
+            )
+
+    def _create_payload(
+        self,
+        env_vars: Dict[str, str],
+        remote_repo_url: str,
+    ) -> Dict[str, Any]:
+        """
+        Create the payload for crew creation.
+
+        Args:
+            remote_repo_url (str): Remote repository URL.
+            env_vars (Dict[str, str]): Environment variables.
+
+        Returns:
+            Dict[str, Any]: The payload for crew creation.
+        """
+        return {
+            "deploy": {
+                "name": self.project_name,
+                "repo_clone_url": remote_repo_url,
+                "env": env_vars,
+            }
+        }
+
+    def _display_creation_success(self, json_response: Dict[str, Any]) -> None:
+        """
+        Display success message after crew creation.
+
+        Args:
+            json_response (Dict[str, Any]): The response containing crew information.
+        """
+        console.print("Deployment created successfully!\n", style="bold green")
+        console.print(
+            f"Name: {self.project_name} ({json_response['uuid']})", style="bold green"
+        )
+        console.print(f"Status: {json_response['status']}", style="bold green")
+        console.print("\nTo (re)deploy the crew, run:")
+        console.print("crewai deploy push")
+        console.print(" or")
+        console.print(f"crewai deploy push --uuid {json_response['uuid']}")
+
+    def list_crews(self) -> None:
+        """
+        List all available crews.
+        """
+        console.print("Listing all Crews\n", style="bold blue")
+
+        response = self.client.list_crews()
+        json_response = response.json()
+        if response.status_code == 200:
+            self._display_crews(json_response)
+        else:
+            self._display_no_crews_message()
+
+    def _display_crews(self, crews_data: List[Dict[str, Any]]) -> None:
+        """
+        Display the list of crews.
+
+        Args:
+            crews_data (List[Dict[str, Any]]): List of crew data to display.
+        """
+        for crew_data in crews_data:
+            console.print(
+                f"- {crew_data['name']} ({crew_data['uuid']}) [blue]{crew_data['status']}[/blue]"
+            )
+
+    def _display_no_crews_message(self) -> None:
+        """
+        Display a message when no crews are available.
+        """
+        console.print("You don't have any Crews yet. Let's create one!", style="yellow")
+        console.print("  crewai create crew <crew_name>", style="green")
+
+    def get_crew_status(self, uuid: Optional[str] = None) -> None:
+        """
+        Get the status of a crew.
+
+        Args:
+            uuid (Optional[str]): The UUID of the crew to check.
+        """
+        console.print("Fetching deployment status...", style="bold blue")
+        if uuid:
+            response = self.client.status_by_uuid(uuid)
+        elif self.project_name:
+            response = self.client.status_by_name(self.project_name)
+        else:
+            self._standard_no_param_error_message()
+            return
+
+        json_response = response.json()
+        if response.status_code == 200:
+            self._display_crew_status(json_response)
+        else:
+            self._handle_error(json_response)
+
+    def _display_crew_status(self, status_data: Dict[str, str]) -> None:
+        """
+        Display the status of a crew.
+
+        Args:
+            status_data (Dict[str, str]): The status data to display.
+        """
+        console.print(f"Name:\t {status_data['name']}")
+        console.print(f"Status:\t {status_data['status']}")
+
+    def get_crew_logs(self, uuid: Optional[str], log_type: str = "deployment") -> None:
+        """
+        Get logs for a crew.
+
+        Args:
+            uuid (Optional[str]): The UUID of the crew to get logs for.
+            log_type (str): The type of logs to retrieve (default: "deployment").
+        """
+        self._get_crew_logs_span = self._telemetry.get_crew_logs_span(uuid, log_type)
+        console.print(f"Fetching {log_type} logs...", style="bold blue")
+
+        if uuid:
+            response = self.client.logs_by_uuid(uuid, log_type)
+        elif self.project_name:
+            response = self.client.logs_by_name(self.project_name, log_type)
+        else:
+            self._standard_no_param_error_message()
+            return
+
+        if response.status_code == 200:
+            self._display_logs(response.json())
+        else:
+            self._handle_error(response.json())
+
+    def remove_crew(self, uuid: Optional[str]) -> None:
+        """
+        Remove a crew deployment.
+
+        Args:
+            uuid (Optional[str]): The UUID of the crew to remove.
+        """
+        self._remove_crew_span = self._telemetry.remove_crew_span(uuid)
+        console.print("Removing deployment...", style="bold blue")
+
+        if uuid:
+            response = self.client.delete_by_uuid(uuid)
+        elif self.project_name:
+            response = self.client.delete_by_name(self.project_name)
+        else:
+            self._standard_no_param_error_message()
+            return
+
+        if response.status_code == 204:
+            console.print(
+                f"Crew '{self.project_name}' removed successfully.", style="green"
+            )
+        else:
+            console.print(
+                f"Failed to remove crew '{self.project_name}'", style="bold red"
+            )

src/crewai/cli/deploy/utils.py

@@ -0,0 +1,155 @@
+import sys
+import re
+import subprocess
+
+from rich.console import Console
+
+from ..authentication.utils import TokenManager
+
+console = Console()
+
+
+if sys.version_info >= (3, 11):
+    import tomllib
+
+
+# Drop the simple_toml_parser when we move to python3.11
+def simple_toml_parser(content):
+    result = {}
+    current_section = result
+    for line in content.split('\n'):
+        line = line.strip()
+        if line.startswith('[') and line.endswith(']'):
+            # New section
+            section = line[1:-1].split('.')
+            current_section = result
+            for key in section:
+                current_section = current_section.setdefault(key, {})
+        elif '=' in line:
+            key, value = line.split('=', 1)
+            key = key.strip()
+            value = value.strip().strip('"')
+            current_section[key] = value
+    return result
+
+
+def parse_toml(content):
+    if sys.version_info >= (3, 11):
+        return tomllib.loads(content)
+    else:
+        return simple_toml_parser(content)
+
+
+def get_git_remote_url() -> str | None:
+    """Get the Git repository's remote URL."""
+    try:
+        # Run the git remote -v command
+        result = subprocess.run(
+            ["git", "remote", "-v"], capture_output=True, text=True, check=True
+        )
+
+        # Get the output
+        output = result.stdout
+
+        # Parse the output to find the origin URL
+        matches = re.findall(r"origin\s+(.*?)\s+\(fetch\)", output)
+
+        if matches:
+            return matches[0]  # Return the first match (origin URL)
+        else:
+            console.print("No origin remote found.", style="bold red")
+
+    except subprocess.CalledProcessError as e:
+        console.print(f"Error running trying to fetch the Git Repository: {e}", style="bold red")
+    except FileNotFoundError:
+        console.print("Git command not found. Make sure Git is installed and in your PATH.", style="bold red")
+
+    return None
+
+
+def get_project_name(pyproject_path: str = "pyproject.toml") -> str | None:
+    """Get the project name from the pyproject.toml file."""
+    try:
+        # Read the pyproject.toml file
+        with open(pyproject_path, "r") as f:
+            pyproject_content = parse_toml(f.read())
+
+        # Extract the project name
+        project_name = pyproject_content["tool"]["poetry"]["name"]
+
+        if "crewai" not in pyproject_content["tool"]["poetry"]["dependencies"]:
+            raise Exception("crewai is not in the dependencies.")
+
+        return project_name
+
+    except FileNotFoundError:
+        print(f"Error: {pyproject_path} not found.")
+    except KeyError:
+        print(f"Error: {pyproject_path} is not a valid pyproject.toml file.")
+    except tomllib.TOMLDecodeError if sys.version_info >= (3, 11) else Exception as e:  # type: ignore
+        print(
+            f"Error: {pyproject_path} is not a valid TOML file."
+            if sys.version_info >= (3, 11)
+            else f"Error reading the pyproject.toml file: {e}"
+        )
+    except Exception as e:
+        print(f"Error reading the pyproject.toml file: {e}")
+
+    return None
+
+
+def get_crewai_version(poetry_lock_path: str = "poetry.lock") -> str:
+    """Get the version number of crewai from the poetry.lock file."""
+    try:
+        with open(poetry_lock_path, "r") as f:
+            lock_content = f.read()
+
+        match = re.search(
+            r'\[\[package\]\]\s*name\s*=\s*"crewai"\s*version\s*=\s*"([^"]+)"',
+            lock_content,
+            re.DOTALL,
+        )
+        if match:
+            return match.group(1)
+        else:
+            print("crewai package not found in poetry.lock")
+            return "no-version-found"
+
+    except FileNotFoundError:
+        print(f"Error: {poetry_lock_path} not found.")
+    except Exception as e:
+        print(f"Error reading the poetry.lock file: {e}")
+
+    return "no-version-found"
+
+
+def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
+    """Fetch the environment variables from a .env file and return them as a dictionary."""
+    try:
+        # Read the .env file
+        with open(env_file_path, "r") as f:
+            env_content = f.read()
+
+        # Parse the .env file content to a dictionary
+        env_dict = {}
+        for line in env_content.splitlines():
+            if line.strip() and not line.strip().startswith("#"):
+                key, value = line.split("=", 1)
+                env_dict[key.strip()] = value.strip()
+
+        return env_dict
+
+    except FileNotFoundError:
+        print(f"Error: {env_file_path} not found.")
+    except Exception as e:
+        print(f"Error reading the .env file: {e}")
+
+    return {}
+
+
+def get_auth_token() -> str:
+    """Get the authentication token."""
+    access_token = TokenManager().get_token()
+    if not access_token:
+        raise Exception()
+    return access_token

src/crewai/cli/evaluate_crew.py

@@ -0,0 +1,30 @@
+import subprocess
+
+import click
+
+
+def evaluate_crew(n_iterations: int, model: str) -> None:
+    """
+    Test and Evaluate the crew by running a command in the Poetry environment.
+
+    Args:
+        n_iterations (int): The number of iterations to test the crew.
+        model (str): The model to test the crew with.
+    """
+    command = ["poetry", "run", "test", str(n_iterations), model]
+
+    try:
+        if n_iterations <= 0:
+            raise ValueError("The number of iterations must be a positive integer.")
+
+        result = subprocess.run(command, capture_output=False, text=True, check=True)
+
+        if result.stderr:
+            click.echo(result.stderr, err=True)
+
+    except subprocess.CalledProcessError as e:
+        click.echo(f"An error occurred while testing the crew: {e}", err=True)
+        click.echo(e.output, err=True)
+
+    except Exception as e:
+        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/install_crew.py

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

src/crewai/cli/reset_memories_command.py

@@ -0,0 +1,49 @@
+import subprocess
+import click
+
+from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.long_term.long_term_memory import LongTermMemory
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+
+
+def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
+    """
+    Reset the crew memories.
+
+    Args:
+      long (bool): Whether to reset the long-term memory.
+      short (bool): Whether to reset the short-term memory.
+      entity (bool): Whether to reset the entity memory.
+      kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
+      all (bool): Whether to reset all memories.
+    """
+
+    try:
+        if all:
+            ShortTermMemory().reset()
+            EntityMemory().reset()
+            LongTermMemory().reset()
+            TaskOutputStorageHandler().reset()
+            click.echo("All memories have been reset.")
+        else:
+            if long:
+                LongTermMemory().reset()
+                click.echo("Long term memory has been reset.")
+
+            if short:
+                ShortTermMemory().reset()
+                click.echo("Short term memory has been reset.")
+            if entity:
+                EntityMemory().reset()
+                click.echo("Entity memory has been reset.")
+            if kickoff_outputs:
+                TaskOutputStorageHandler().reset()
+                click.echo("Latest Kickoff outputs stored has been reset.")
+
+    except subprocess.CalledProcessError as e:
+        click.echo(f"An error occurred while resetting the memories: {e}", err=True)
+        click.echo(e.output, err=True)
+
+    except Exception as e:
+        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/run_crew.py

@@ -0,0 +1,23 @@
+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/README.md

@@ -0,0 +1,54 @@
+# {{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 install them by using the CLI command:
+```bash
+crewai 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
+```
+
+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/crew/config/tasks.yaml

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

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

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

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

@@ -0,0 +1,54 @@
+#!/usr/bin/env python
+import sys
+from {{folder_name}}.crew import {{crew_name}}Crew
+
+# This main file is intended to be a way for your to run your
+# crew locally, so refrain from adding necessary logic into this file.
+# Replace with inputs you want to test with, it will automatically
+# interpolate any tasks and agents information
+
+def run():
+    """
+    Run the crew.
+    """
+    inputs = {
+        'topic': 'AI LLMs'
+    }
+    {{crew_name}}Crew().crew().kickoff(inputs=inputs)
+
+
+def train():
+    """
+    Train the crew for a given number of iterations.
+    """
+    inputs = {
+        "topic": "AI LLMs"
+    }
+    try:
+        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
+
+    except Exception as e:
+        raise Exception(f"An error occurred while training the crew: {e}")
+
+def replay():
+    """
+    Replay the crew execution from a specific task.
+    """
+    try:
+        {{crew_name}}Crew().crew().replay(task_id=sys.argv[1])
+
+    except Exception as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")
+
+def test():
+    """
+    Test the crew execution and returns the results.
+    """
+    inputs = {
+        "topic": "AI LLMs"
+    }
+    try:
+        {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
+
+    except Exception as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")

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

@@ -6,12 +6,15 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.35.8" }
+crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+
 
 [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_from_task"
+replay = "{{folder_name}}.main:replay"
+test = "{{folder_name}}.main:test"
 
 [build-system]
 requires = ["poetry-core"]

src/crewai/cli/templates/main.py

@@ -1,33 +0,0 @@
-#!/usr/bin/env python
-import sys
-from {{folder_name}}.crew import {{crew_name}}Crew
-
-
-def run():
-    # Replace with your inputs, it will automatically interpolate any tasks and agents information
-    inputs = {
-        'topic': 'AI LLMs'
-    }
-    {{crew_name}}Crew().crew().kickoff(inputs=inputs)
-
-
-def train():
-    """
-    Train the crew for a given number of iterations.
-    """
-    inputs = {"topic": "AI LLMs"}
-    try:
-        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), inputs=inputs)
-
-    except Exception as e:
-        raise Exception(f"An error occurred while training the crew: {e}")
-
-def replay_from_task():
-    """
-    Replay the crew execution from a specific task.
-    """
-    try:
-        {{crew_name}}Crew().crew().replay_from_task(task_id=sys.argv[1])
-
-    except Exception as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")

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

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

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

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

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

@@ -0,0 +1,19 @@
+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

@@ -0,0 +1,16 @@
+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

@@ -0,0 +1,58 @@
+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,
+		)