preparing for version 0.64.0

This commit adds two commands to the CLI:

- `crewai tool publish`
    - Builds the project using Poetry
    - Uploads the tarball to CrewAI's tool repository

- `crewai tool install my-tool`
    - Adds my-tool's index to Poetry and its credentials
    - Installs my-tool from the custom index

.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
@@ -19,13 +20,13 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: "3.11.9"
 
       - name: Install Requirements
         run: |
+          set -e
           pip install poetry
-          poetry lock &&
           poetry install
 
       - name: Run tests
-        run: poetry run pytest tests
+        run: poetry run pytest

.gitignore

@@ -11,4 +11,8 @@ chroma.sqlite3
 old_en.json
 db/
 test.py
-rc-tests/*
+rc-tests/*
+*.pkl
+temp/*
+.vscode/*
+crew_tasks_output.json

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>
@@ -64,24 +64,8 @@ from crewai_tools import SerperDevTool
 os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
 os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 
-# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
-
-# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
-# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
-# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'
-
-# 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/)
-#
-# import os
-# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
-#
-# OR
-#
-# from langchain_openai import ChatOpenAI
-
-search_tool = SerperDevTool()
 
 # Define your agents with roles and goals
 researcher = Agent(
@@ -94,7 +78,7 @@ researcher = Agent(
   allow_delegation=False,
   # You can pass an optional llm attribute specifying what model you wanna use.
   # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
-  tools=[search_tool]
+  tools=[SerperDevTool()]
 )
 writer = Agent(
   role='Tech Content Strategist',
@@ -126,7 +110,8 @@ 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
 )
 
 # Get your crew to work!
@@ -152,12 +137,12 @@ 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
 
@@ -165,19 +150,19 @@ 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")
 
@@ -189,12 +174,12 @@ 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:
@@ -252,7 +237,7 @@ 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:
 
@@ -277,8 +262,44 @@ 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

@@ -11,29 +11,34 @@ description: What are crewAI Agents and how to use them.
       <li class='leading-3'>Make decisions</li>
       <li class='leading-3'>Communicate with other agents</li>
     </ul>
-      <br/>
+    <br/>
     Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
 
 ## Agent Attributes
 
-| Attribute                  | Description                                                                                                                                                                                                                                    |
-| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Role**                   | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
-| **Goal**                   | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
-| **Backstory**              | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
-| **LLM** *(optional)*       | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
-| **Tools** *(optional)*     | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
-| **Function Calling LLM** *(optional)* | 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` 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` is Tte 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` 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)*   | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
-| **Allow Delegation** *(optional)* | 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)* | 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`.                                                               |
-| **Cache** *(optional)*     | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
-| **System Template** *(optional)*     | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Prompt Template** *(optional)*     | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Response Template** *(optional)*     | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
+| Attribute                  | Parameter  | Description                                                                                                                                                                                                                                    |
+| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Role**                   | `role`     | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
+| **Goal**                   | `goal`     | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
+| **Backstory**              | `backstory`| Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
+| **LLM** *(optional)*       | `llm`      | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
+| **Tools** *(optional)*     | `tools`    | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
+| **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.                                                                     |
+| **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 `False`.
+| **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`.                                                               |
+| **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
+| **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`.
+| **Use System Prompt** *(optional)*     | `use_system_prompt`  | Adds the ability to not use system prompt (to support o1 models). Default is `True`.                                                                                                                                                                  |
+| **Respect Context Window** *(optional)*     | `respect_context_window`  | Summary strategy to avoid overflowing the context window. Default is `True`.                                                                                                                                                                  |
+
 ## Creating an Agent
 
 !!! note "Agent Interaction"
@@ -60,7 +65,7 @@ agent = Agent(
   max_rpm=None, # Optional
   max_execution_time=None, # Optional
   verbose=True,  # Optional
-  allow_delegation=True,  # Optional
+  allow_delegation=False,  # Optional
   step_callback=my_intermediate_step_callback,  # Optional
   cache=True,  # Optional
   system_template=my_system_template,  # Optional
@@ -71,7 +76,10 @@ 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,  # Optional
+  max_retry_limit=2,  # Optional
+  use_system_prompt=True,  # Optional
+  respect_context_window=True,  # Optional
 )
 ```
 
@@ -96,5 +104,52 @@ agent = Agent(
     )
 ```
 
+## Bring your Third Party Agents
+!!! note "Extend your Third Party Agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the crewai's BaseAgent class."
+
+    BaseAgent includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
+
+    CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
+
+
+```py
+from crewai import Agent, Task, Crew
+from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
+
+from langchain.agents import load_tools
+
+langchain_tools = load_tools(["google-serper"], llm=llm)
+
+agent1 = CustomAgent(
+    role="agent role",
+    goal="who is {input}?",
+    backstory="agent backstory",
+    verbose=True,
+)
+
+task1 = Task(
+    expected_output="a short biography of {input}",
+    description="a short biography of {input}",
+    agent=agent1,
+)
+
+agent2 = Agent(
+    role="agent role",
+    goal="summarize the short bio for {input} and if needed do more research",
+    backstory="agent backstory",
+    verbose=True,
+)
+
+task2 = Task(
+    description="a tldr summary of the short biography",
+    expected_output="5 bullet point summary of the biography",
+    agent=agent2,
+    context=[task1],
+)
+
+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.

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

@@ -27,7 +27,9 @@ The `Crew` class has been enriched with several attributes to support advanced f
 - **Memory Usage (`memory`)**: Indicates whether the crew should use memory to store memories of its execution, enhancing task execution and agent learning.
 - **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.
+- **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew's 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

@@ -4,89 +4,90 @@ 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                   | Description                                                  |
-| :-------------------------- | :----------------------------------------------------------- |
-| **Tasks**                   | A list of tasks assigned to the crew.                        |
-| **Agents**                  | A list of agents that are part of the crew.                  |
-| **Process** *(optional)*    | The process flow (e.g., sequential, hierarchical) the crew follows. |
-| **Verbose** *(optional)*    | The verbosity level for logging during execution.            |
-| **Manager LLM** *(optional)*| The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
-| **Function Calling LLM** *(optional)* | 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)*     | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
-| **Max RPM** *(optional)*    | Maximum requests per minute the crew adheres to during execution. |
-| **Language**  *(optional)*  | Language used for the crew, defaults to English.             |
-| **Language File** *(optional)* | Path to the language file to be used for the crew.          |
-| **Memory** *(optional)*     | Utilized for storing execution memories (short-term, long-term, entity memory). |
-| **Cache** *(optional)*      | Specifies whether to use a cache for storing the results of tools' execution. |
-| **Embedder** *(optional)*   | Configuration for the embedder to be used by the crew. Mostly used by memory for now.       |
-| **Full Output** *(optional)*| Whether the crew should return the full output with all tasks outputs or just the final output. |
-| **Step Callback** *(optional)* | 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)* | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
-| **Share Crew** *(optional)* | 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)* | 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` sets a ustom agent that will be used as a manager. |
-| **Manager Callbacks** *(optional)* | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
-| **Prompt File** *(optional)* | 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. Default is `sequential`.                                                                                                                                                              |
+| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution. Defaults to `False`.                                                                                                                                                                                    |
+| **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. Defaults to `None`.                                                                                                                                                                     |
+| **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). Defaults to `False`.                                                                                                                                                       |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                          |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                                                     |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                       |
+| **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
 
-When assembling a crew, you combine agents with complementary roles and tools, assign tasks, and select a process that dictates their execution order and interaction.
+## Crew Output
 
-### Example: Assembling a Crew
+!!! 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
-from crewai import Crew, Agent, Task, Process
-from langchain_community.tools import DuckDuckGoSearchRun
-
-# Define agents with specific roles and tools
-researcher = Agent(
-    role='Senior Research Analyst',
-    goal='Discover innovative AI technologies',
-    backstory="""You're a senior research analyst at a large company.
-        You're responsible for analyzing data and providing insights
-        to the business.
-        You're currently working on a project to analyze the
-        trends and innovations in the space of artificial intelligence.""",
-    tools=[DuckDuckGoSearchRun()]
-)
-
-writer = Agent(
-    role='Content Writer',
-    goal='Write engaging articles on AI discoveries',
-    backstory="""You're a senior writer at a large company.
-        You're responsible for creating content to the business.
-        You're currently working on a project to write about trends
-        and innovations in the space of AI for your next meeting.""",
+# Example crew execution
+crew = Crew(
+    agents=[research_agent, writer_agent],
+    tasks=[research_task, write_article_task],
     verbose=True
 )
 
-# Create tasks for the agents
-research_task = Task(
-    description='Identify breakthrough AI technologies',
-    agent=researcher,
-    expected_output='A bullet list summary of the top 5 most important AI news'
-)
-write_article_task = Task(
-    description='Draft an article on the latest AI technologies',
-    agent=writer,
-    expected_output='3 paragraph blog post on the latest AI technologies'
-)
+crew_output = crew.kickoff()
 
-# Assemble the crew with a sequential process
-my_crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, write_article_task],
-    process=Process.sequential,
-    full_output=True,
-    verbose=True,
-)
+# 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
@@ -123,14 +124,14 @@ result = my_crew.kickoff()
 print(result)
 ```
 
-### 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
@@ -138,18 +139,51 @@ result = my_crew.kickoff()
 print(result)
 
 # Example of using kickoff_for_each
-results = my_crew.kickoff_for_each()
+inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
+results = my_crew.kickoff_for_each(inputs=inputs_array)
 for result in results:
     print(result)
 
 # Example of using kickoff_async
-async_result = my_crew.kickoff_async()
+inputs = {'topic': 'AI in healthcare'}
+async_result = my_crew.kickoff_async(inputs=inputs)
 print(async_result)
 
 # Example of using kickoff_for_each_async
-async_results = my_crew.kickoff_for_each_async()
+inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
+async_results = my_crew.kickoff_for_each_async(inputs=inputs_array)
 for async_result in async_results:
     print(async_result)
 ```
 
-These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs
+These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
+
+### Replaying from a Specific Task
+
+You can now replay from a specific task using our CLI command `replay`.
+
+The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
+
+Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
+
+### Replaying from a Specific Task Using the CLI
+
+To use the replay feature, follow these steps:
+
+1. Open your terminal or command prompt.
+2. Navigate to the directory where your CrewAI project is located.
+3. Run the following command:
+
+To view the latest kickoff task IDs, use:
+
+```shell
+crewai log-tasks-outputs
+```
+
+Then, to replay from a specific task, use:
+
+```shell
+crewai replay -t <task_id>
+```
+
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/core-concepts/LLMs.md

@@ -0,0 +1,155 @@
+# Large Language Models (LLMs) in crewAI
+
+## Introduction
+Large Language Models (LLMs) are the backbone of intelligent agents in the crewAI framework. This guide will help you understand, configure, and optimize LLM usage for your crewAI projects.
+
+## Table of Contents
+- [Key Concepts](#key-concepts)
+- [Configuring LLMs for Agents](#configuring-llms-for-agents)
+  - [1. Default Configuration](#1-default-configuration)
+  - [2. String Identifier](#2-string-identifier)
+  - [3. LLM Instance](#3-llm-instance)
+  - [4. Custom LLM Objects](#4-custom-llm-objects)
+- [Connecting to OpenAI-Compatible LLMs](#connecting-to-openai-compatible-llms)
+- [LLM Configuration Options](#llm-configuration-options)
+- [Using Ollama (Local LLMs)](#using-ollama-local-llms)
+- [Changing the Base API URL](#changing-the-base-api-url)
+- [Best Practices](#best-practices)
+- [Troubleshooting](#troubleshooting)
+
+## Key Concepts
+- **LLM**: Large Language Model, the AI powering agent intelligence
+- **Agent**: A crewAI entity that uses an LLM to perform tasks
+- **Provider**: A service that offers LLM capabilities (e.g., OpenAI, Anthropic, Ollama, [more providers](https://docs.litellm.ai/docs/providers))
+
+## Configuring LLMs for Agents
+
+crewAI offers flexible options for setting up LLMs:
+
+### 1. Default Configuration
+By default, crewAI uses the `gpt-4o-mini` model. It uses environment variables if no LLM is specified:
+- `OPENAI_MODEL_NAME` (defaults to "gpt-4o-mini" if not set)
+- `OPENAI_API_BASE`
+- `OPENAI_API_KEY`
+
+### 2. String Identifier
+```python
+agent = Agent(llm="gpt-4o", ...)
+```
+
+### 3. LLM Instance
+List of [more providers](https://docs.litellm.ai/docs/providers).
+```python
+from crewai import LLM
+
+llm = LLM(model="gpt-4", temperature=0.7)
+agent = Agent(llm=llm, ...)
+```
+
+### 4. Custom LLM Objects
+Pass a custom LLM implementation or object from another library.
+
+## Connecting to OpenAI-Compatible LLMs
+
+You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
+
+1. Using environment variables:
+```python
+import os
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
+```
+
+2. Using LLM class attributes:
+```python
+llm = LLM(
+    model="custom-model-name",
+    api_key="your-api-key",
+    base_url="https://api.your-provider.com/v1"
+)
+agent = Agent(llm=llm, ...)
+```
+
+## LLM Configuration Options
+
+When configuring an LLM for your agent, you have access to a wide range of parameters:
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `model` | str | The name of the model to use (e.g., "gpt-4", "gpt-3.5-turbo", "ollama/llama3.1", [more providers](https://docs.litellm.ai/docs/providers)) |
+| `timeout` | float, int | Maximum time (in seconds) to wait for a response |
+| `temperature` | float | Controls randomness in output (0.0 to 1.0) |
+| `top_p` | float | Controls diversity of output (0.0 to 1.0) |
+| `n` | int | Number of completions to generate |
+| `stop` | str, List[str] | Sequence(s) to stop generation |
+| `max_tokens` | int | Maximum number of tokens to generate |
+| `presence_penalty` | float | Penalizes new tokens based on their presence in the text so far |
+| `frequency_penalty` | float | Penalizes new tokens based on their frequency in the text so far |
+| `logit_bias` | Dict[int, float] | Modifies likelihood of specified tokens appearing in the completion |
+| `response_format` | Dict[str, Any] | Specifies the format of the response (e.g., {"type": "json_object"}) |
+| `seed` | int | Sets a random seed for deterministic results |
+| `logprobs` | bool | Whether to return log probabilities of the output tokens |
+| `top_logprobs` | int | Number of most likely tokens to return the log probabilities for |
+| `base_url` | str | The base URL for the API endpoint |
+| `api_version` | str | The version of the API to use |
+| `api_key` | str | Your API key for authentication |
+
+Example:
+```python
+llm = LLM(
+    model="gpt-4",
+    temperature=0.8,
+    max_tokens=150,
+    top_p=0.9,
+    frequency_penalty=0.1,
+    presence_penalty=0.1,
+    stop=["END"],
+    seed=42,
+    base_url="https://api.openai.com/v1",
+    api_key="your-api-key-here"
+)
+agent = Agent(llm=llm, ...)
+```
+
+## Using Ollama (Local LLMs)
+
+crewAI supports using Ollama for running open-source models locally:
+
+1. Install Ollama: [ollama.ai](https://ollama.ai/)
+2. Run a model: `ollama run llama2`
+3. Configure agent:
+```python
+agent = Agent(
+    llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
+    ...
+)
+```
+
+## Changing the Base API URL
+
+You can change the base API URL for any LLM provider by setting the `base_url` parameter:
+
+```python
+llm = LLM(
+    model="custom-model-name",
+    base_url="https://api.your-provider.com/v1",
+    api_key="your-api-key"
+)
+agent = Agent(llm=llm, ...)
+```
+
+This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
+
+## Best Practices
+1. **Choose the right model**: Balance capability and cost.
+2. **Optimize prompts**: Clear, concise instructions improve output.
+3. **Manage tokens**: Monitor and limit token usage for efficiency.
+4. **Use appropriate temperature**: Lower for factual tasks, higher for creative ones.
+5. **Implement error handling**: Gracefully manage API errors and rate limits.
+
+## Troubleshooting
+- **API Errors**: Check your API key, network connection, and rate limits.
+- **Unexpected Outputs**: Refine your prompts and adjust temperature or top_p.
+- **Performance Issues**: Consider using a more powerful model or optimizing your queries.
+- **Timeout Errors**: Increase the `timeout` parameter or optimize your input.

docs/core-concepts/Memory.md

@@ -4,16 +4,17 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
 ---
 
 ## Introduction to Memory Systems in crewAI
+
 !!! note "Enhancing Agent Intelligence"
     The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. This system comprises short-term memory, long-term memory, entity memory, and contextual memory, each serving a unique purpose in aiding agents to remember, reason, and learn from past interactions.
 
 ## Memory System Components
 
-| Component            | Description                                                  |
-| :------------------- | :----------------------------------------------------------- |
-| **Short-Term Memory**| Temporarily stores recent interactions and outcomes, enabling agents to recall and utilize information relevant to their current context during the current executions. |
-| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. So Agents can remeber what they did right and wrong across multiple executions |
-| **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. |
+| Component            | Description                                                                                                             |
+| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
+| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
+| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
+| **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
 | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
 
 ## How Memory Systems Empower Agents
@@ -27,7 +28,12 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
 ## Implementing Memory in Your Crew
 
 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.
+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. It's also possible to initialize the memory instance with your own instance.
+
+The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using the EmbedChain package.
+The **Long-Term Memory** uses SQLite3 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 can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
 
 ### Example: Configuring Memory for a Crew
 
@@ -44,6 +50,45 @@ my_crew = Crew(
 )
 ```
 
+### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
+
+```python
+from crewai import Crew, Agent, Task, Process
+
+# Assemble your crew with memory capabilities
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process="Process.sequential",
+    memory=True,
+    long_term_memory=EnhanceLongTermMemory(
+        storage=LTMSQLiteStorage(
+            db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
+        )
+    ),
+    short_term_memory=EnhanceShortTermMemory(
+        storage=CustomRAGStorage(
+            crew_name="my_crew",
+            storage_type="short_term",
+            data_dir="//my_data_dir",
+            model=embedder["model"],
+            dimension=embedder["dimension"],
+        ),
+    ),
+    entity_memory=EnhanceEntityMemory(
+        storage=CustomRAGStorage(
+            crew_name="my_crew",
+            storage_type="entities",
+            data_dir="//my_data_dir",
+            model=embedder["model"],
+            dimension=embedder["dimension"],
+        ),
+    ),
+    verbose=True,
+)
+```
+
+
 ## Additional Embedding Providers
 
 ### Using OpenAI embeddings (already default)
@@ -51,17 +96,17 @@ my_crew = Crew(
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-				"provider": "openai",
-				"config":{
-						"model": 'text-embedding-3-small'
-				}
-		}
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": 'text-embedding-3-small'
+        }
+    }
 )
 ```
 
@@ -70,19 +115,19 @@ my_crew = Crew(
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "google",
-			"config":{
-				"model": 'models/embedding-001',
-				"task_type": "retrieval_document",
-				"title": "Embeddings for Embedchain"
-			}
-		}
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "google",
+        "config": {
+            "model": 'models/embedding-001',
+            "task_type": "retrieval_document",
+            "title": "Embeddings for Embedchain"
+        }
+    }
 )
 ```
 
@@ -91,18 +136,18 @@ my_crew = Crew(
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "azure_openai",
-			"config":{
-				"model": 'text-embedding-ada-002',
-				"deployment_name": "you_embedding_model_deployment_name"
-			}
-		}
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "azure_openai",
+        "config": {
+            "model": 'text-embedding-ada-002',
+            "deployment_name": "your_embedding_model_deployment_name"
+        }
+    }
 )
 ```
 
@@ -111,14 +156,14 @@ my_crew = Crew(
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "gpt4all"
-		}
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "gpt4all"
+    }
 )
 ```
 
@@ -127,17 +172,17 @@ my_crew = Crew(
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "vertexai",
-			"config":{
-				"model": 'textembedding-gecko'
-			}
-		}
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "vertexai",
+        "config": {
+            "model": 'textembedding-gecko'
+        }
+    }
 )
 ```
 
@@ -146,21 +191,52 @@ my_crew = Crew(
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "cohere",
-			"config":{
-				"model": "embed-english-v3.0"
-    		"vector_dimension": 1024
-			}
-		}
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "cohere",
+        "config": {
+            "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 +1,268 @@
+---
+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).
+- **Kickoff**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
+- **Branch**: Parallel executions within a stage (e.g., concurrent crew operations).
+- **Trace**: The journey of an individual input through the entire pipeline, 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 kickoff, flowing through all stages of the pipeline. Multiple kickoffs can be processed concurrently, each following the defined pipeline structure.
+
+## Pipeline Attributes
+
+| Attribute  | Parameters  | Description                                                                                                        |
+| :--------- | :---------- | :----------------------------------------------------------------------------------------------------------------- |
+| **Stages** | `stages`   | A list of `PipelineStage` (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, Process, 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                                                                                                                                                                    |
+| :--------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **kickoff**      | Executes the pipeline, processing all stages and returning the results. This method initiates one or more kickoffs through the pipeline, handling the flow of data between stages. |
+| **process_runs** | Runs the pipeline for each input provided, handling the flow and transformation 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 kickoff.                                           |
+| **Pydantic**      | `pydantic`      | `Any`                      | A Pydantic model object representing the structured output of the final stage, if applicable. |
+| **JSON Dict**     | `json_dict`     | `Union[Dict[str, Any], None]` | A dictionary representing the JSON output of the final stage, if applicable.                  |
+| **Token Usage**   | `token_usage`   | `Dict[str, UsageMetrics]`  | A summary of token usage across all stages of the pipeline kickoff.                          |
+| **Trace**         | `trace`         | `List[Any]`                | A trace of the journey of inputs through the pipeline kickoff.                                |
+| **Crews Outputs** | `crews_outputs` | `List[CrewOutput]`         | A list of `CrewOutput` objects, representing the outputs from each crew in the pipeline kickoff.  |
+
+### 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=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 +1,133 @@
+---
+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 fledged 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              | Description                                                                                   |
-| :----------------------| :-------------------------------------------------------------------------------------------- |
-| **Description**        | A clear, concise statement of what the task entails.                                          |
-| **Agent**              | The agent responsible for the task, assigned either directly or by the crew's process.        |
-| **Expected Output**    | A detailed description of what the task's completion looks like.                              |
-| **Tools** *(optional)* | The functions or capabilities the agent can utilize to perform the task.                      |
-| **Async Execution** *(optional)* | If set, the task executes asynchronously, allowing progression without waiting for completion.|
-| **Context**  *(optional)* | Specifies tasks whose outputs are used as context for this task.                              |
-| **Config** *(optional)* | Additional configuration details for the agent executing the task, allowing further customization. |
-| **Output JSON**  *(optional)* | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.         |
-| **Output Pydantic**  *(optional)* | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
-| **Output File**  *(optional)* | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Callback**  *(optional)* | A Python callable that is executed with the task's output upon completion.                    |
-| **Human Input** *(optional)* | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
+| Attribute                        | Parameters        | Type                          | Description                                                                                                          |
+| :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Description**                  | `description`     | `str`                         | A clear, concise statement of what the task entails.                                                                 |
+| **Agent**                        | `agent`           | `Optional[BaseAgent]`         | The agent responsible for the task, assigned either directly or by the crew's process.                               |
+| **Expected Output**              | `expected_output` | `str`                         | A detailed description of what the task's completion looks like.                                                     |
+| **Tools** _(optional)_           | `tools`           | `Optional[List[Any]]`         | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list.                  |
+| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False.    |
+| **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Specifies tasks whose outputs are used as context for this task.                                                     |
+| **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
+| **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
+| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
+| **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
+| **Output** _(optional)_          | `output`          | `Optional[TaskOutput]`        | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details.                  |
+| **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | A callable that is executed with the task's output upon completion.                                                  |
+| **Human Input** _(optional)_     | `human_input`     | `Optional[bool]`              | Indicates if the task should involve human review at the end, useful for tasks needing human oversight. Defaults to False.|
+| **Converter Class** _(optional)_ | `converter_cls`   | `Optional[Type[Converter]]`   | 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 output, 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`                      | Description of the task.                                                                           |
+| **Summary**       | `summary`       | `Optional[str]`            | 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 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()
@@ -167,7 +234,7 @@ def callback_function(output: TaskOutput):
     print(f"""
         Task completed!
         Task: {output.description}
-        Output: {output.raw_output}
+        Output: {output.raw}
     """)
 
 research_task = Task(
@@ -199,7 +266,7 @@ task1 = Task(
 crew = Crew(
     agents=[research_agent],
     tasks=[task1, task2, task3],
-    verbose=2
+    verbose=True
 )
 
 result = crew.kickoff()
@@ -208,7 +275,7 @@ result = crew.kickoff()
 print(f"""
     Task completed!
     Task: {task1.output.description}
-    Output: {task1.output.raw_output}
+    Output: {task1.output.raw}
 """)
 ```
 

docs/core-concepts/Testing.md

@@ -0,0 +1,56 @@
+---
+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
+```
+
+or using the short forms:
+
+```bash
+crewai test -n 5 -m 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:
+
+```
+                                                     Tasks Scores
+                                                (1-10 Higher is better)
+┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━┯━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Tasks/Crew/Agents  │ Run 1 │ Run 2 │ Avg. Total │ Agents                         │                                 ┃
+┠────────────────────┼───────┼───────┼────────────┼────────────────────────────────┼─────────────────────────────────┨
+┃ Task 1             │  9.0  │  9.5  │    9.2     │ - Professional Insights        │                                 ┃
+┃                    │       │       │            │ Researcher                     │                                 ┃
+┃                    │       │       │            │                                │                                 ┃
+┃ Task 2             │  9.0  │ 10.0  │    9.5     │ - Company Profile Investigator │                                 ┃
+┃                    │       │       │            │                                │                                 ┃
+┃ Task 3             │  9.0  │  9.0  │    9.0     │ - Automation Insights          │                                 ┃
+┃                    │       │       │            │ Specialist                     │                                 ┃
+┃                    │       │       │            │                                │                                 ┃
+┃ Task 4             │  9.0  │  9.0  │    9.0     │ - Final Report Compiler        │                                 ┃
+┃                    │       │       │            │                                │ - Automation Insights           ┃
+┃                    │       │       │            │                                │ Specialist                      ┃
+┃ Crew               │ 9.00  │ 9.38  │    9.2     │                                │                                 ┃
+┃ Execution Time (s) │  126  │  145  │    135     │                                │                                 ┃
+┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━┷━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+```
+
+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
@@ -100,28 +101,36 @@ 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. |
-| **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.                 |
-| **WebsiteSearchTool**       | A RAG tool for searching website content, optimized for web data extraction.                   |
-| **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.               |
+| **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.                |
+| **WebsiteSearchTool**       | A RAG tool for searching website content, optimized for web data extraction.                  |
+| **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.         |
 
 ## Creating your own Tools
 
@@ -135,6 +144,7 @@ pip install 'crewai[tools]'
 ```
 
 Once you do that there are two main ways for one to create a crewAI tool:
+
 ### Subclassing `BaseTool`
 
 ```python
@@ -189,6 +199,5 @@ 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

@@ -0,0 +1,54 @@
+---
+title: crewAI Train
+description: Learn how to train your crewAI agents by giving them feedback early on and get consistent results.
+---
+
+## Introduction
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
+
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. This helps the agents improve their understanding, decision-making, and problem-solving abilities.
+
+### Training Your Crew Using the CLI
+To use the training 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:
+
+```shell
+crewai train -n <n_iterations> <filename> (optional)
+```
+
+!!! 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:
+
+1. Define the number of iterations for training.
+2. Specify the input parameters for the training process.
+3. Execute the training command within a try-except block to handle potential errors.
+
+```python
+n_iterations = 2
+inputs = {"topic": "CrewAI Training"}
+filename = "your_model.pkl"
+
+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}")
+```
+
+### 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.
+
+Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
+
+Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
+
+Happy training with CrewAI!

docs/core-concepts/Using-LangChain-Tools.md

@@ -5,9 +5,10 @@ description: Learn how to integrate LangChain tools with CrewAI agents to enhanc
 
 ## Using LangChain Tools
 !!! info "LangChain Integration"
-    CrewAI seamlessly integrates with LangChain’s comprehensive toolkit for search-based queries and more, here are the available built-in tools that are offered by Langchain [LangChain Toolkit](https://python.langchain.com/docs/integrations/tools/)
+    CrewAI seamlessly integrates with LangChain’s comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with crewAI.
 
 ```python
+import os
 from crewai import Agent
 from langchain.agents import Tool
 from langchain.utilities import GoogleSerperAPIWrapper

docs/core-concepts/Using-LlamaIndex-Tools.md

@@ -35,10 +35,10 @@ query_tool = LlamaIndexTool.from_query_engine(
 
 # Create and assign the tools to an agent
 agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
-  tools=[tool, *tools, query_tool]
+    role='Research Analyst',
+    goal='Provide up-to-date market analysis',
+    backstory='An expert analyst with a keen eye for market trends.',
+    tools=[tool, *tools, query_tool]
 )
 
 # rest of the code ...
@@ -54,4 +54,4 @@ To effectively use the LlamaIndexTool, follow these steps:
     pip install 'crewai[tools]'
     ```
 
-2. **Install and Use LlamaIndex**: Follow LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
+2. **Install and Use LlamaIndex**: Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.

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

@@ -0,0 +1,163 @@
+# 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 1: Defining a Two-Stage Sequential Pipeline
+
+Here's an example of how to define a pipeline with sequential stages in `src/<project_name>/pipelines/pipeline.py`:
+
+```python
+from crewai import Pipeline
+from crewai.project import PipelineBase
+from ..crews.research_crew.research_crew import ResearchCrew
+from ..crews.write_x_crew.write_x_crew import WriteXCrew
+
+@PipelineBase
+class SequentialPipeline:
+    def __init__(self):
+        # Initialize crews
+        self.research_crew = ResearchCrew().crew()
+        self.write_x_crew = WriteXCrew().crew()
+
+    def create_pipeline(self):
+        return Pipeline(
+            stages=[
+                self.research_crew,
+                self.write_x_crew
+            ]
+        )
+
+    async def kickoff(self, inputs):
+        pipeline = self.create_pipeline()
+        results = await pipeline.kickoff(inputs)
+        return results
+```
+
+## Example 2: Defining a Two-Stage Pipeline with Parallel Execution
+
+```python
+from crewai import Pipeline
+from crewai.project import PipelineBase
+from ..crews.research_crew.research_crew import ResearchCrew
+from ..crews.write_x_crew.write_x_crew import WriteXCrew
+from ..crews.write_linkedin_crew.write_linkedin_crew import WriteLinkedInCrew
+
+@PipelineBase
+class ParallelExecutionPipeline:
+    def __init__(self):
+        # Initialize crews
+        self.research_crew = ResearchCrew().crew()
+        self.write_x_crew = WriteXCrew().crew()
+        self.write_linkedin_crew = WriteLinkedInCrew().crew()
+
+    def create_pipeline(self):
+        return Pipeline(
+            stages=[
+                self.research_crew,
+                [self.write_x_crew, self.write_linkedin_crew]  # Parallel execution
+            ]
+        )
+
+    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 +1,236 @@
+---
+
+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 `tasks.yaml` file. Ensure your annotated agent and function name are the same; otherwise, your task won't recognize the reference properly.
+
+#### Example References
+
+`agents.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
+```
+
+`tasks.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`
+* `@task`
+* `@crew`
+* `@tool`
+* `@callback`
+* `@output_json`
+* `@output_pydantic`
+* `@cache_handler`
+
+`crew.py`
+
+```python
+# ...
+@agent
+def email_summarizer(self) -> Agent:
+    return Agent(
+        config=self.agents_config["email_summarizer"],
+    )
+
+@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.
+
+#### tasks.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

@@ -0,0 +1,78 @@
+---
+title: Coding Agents
+description: Learn how to enable your crewAI Agents to write and execute code, and explore advanced features for enhanced functionality.
+---
+
+## Introduction
+
+crewAI Agents now have the powerful ability to write and execute code, significantly enhancing their problem-solving capabilities. This feature is particularly useful for tasks that require computational or programmatic solutions.
+
+## Enabling Code Execution
+
+To enable code execution for an agent, set the `allow_code_execution` parameter to `True` when creating the agent. Here's an example:
+
+```python
+from crewai import Agent
+
+coding_agent = Agent(
+    role="Senior Python Developer",
+    goal="Craft well-designed and thought-out code",
+    backstory="You are a senior Python developer with extensive experience in software architecture and best practices.",
+    allow_code_execution=True
+)
+```
+
+**Note**: The `allow_code_execution` parameter defaults to `False`.
+
+## Important Considerations
+
+1. **Model Selection**: It is strongly recommended to use more capable models like Claude 3.5 Sonnet and GPT-4 when enabling code execution. These models have a better understanding of programming concepts and are more likely to generate correct and efficient code.
+
+2. **Error Handling**: The code execution feature includes error handling. If executed code raises an exception, the agent will receive the error message and can attempt to correct the code or provide alternative solutions. The `max_retry_limit` parameter, which defaults to 2, controls the maximum number of retries for a task.
+
+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."
+
+## Code Execution Process
+
+When an agent with code execution enabled encounters a task requiring programming:
+
+1. The agent analyzes the task and determines that code execution is necessary.
+2. It formulates the Python code needed to solve the problem.
+3. The code is sent to the internal code execution tool (`CodeInterpreterTool`).
+4. The tool executes the code in a controlled environment and returns the result.
+5. The agent interprets the result and incorporates it into its response or uses it for further problem-solving.
+
+## Example Usage
+
+Here's a detailed example of creating an agent with code execution capabilities and using it in a task:
+
+```python
+from crewai import Agent, Task, Crew
+
+# 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 a task that requires code execution
+data_analysis_task = Task(
+    description="Analyze the given dataset and calculate the average age of participants.",
+    agent=coding_agent
+)
+
+# Create a crew and add the task
+analysis_crew = Crew(
+    agents=[coding_agent],
+    tasks=[data_analysis_task]
+)
+
+# Execute the crew
+result = analysis_crew.kickoff()
+
+print(result)
+```
+
+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, the task will be skipped, if true, then execute the 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="A complete report on the customer and their customers and competitors, including their demographics, preferences, market positioning and audience engagement.",
+    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
+)
+
+# 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
@@ -51,7 +52,7 @@ To optimize tool performance with caching, define custom caching strategies usin
 @tool("Tool with Caching")
 def cached_tool(argument: str) -> str:
     """Tool functionality description."""
-    return "Cachable result"
+    return "Cacheable result"
 
 def my_cache_strategy(arguments: dict, result: str) -> bool:
     # Define custom caching logic

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

@@ -1,136 +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, and more.
-
----
-
-## 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.
-
-## 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 like verbose mode, memory usage, and the ability to set specific agents as managers. These elements add depth and guide their task execution and interaction within the crew.
-
-```python
-import os
-os.environ["SERPER_API_KEY"] = "Your Key"  # serper.dev API key
-os.environ["OPENAI_API_KEY"] = "Your Key"
-
-from crewai import Agent
-from crewai_tools import SerperDevTool, BrowserbaseTool, ExaSearchTool
-search_tool = SerperDevTool()
-browser_tool = BrowserbaseTool()
-exa_search_tool = ExaSearchTool()
-
-# Creating a senior researcher agent with memory and verbose mode
-researcher = Agent(
-  role='Senior Researcher',
-  goal='Uncover groundbreaking technologies in {topic}',
-  verbose=True,
-  memory=True,
-  backstory=(
-    "Driven by curiosity, you're at the forefront of"
-    "innovation, eager to explore and share knowledge that could change"
-    "the world."
-  ),
-  tools=[search_tool, browser_tool],
-)
-
-# Creating a writer agent with custom tools and delegation capability
-writer = Agent(
-  role='Writer',
-  goal='Narrate compelling tech stories about {topic}',
-  verbose=True,
-  memory=True,
-  backstory=(
-    "With a flair for simplifying complex topics, you craft"
-    "engaging narratives that captivate and educate, bringing new"
-    "discoveries to light in an accessible manner."
-  ),
-  tools=[exa_search_tool],
-  allow_delegation=False
-)
-
-# 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."
-  )
-)
-```
-
-## Step 2: Define the Tasks
-Detail the specific objectives for your agents, including new features for asynchronous execution and output customization. These tasks ensure a targeted approach to their roles.
-
-```python
-from crewai import Task
-
-# Research task
-research_task = Task(
-  description=(
-    "Identify the next big trend in {topic}."
-    "Focus on identifying pros and cons and the overall narrative."
-    "Your final report should clearly articulate the key points,"
-    "its market opportunities, and potential risks."
-  ),
-  expected_output='A comprehensive 3 paragraphs long report on the latest AI trends.',
-  tools=[search_tool],
-  agent=researcher,
-  callback="research_callback",  # Example of task callback
-  human_input=True
-)
-
-# Writing task with language model configuration
-write_task = Task(
-  description=(
-    "Compose an insightful article on {topic}."
-    "Focus on the latest trends and how it's impacting the industry."
-    "This article should be easy to understand, engaging, and positive."
-  ),
-  expected_output='A 4 paragraph article on {topic} advancements formatted as markdown.',
-  tools=[exa_search_tool],
-  agent=writer,
-  output_file='new-blog-post.md',  # Example of output customization
-)
-```
-
-## Step 3: Form the Crew
-Combine your agents into a crew, setting the workflow process they'll follow to accomplish the tasks. Now with options to configure language models for enhanced interaction and additional configurations for optimizing performance, such as creating directories when saving files.
-
-```python
-from crewai import Crew, Process
-
-# Forming the tech-focused crew with some enhanced configurations
-crew = Crew(
-  agents=[researcher, writer],
-  tasks=[research_task, write_task],
-  process=Process.sequential,  # Optional: Sequential task execution is default
-  memory=True,
-  cache=True,
-  max_rpm=100,
-  manager_agent=manager
-)
-```
-
-## Step 4: Kick It Off
-Initiate the process with your enhanced crew ready. Observe as your agents collaborate, leveraging their new capabilities for a successful project outcome. Input variables will be interpolated into the agents and tasks for a personalized approach.
-
-```python
-# Starting the task execution process with enhanced feedback
-result = crew.kickoff(inputs={'topic': 'AI in healthcare'})
-print(result)
-```
-
-## Conclusion
-Building and activating a crew in CrewAI has evolved with new functionalities. By incorporating verbose mode, memory capabilities, asynchronous task execution, output customization, language model configuration, and enhanced crew configurations, your AI team is more equipped than ever to tackle challenges efficiently. The depth of agent backstories and the precision of their objectives enrich collaboration, leading to successful project outcomes. This guide aims to provide you with a clear and detailed understanding of setting up and utilizing the CrewAI framework to its full potential.

docs/how-to/Customize-Prompts.md

@@ -91,4 +91,4 @@ Custom prompt files should be structured in JSON format and include all necessar
 - **Improved Usability**: Supports multiple languages, making it suitable for global projects.
 - **Consistency**: Ensures uniform prompt structures across different agents and tasks.
 
-By incorporating these updates, CrewAI provides users with the ability to fully customize and internationalize their agent prompts, making the platform more versatile and user-friendly.
+By incorporating these updates, CrewAI provides users with the ability to fully customize and internationalize their agent prompts, making the platform more versatile and user-friendly.

docs/how-to/Customizing-Agents.md

@@ -14,12 +14,15 @@ Crafting an efficient CrewAI team hinges on the ability to dynamically tailor yo
 - **Cache** *(Optional)*: Determines whether the agent should use a cache for tool usage.
 - **Max RPM**: Sets the maximum number of requests per minute (`max_rpm`). This attribute is optional and can be set to `None` for no limit, allowing for unlimited queries to external services if needed.
 - **Verbose** *(Optional)*: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
-- **Allow Delegation** *(Optional)*: `allow_delegation` controls whether the agent is allowed to delegate tasks to other agents.
-- **Max Iter** *(Optional)*: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 25, providing a balance between thoroughness and efficiency. Once the agent approaches this number, it will try its best to give a good answer.
+- **Allow Delegation** *(Optional)*: `allow_delegation` controls whether the agent is allowed to delegate tasks to other agents. This attribute is now set to `False` by default.
+- **Max Iter** *(Optional)*: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 25, providing a balance between thoroughness and efficiency.
 - **Max Execution Time** *(Optional)*: `max_execution_time` Sets the maximum execution time for an agent to complete a task.
 - **System Template** *(Optional)*: `system_template` defines the system format for the agent.
 - **Prompt Template** *(Optional)*: `prompt_template` defines the prompt format for the agent.
 - **Response Template** *(Optional)*: `response_template` defines the response format for the agent.
+- **Use System Prompt** *(Optional)*: `use_system_prompt` controls whether the agent will use a system prompt for task execution. Agents can now operate without system prompts.
+- **Respect Context Window**: `respect_context_window` renames the sliding context window attribute and enables it by default to maintain context size.
+- **Max Retry Limit**: `max_retry_limit` defines the maximum number of retries for an agent to execute a task when an error occurs.
 
 ## Advanced Customization Options
 Beyond the basic attributes, CrewAI allows for deeper customization to enhance an agent's behavior and capabilities significantly.
@@ -67,12 +70,11 @@ agent = Agent(
   verbose=True,
   max_rpm=None, # No limit on requests per minute
   max_iter=25, # Default value for maximum iterations
-  allow_delegation=False
 )
 ```
 
 ## Delegation and Autonomy
-Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default, the `allow_delegation` attribute is set to `True`, enabling agents to seek assistance or delegate tasks as needed. This default behavior promotes collaborative problem-solving and efficiency within the CrewAI ecosystem. If needed, delegation can be disabled to suit specific operational requirements.
+Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default, the `allow_delegation` attribute is now set to `False`, disabling agents to seek assistance or delegate tasks as needed. This default behavior can be changed to promote collaborative problem-solving and efficiency within the CrewAI ecosystem. If needed, delegation can be enabled to suit specific operational requirements.
 
 ### Example: Disabling Delegation for an Agent
 ```python
@@ -80,7 +82,7 @@ agent = Agent(
   role='Content Writer',
   goal='Write engaging content on market trends',
   backstory='A seasoned writer with expertise in market analysis.',
-  allow_delegation=False # Disabling delegation
+  allow_delegation=True # Enabling delegation
 )
 ```
 

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

@@ -0,0 +1,36 @@
+---
+title: Forcing Tool Output as Result
+description: Learn how to force tool output as the result in 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, avoiding any agent modification during the task execution.
+
+## Forcing Tool Output as Result
+To force the tool output as the result of an agent's task, you need to set the `result_as_answer` parameter to `True` when adding a tool to 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
+from my_tool import MyCustomTool
+
+# Create a coding agent with the custom tool
+coding_agent = Agent(
+        role="Data Scientist",
+        goal="Produce amazing reports on AI",
+        backstory="You work with data and AI",
+        tools=[MyCustomTool(result_as_answer=True)],
+    )
+
+# Assuming the tool's execution and result population occurs within the system
+task_result = coding_agent.execute_task(task)
+```
+
+## Workflow in Action
+
+1. **Task Execution**: The agent executes the task using the tool provided.
+2. **Tool Output**: The tool generates the output, which is captured as the task result.
+3. **Agent Interaction**: The agent may reflect and take learnings from the tool but the output is not modified.
+4. **Result Return**: The tool output is returned as the task result without any modifications.

docs/how-to/Hierarchical.md

@@ -16,6 +16,13 @@ By default, tasks in CrewAI are managed through a sequential process. However, a
 - **Task Delegation**: A manager agent allocates tasks among crew members based on their roles and capabilities.
 - **Result Validation**: The manager evaluates outcomes to ensure they meet the required standards.
 - **Efficient Workflow**: Emulates corporate structures, providing an organized approach to task management.
+- **System Prompt Handling**: Optionally specify whether the system should use predefined prompts.
+- **Stop Words Control**: Optionally specify whether stop words should be used, supporting various models including the o1 models.
+- **Context Window Respect**: Prioritize important context by enabling respect of the context window, which is now the default behavior.
+- **Delegation Control**: Delegation is now disabled by default to give users explicit control.
+- **Max Requests Per Minute**: Configurable option to set the maximum number of requests per minute.
+- **Max Iterations**: Limit the maximum number of iterations for obtaining a final answer.
+
 
 ## Implementing the Hierarchical Process
 To utilize the hierarchical process, it's essential to explicitly set the process attribute to `Process.hierarchical`, as the default behavior is `Process.sequential`. Define a crew with a designated manager and establish a clear chain of command.
@@ -38,6 +45,9 @@ researcher = Agent(
     cache=True,
     verbose=False,
     # tools=[]  # This can be optionally specified; defaults to an empty list
+    use_system_prompt=True,  # Enable or disable system prompts for this agent
+    max_rpm=30,  # Limit on the number of requests per minute
+    max_iter=5  # Maximum number of iterations for a final answer
 )
 writer = Agent(
     role='Writer',
@@ -46,6 +56,9 @@ writer = Agent(
     cache=True,
     verbose=False,
     # tools=[]  # Optionally specify tools; defaults to an empty list
+    use_system_prompt=True,  # Enable or disable system prompts for this agent
+    max_rpm=30,  # Limit on the number of requests per minute
+    max_iter=5  # Maximum number of iterations for a final answer
 )
 
 # Establishing the crew with a hierarchical process and additional configurations
@@ -54,8 +67,10 @@ project_crew = Crew(
     agents=[researcher, writer],
     manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),  # Mandatory if manager_agent is not set
     process=Process.hierarchical,  # Specifies the hierarchical management approach
+    respect_context_window=True,  # Enable respect of the context window for tasks
     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

@@ -74,15 +74,17 @@ task2 = Task(
         "Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
     ),
     expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2024',
-    agent=writer
+    agent=writer,
+    human_input=True
 )
 
 # Instantiate your crew with a sequential process
 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

@@ -0,0 +1,117 @@
+---
+title: Kickoff Async
+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.
+
+### 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
+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 a task that requires code execution
+data_analysis_task = Task(
+    description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
+    agent=coding_agent
+)
+
+# Create a crew and add the task
+analysis_crew = Crew(
+    agents=[coding_agent],
+    tasks=[data_analysis_task]
+)
+
+# 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/Kickoff-for-each.md

@@ -0,0 +1,49 @@
+---
+title: Kickoff For Each
+description: Kickoff a Crew for a List
+---
+
+## Introduction
+CrewAI provides the ability to kickoff a crew for each item in a list, allowing you to execute the crew for each item in the list. This feature is particularly useful when you need to perform the same set of tasks for multiple items.
+
+## Kicking Off a Crew for Each Item
+To kickoff a crew for each item in a list, use the `kickoff_for_each()` method. This method executes the crew for each item in the list, allowing you to process multiple items efficiently.
+
+Here's an example of how to kickoff a crew for each item in a list:
+
+```python
+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 a task that requires code execution
+data_analysis_task = Task(
+    description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
+    agent=coding_agent,
+    expected_output="The average age calculated from the dataset"
+)
+
+# Create a crew and add the task
+analysis_crew = Crew(
+    agents=[coding_agent],
+    tasks=[data_analysis_task],
+    verbose=True,
+    memory=False,
+    respect_context_window=True  # enable by default
+)
+
+datasets = [
+  { "ages": [25, 30, 35, 40, 45] },
+  { "ages": [20, 25, 30, 35, 40] },
+  { "ages": [30, 35, 40, 45, 50] }
+]
+
+# Execute the crew
+result = analysis_crew.kickoff_for_each(inputs=datasets)
+```

docs/how-to/LLM-Connections.md

@@ -1,242 +1,163 @@
 ---
 title: Connect CrewAI to LLMs
-description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs), including detailed class attributes and methods.
+description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs) using LiteLLM, including supported providers and configuration options.
 ---
 
 ## Connect CrewAI to LLMs
+
+CrewAI uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface.
+
 !!! note "Default LLM"
-    By default, CrewAI uses OpenAI's GPT-4 model for language processing. You can configure your agents to use a different model or API. This guide shows how to connect your agents to various LLMs through environment variables and direct instantiation.
+    By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set. You can easily configure your agents to use a different model or provider 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.
+## Supported Providers
 
-## CrewAI Agent Overview
-The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. Here's an updated overview reflecting the latest codebase changes:
+LiteLLM supports a wide range of providers, including but not limited to:
 
-- **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.
+- OpenAI
+- Anthropic
+- Google (Vertex AI, Gemini)
+- Azure OpenAI
+- AWS (Bedrock, SageMaker)
+- Cohere
+- Hugging Face
+- Ollama
+- Mistral AI
+- Replicate
+- Together AI
+- AI21
+- Cloudflare Workers AI
+- DeepInfra
+- Groq
+- And many more!
+
+For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers).
+
+## Changing the LLM
+
+To use a different LLM with your CrewAI agents, you have several options:
+
+### 1. Using a String Identifier
+
+Pass the model name as a string when initializing the agent:
 
 ```python
-# Required
-os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
+from crewai import Agent
 
-# Agent will automatically use the model defined in the environment variable
-example_agent = Agent(
-  role='Local Expert',
-  goal='Provide insights about the city',
-  backstory="A knowledgeable local guide.",
-  verbose=True
+# Using OpenAI's GPT-4
+openai_agent = Agent(
+    role='OpenAI Expert',
+    goal='Provide insights using GPT-4',
+    backstory="An AI assistant powered by OpenAI's latest model.",
+    llm='gpt-4'
+)
+
+# Using Anthropic's Claude
+claude_agent = Agent(
+    role='Anthropic Expert',
+    goal='Analyze data using Claude',
+    backstory="An AI assistant leveraging Anthropic's language model.",
+    llm='claude-2'
 )
 ```
 
-## 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.
+### 2. Using the LLM Class
 
-### Setting Up Ollama
-- **Environment Variables Configuration**: To integrate Ollama, set the following environment variables:
-```sh
-OPENAI_API_BASE='http://localhost:11434/v1'
-OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model
-OPENAI_API_KEY=''
-```
+For more detailed configuration, use the LLM class:
 
-## 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. Create a ModelFile similar the one below in your project directory.
-```
-FROM llama2
-
-# Set parameters
-
-PARAMETER temperature 0.8
-PARAMETER stop Result
-
-# Sets a custom system message to specify the behavior of the chat assistant
-
-# Leaving it blank for now.
-
-SYSTEM """"""
-```
-4. Create a script to get the base model, which in our case is llama2, and create a model on top of that with ModelFile above. PS: this will be ".sh" file.
-```
-#!/bin/zsh
-
-# variables
-model_name="llama2"
-custom_model_name="crewai-llama2"
-
-#get the base model
-ollama pull $model_name
-
-#create the model file
-ollama create $custom_model_name -f ./Llama2ModelFile
-```
-5. Go into the directory where the script file and ModelFile is located and run the script.
-6. Enjoy your free Llama2 model that is powered up by excellent agents from CrewAI.
 ```python
-from crewai import Agent, Task, Crew
-from langchain_openai import ChatOpenAI
-import os
-os.environ["OPENAI_API_KEY"] = "NA"
+from crewai import Agent, LLM
 
-llm = ChatOpenAI(
-    model = "crewai-llama2",
-    base_url = "http://localhost:11434/v1")
-
-general_agent = Agent(role = "Math Professor",
-                      goal = """Provide the solution to the students that are asking mathematical questions and give them the answer.""",
-                      backstory = """You are an excellent math professor that likes to solve math questions in a way that everyone can understand your solution""",
-                      allow_delegation = False,
-                      verbose = True,
-                      llm = llm)
-
-task = Task(description="""what is 3 + 5""",
-             agent = general_agent,
-             expected_output="A numerical answer.")
-
-crew = Crew(
-            agents=[general_agent],
-            tasks=[task],
-            verbose=2
-        )
-
-result = crew.kickoff()
-
-print(result)
-```
-
-## HuggingFace Integration
-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
-
-llm = HuggingFaceEndpoint(
-    endpoint_url="<YOUR_ENDPOINT_URL_HERE>",
-    huggingfacehub_api_token="<HF_TOKEN_HERE>",
-    task="text-generation",
-    max_new_tokens=512
+llm = LLM(
+    model="gpt-4",
+    temperature=0.7,
+    base_url="https://api.openai.com/v1",
+    api_key="your-api-key-here"
 )
 
 agent = Agent(
-    role="HuggingFace Agent",
-    goal="Generate text using HuggingFace",
-    backstory="A diligent explorer of GitHub docs.",
+    role='Customized LLM Expert',
+    goal='Provide tailored responses',
+    backstory="An AI assistant with custom LLM settings.",
     llm=llm
 )
 ```
 
-### From HuggingFaceHub endpoint
-```python
-from langchain_community.llms import HuggingFaceHub
+## Configuration Options
 
-llm = HuggingFaceHub(
-    repo_id="HuggingFaceH4/zephyr-7b-beta",
-    huggingfacehub_api_token="<HF_TOKEN_HERE>",
-    task="text-generation",
+When configuring an LLM for your agent, you have access to a wide range of parameters:
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `model` | str | The name of the model to use (e.g., "gpt-4", "claude-2") |
+| `temperature` | float | Controls randomness in output (0.0 to 1.0) |
+| `max_tokens` | int | Maximum number of tokens to generate |
+| `top_p` | float | Controls diversity of output (0.0 to 1.0) |
+| `frequency_penalty` | float | Penalizes new tokens based on their frequency in the text so far |
+| `presence_penalty` | float | Penalizes new tokens based on their presence in the text so far |
+| `stop` | str, List[str] | Sequence(s) to stop generation |
+| `base_url` | str | The base URL for the API endpoint |
+| `api_key` | str | Your API key for authentication |
+
+For a complete list of parameters and their descriptions, refer to the LLM class documentation.
+
+## Connecting to OpenAI-Compatible LLMs
+
+You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
+
+### Using Environment Variables
+
+```python
+import os
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
+os.environ["OPENAI_MODEL_NAME"] = "your-model-name"
+```
+
+### Using LLM Class Attributes
+
+```python
+llm = LLM(
+    model="custom-model-name",
+    api_key="your-api-key",
+    base_url="https://api.your-provider.com/v1"
+)
+agent = Agent(llm=llm, ...)
+```
+
+## Using Local Models with Ollama
+
+For local models like those provided by Ollama:
+
+1. [Download and install Ollama](https://ollama.com/download)
+2. Pull the desired model (e.g., `ollama pull llama2`)
+3. Configure your agent:
+
+```python
+agent = Agent(
+    role='Local AI Expert',
+    goal='Process information using a local model',
+    backstory="An AI assistant running on local hardware.",
+    llm=LLM(model="ollama/llama2", base_url="http://localhost:11434")
 )
 ```
 
-## OpenAI Compatible API Endpoints
-Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, and Mistral AI.
+## Changing the Base API URL
 
-### Configuration Examples
-#### FastChat
-```sh
-OPENAI_API_BASE="http://localhost:8001/v1"
-OPENAI_MODEL_NAME='oh-2.5m7b-q51'
-OPENAI_API_KEY=NA
-```
+You can change the base API URL for any LLM provider by setting the `base_url` parameter:
 
-#### 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"
-```
-
-#### Mistral API
-```sh
-OPENAI_API_KEY=your-mistral-api-key
-OPENAI_API_BASE=https://api.mistral.ai/v1
-OPENAI_MODEL_NAME="mistral-small"
-```
-
-### Solar
 ```python
-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)
-
-# 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"
-llm = ChatCohere()
-
-# Free developer API key available here: https://cohere.com/
-# Langchain Documentation: https://python.langchain.com/docs/integrations/chat/cohere
-```
-
-### 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=""
-```
-
-### Example Agent with Azure LLM
-```python
-from dotenv import load_dotenv
-from crewai import Agent
-from langchain_openai import AzureChatOpenAI
-
-load_dotenv()
-
-azure_llm = AzureChatOpenAI(
-    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
-    api_key=os.environ.get("AZURE_OPENAI_KEY")
-)
-
-azure_agent = Agent(
-  role='Example Agent',
-  goal='Demonstrate custom LLM configuration',
-  backstory='A diligent explorer of GitHub docs.',
-  llm=azure_llm
+llm = LLM(
+    model="custom-model-name",
+    base_url="https://api.your-provider.com/v1",
+    api_key="your-api-key"
 )
+agent = Agent(llm=llm, ...)
 ```
 
+This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
+
 ## Conclusion
-Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.
+
+By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.

docs/how-to/Langtrace-Observability.md

@@ -1,44 +1,64 @@
 ---
 title: CrewAI Agent Monitoring with Langtrace
-description: How to monitor cost, latency, and performance of CrewAI Agents using Langtrace.
+description: How to monitor cost, latency, and performance of CrewAI Agents using Langtrace, an external observability tool.
 ---
 
 # Langtrace Overview
-Langtrace is an open-source tool that helps you set up observability and evaluations for LLMs, LLM frameworks, and VectorDB. With Langtrace, you can get deep visibility into the cost, latency, and performance of your CrewAI Agents. Additionally, you can log the hyperparameters and monitor for any performance regressions and set up a process to continuously improve your Agents.
+
+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 going to [https://langtrace.ai/signup](https://langtrace.ai/signup).
-2. Create a project and generate an API key.
-3. Install Langtrace in your code using the following commands.
-**Note**: For detailed instructions on integrating Langtrace, you can check out the official docs from [here](https://docs.langtrace.ai/supported-integrations/llm-frameworks/crewai).
+1. Sign up for [Langtrace](https://langtrace.ai/) by visiting [https://langtrace.ai/signup](https://langtrace.ai/signup).
+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
 # Install the SDK
 pip install langtrace-python-sdk
-
-# Import it into your project
-from langtrace_python_sdk import langtrace # Must precede any llm module imports
-langtrace.init(api_key = '<LANGTRACE_API_KEY>')
 ```
 
-### Features
-- **LLM Token and Cost tracking**
-- **Trace graph showing detailed execution steps with latency and logs**
-- **Dataset curation using manual annotation**
-- **Prompt versioning and management**
-- **Prompt Playground with comparison views between models**
-- **Testing and Evaluations**
+## Using Langtrace with CrewAI
 
-![Langtrace Cost and Usage Tracking](..%2Fassets%2Fcrewai-langtrace-stats.png)
-![Langtrace Span Graph and Logs Dashboard](..%2Fassets%2Fcrewai-langtrace-spans.png)
+To integrate Langtrace with your CrewAI project, follow these steps:
 
-#### Extra links
+1. Import and initialize Langtrace at the beginning of your script, before any CrewAI imports:
 
-<a href="https://x.com/langtrace_ai">🐦 Twitter</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://discord.com/invite/EaSATwtr4t">📢 Discord</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://langtrace.ai/">🖇 Website</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://docs.langtrace.ai/introduction">📙 Documentation</a>
+```python
+from langtrace_python_sdk import langtrace
+langtrace.init(api_key='<LANGTRACE_API_KEY>')
+
+# Now import CrewAI modules
+from crewai import Agent, Task, Crew
+```
+
+### Features and Their Application to CrewAI
+
+1. **LLM Token and Cost Tracking**
+
+   - Monitor the token usage and associated costs for each CrewAI agent interaction.
+
+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.
+
+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.

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

@@ -0,0 +1,58 @@
+---
+title: Replay Tasks from Latest Crew Kickoff
+description: Replay tasks from the latest crew.kickoff(...)
+
+---
+
+## Introduction
+CrewAI provides the ability to replay from a task specified from the latest crew kickoff. This feature is particularly useful when you've finished a kickoff and may want to retry certain tasks or don't need to refetch data over and your agents already have the context saved from the kickoff execution so you just need to replay the tasks you want to.
+
+## Note:
+You must run `crew.kickoff()` before you can replay a task. Currently, only the latest kickoff is supported, so if you use `kickoff_for_each`, it will only allow you to replay from the most recent crew run.
+
+Here's an example of how to replay from a task:
+
+### 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 commands:
+
+To view the latest kickoff task_ids use:
+```shell
+crewai log-tasks-outputs
+```
+
+Once you have your `task_id` to replay, use:
+```shell
+crewai replay -t <task_id>
+```
+
+**Note:** Ensure `crewai` is installed and configured correctly in your development environment.
+
+### 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():
+    """
+    Replay the crew execution from a specific task.
+    """
+    task_id = '<task_id>'
+    inputs = {"topic": "CrewAI Training"}  # This is optional; you can pass in the inputs you want to replay; otherwise, it uses the previous kickoff's inputs.
+    try:
+        YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
+
+    except subprocess.CalledProcessError as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")
+
+    except Exception as e:
+        raise Exception(f"An unexpected error occurred: {e}")
+```
+
+## Conclusion
+With the above enhancements and detailed functionality, replaying specific tasks in CrewAI has been made more efficient and robust. Ensure you follow the commands and steps precisely to make the most of these features.

docs/how-to/Sequential.md

@@ -15,10 +15,10 @@ The sequential process ensures tasks are executed one after the other, following
 - **Easy Monitoring**: Facilitates easy tracking of task completion and project progress.
 
 ## Implementing the Sequential Process
-Assemble your crew and define tasks in the order they need to be executed.
+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(
@@ -37,10 +37,10 @@ writer = Agent(
   backstory='A skilled writer with a talent for crafting compelling narratives'
 )
 
-# Define the tasks in sequence
-research_task = Task(description='Gather relevant data...', agent=researcher)
-analysis_task = Task(description='Analyze the data...', agent=analyst)
-writing_task = Task(description='Compose the report...', agent=writer)
+# 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')
 
 # Form the crew with a sequential process
 report_crew = Crew(
@@ -48,12 +48,48 @@ report_crew = Crew(
   tasks=[research_task, analysis_task, writing_task],
   process=Process.sequential
 )
+
+# Execute the crew
+result = report_crew.kickoff()
+
+# Accessing the type-safe output
+task_output: TaskOutput = result.tasks[0].output
+crew_output: CrewOutput = result.output
 ```
 
+### Note:
+Each task in a sequential process **must** have an agent assigned. Ensure that every `Task` includes an `agent` parameter.
+
 ### Workflow in Action
 1. **Initial Task**: In a sequential process, the first agent completes their task and signals completion.
-2. **Subsequent Tasks**: Agents pick up their tasks based on the process type, with outcomes of preceding tasks or manager directives guiding their execution.
+2. **Subsequent Tasks**: Agents pick up their tasks based on the process type, with outcomes of preceding tasks or directives guiding their execution.
 3. **Completion**: The process concludes once the final task is executed, leading to project completion.
 
-## Conclusion
-The sequential and hierarchical processes in CrewAI offer clear, adaptable paths for task execution. They are well-suited for projects requiring logical progression and dynamic decision-making, ensuring each step is completed effectively, thereby facilitating a cohesive final product.
+## Advanced Features
+
+### Task Delegation
+In sequential processes, if an agent has `allow_delegation` set to `True`, they can delegate tasks to other agents in the crew. This feature is automatically set up when there are multiple agents in the crew.
+
+### Asynchronous Execution
+Tasks can be executed asynchronously, allowing for parallel processing when appropriate. To create an asynchronous task, set `async_execution=True` when defining the task.
+
+### Memory and Caching
+CrewAI supports both memory and caching features:
+- **Memory**: Enable by setting `memory=True` when creating the Crew. This allows agents to retain information across tasks.
+- **Caching**: By default, caching is enabled. Set `cache=False` to disable it.
+
+### Callbacks
+You can set callbacks at both the task and step level:
+- `task_callback`: Executed after each task completion.
+- `step_callback`: Executed after each step in an agent's execution.
+
+### Usage Metrics
+CrewAI tracks token usage across all tasks and agents. You can access these metrics after execution.
+
+## Best Practices for Sequential Processes
+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.
+
+This updated documentation ensures that details accurately reflect the latest changes in the codebase and clearly describes how to leverage new features and configurations. The content is kept simple and direct to ensure easy understanding.

docs/how-to/Your-Own-Manager-Agent.md

@@ -1,12 +1,12 @@
 ---
-title: Ability to Set a Specific Agent as Manager in CrewAI
-description: Introducing the ability to set a specific agent as a manager instead of having CrewAI create one automatically.
+title: Setting a Specific Agent as Manager in CrewAI
+description: Learn how to set a custom agent as the manager in CrewAI, providing more control over task management and coordination.
 
 ---
 
-# Ability to Set a Specific Agent as Manager in CrewAI
+# Setting a Specific Agent as Manager in CrewAI
 
-CrewAI now allows users to set a specific agent as the manager of the crew, providing more control over the management and coordination of tasks. This feature enables the customization of the managerial role to better fit the project's requirements.
+CrewAI allows users to set a specific agent as the manager of the crew, providing more control over the management and coordination of tasks. This feature enables the customization of the managerial role to better fit your project's requirements.
 
 ## Using the `manager_agent` Attribute
 
@@ -23,46 +23,65 @@ from crewai import Agent, Task, Crew, Process
 # Define your agents
 researcher = Agent(
     role="Researcher",
-    goal="Make the best research and analysis on content about AI and AI agents",
-    backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
+    goal="Conduct thorough research and analysis on AI and AI agents",
+    backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
     allow_delegation=False,
 )
 
 writer = Agent(
     role="Senior Writer",
-    goal="Write the best content about AI and AI agents.",
-    backstory="You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
+    goal="Create compelling content about AI and AI agents",
+    backstory="You're a senior writer, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently writing content for a new client.",
     allow_delegation=False,
 )
 
 # Define your task
 task = Task(
-    description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
-    expected_output="5 bullet points with a paragraph for each idea.",
+    description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.",
+    expected_output="5 bullet points, each with a paragraph and accompanying notes.",
 )
 
 # Define the manager agent
 manager = Agent(
-    role="Manager",
-    goal="Manage the crew and ensure the tasks are completed efficiently.",
-    backstory="You're an experienced manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
-    allow_delegation=False,
+    role="Project Manager",
+    goal="Efficiently manage the crew and ensure high-quality task completion",
+    backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
+    allow_delegation=True,
 )
 
 # Instantiate your crew with a custom manager
 crew = Crew(
     agents=[researcher, writer],
-    process=Process.hierarchical,
-    manager_agent=manager,
     tasks=[task],
+    manager_agent=manager,
+    process=Process.hierarchical,
 )
 
-# Get your crew to work!
-crew.kickoff()
+# Start the crew's work
+result = crew.kickoff()
 ```
 
 ## Benefits of a Custom Manager Agent
 
-- **Enhanced Control**: Allows for a more tailored management approach, fitting the specific needs of the project.
-- **Improved Coordination**: Ensures that the tasks are efficiently coordinated and managed by an experienced agent.
-- **Customizable Management**: Provides the flexibility to define managerial roles and responsibilities that align with the project's goals.
+- **Enhanced Control**: Tailor the management approach to fit the specific needs of your project.
+- **Improved Coordination**: Ensure efficient task coordination and management by an experienced agent.
+- **Customizable Management**: Define managerial roles and responsibilities that align with your project's goals.
+
+## Setting a Manager LLM
+
+If you're using the hierarchical process and don't want to set a custom manager agent, you can specify the language model for the manager:
+
+```python
+from langchain_openai import ChatOpenAI
+
+manager_llm = ChatOpenAI(model_name="gpt-4")
+
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[task],
+    process=Process.hierarchical,
+    manager_llm=manager_llm
+)
+```
+
+Note: Either `manager_agent` or `manager_llm` must be set when using the hierarchical process.

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>
@@ -33,26 +53,41 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Crews
                 </a>
             </li>
+            <li>
+                <a href="./core-concepts/LLMs">
+                    LLMs
+                </a>
+            </li>
+            <li>
+                <a href="./core-concepts/Pipeline">
+                    Pipeline
+                </a>
+            </li>
+            <li>
+                <a href="./core-concepts/Training-Crew">
+                    Training
+                </a>
+            </li>
             <li>
                 <a href="./core-concepts/Memory">
                     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/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
@@ -78,16 +113,51 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Customizing Agents
                 </a>
             </li>
+            <li>
+                <a href="./how-to/Coding-Agents">
+                    Coding Agents
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Force-Tool-Ouput-as-Result">
+                    Forcing Tool Output as Result
+                </a>
+            </li>
             <li>
                 <a href="./how-to/Human-Input-on-Execution">
                     Human Input on Execution
                 </a>
             </li>
+            <li>
+                <a href="./how-to/Kickoff-async">
+                    Kickoff a Crew Asynchronously
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Kickoff-for-each">
+                    Kickoff a Crew for a List
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Replay-tasks-from-latest-Crew-Kickoff">
+                    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
                 </a>
             </li>
+            <li>
+                <a href="./how-to/Langtrace-Observability">
+                    Agent Monitoring with LangTrace
+                </a>
+            </li>
         </ul>
     </div>
     <div style="width:30%">

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

@@ -0,0 +1,41 @@
+# CodeInterpreterTool
+
+## Description
+This tool enables the Agent to execute Python 3 code that it has generated autonomously. The code is run in a secure, isolated environment, ensuring safety regardless of the content. 
+
+This 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
+
+- Docker
+
+## Installation
+Install the crewai_tools package
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+
+Remember that when using this tool, the code must be generated by the Agent itself. The code must be a Python3 code. And it will take some time for the first time to run because it needs to build the Docker image.
+
+```python
+from crewai import Agent
+from crewai_tools import CodeInterpreterTool
+
+Agent(
+    ...
+    tools=[CodeInterpreterTool()],
+)
+```
+
+We also provide a simple way to use it directly from the Agent.
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    ...
+    allow_code_execution=True,
+)
+```

docs/tools/ComposioTool.md

@@ -0,0 +1,72 @@
+# ComposioTool Documentation
+
+## Description
+
+This tools is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the composio SDK.
+
+## Installation
+
+To incorporate this tool into your project, follow the installation instructions below:
+
+```shell
+pip install composio-core
+pip install 'crewai[tools]'
+```
+
+after the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`.
+
+## Example
+
+The following example demonstrates how to initialize the tool and execute a github action:
+
+1. Initialize Composio tools
+
+```python
+from composio import App
+from crewai_tools import ComposioTool
+from crewai import Agent, Task
+
+
+tools = [ComposioTool.from_action(action=Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER)]
+```
+
+If you don't know what action you want to use, use `from_app` and `tags` filter to get relevant actions
+
+```python
+tools = ComposioTool.from_app(App.GITHUB, tags=["important"])
+```
+
+or use `use_case` to search relevant actions
+
+```python
+tools = ComposioTool.from_app(App.GITHUB, use_case="Star a github repository")
+```
+
+2. Define agent
+
+```python
+crewai_agent = Agent(
+    role="Github Agent",
+    goal="You take action on Github using Github APIs",
+    backstory=(
+        "You are AI agent that is responsible for taking actions on Github "
+        "on users behalf. You need to take action on Github using Github APIs"
+    ),
+    verbose=True,
+    tools=tools,
+)
+```
+
+3. Execute task
+
+```python
+task = Task(
+    description="Star a repo ComposioHQ/composio on GitHub",
+    agent=crewai_agent,
+    expected_output="if the star happened",
+)
+
+task.execute()
+```
+
+* More detailed list of tools can be found [here](https://app.composio.dev)

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/GitHubSearchTool.md

@@ -4,7 +4,7 @@
     We are still working on improving tools, so there might be unexpected behavior or changes in the future.
 
 ## Description
-The GithubSearchTool is a Read, Append, and Generate (RAG) tool specifically designed for conducting semantic searches within GitHub repositories. Utilizing advanced semantic search capabilities, it sifts through code, pull requests, issues, and repositories, making it an essential tool for developers, researchers, or anyone in need of precise information from GitHub.
+The GithubSearchTool is a Retrieval-Augmented Generation (RAG) tool specifically designed for conducting semantic searches within GitHub repositories. Utilizing advanced semantic search capabilities, it sifts through code, pull requests, issues, and repositories, making it an essential tool for developers, researchers, or anyone in need of precise information from GitHub.
 
 ## Installation
 To use the GithubSearchTool, first ensure the crewai_tools package is installed in your Python environment:

docs/tools/MDXSearchTool.md

@@ -4,7 +4,7 @@
     The MDXSearchTool is in continuous development. Features may be added or removed, and functionality could change unpredictably as we refine the tool.
 
 ## Description
-The MDX Search Tool is a component of the `crewai_tools` package aimed at facilitating advanced market data extraction. This tool is invaluable for researchers and analysts seeking quick access to market insights, especially within the AI sector. It simplifies the task of acquiring, interpreting, and organizing market data by interfacing with various data sources.
+The MDX Search Tool is a component of the `crewai_tools` package aimed at facilitating advanced markdown language extraction. It enables users to effectively search and extract relevant information from MD files using query-based searches. This tool is invaluable for data analysis, information management, and research tasks, streamlining the process of finding specific information within large document collections.
 
 ## Installation
 Before using the MDX Search Tool, ensure the `crewai_tools` package is installed. If it is not, you can install it with the following command:
@@ -59,4 +59,4 @@ tool = MDXSearchTool(
         ),
     )
 )
-```
+```

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/PDFSearchTool.md

@@ -29,7 +29,7 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
 ```
 
 ## Arguments
-- `pdf`: **Optinal** The PDF path for the search. Can be provided at initialization or within the `run` method's arguments. If provided at initialization, the tool confines its search to the specified document.
+- `pdf`: **Optional** The PDF path for the search. Can be provided at initialization or within the `run` method's arguments. If provided at initialization, the tool confines its search to the specified document.
 
 ## Custom model and embeddings
 

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/TXTSearchTool.md

@@ -31,7 +31,7 @@ tool = TXTSearchTool(txt='path/to/text/file.txt')
 ```
 
 ## Arguments
-- `txt` (str): **Optinal**. The path to the text file you want to search. This argument is only required if the tool was not initialized with a specific text file; otherwise, the search will be conducted within the initially provided text file.
+- `txt` (str): **Optional**. The path to the text file you want to search. This argument is only required if the tool was not initialized with a specific text file; otherwise, the search will be conducted within the initially provided text file.
 
 ## Custom model and embeddings
 

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
 
@@ -78,14 +78,14 @@ theme:
 
   palette:
     - scheme: default
-      primary: red
-      accent: red
+      primary: deep orange
+      accent: deep orange
       toggle:
         icon: material/brightness-7
         name: Switch to dark mode
     - scheme: slate
-      primary: red
-      accent: red
+      primary: deep orange
+      accent: deep orange
       toggle:
         icon: material/brightness-4
         name: Switch to light mode
@@ -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'
@@ -126,43 +129,62 @@ 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:
-    - 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'
     - Create your own Manager Agent: 'how-to/Your-Own-Manager-Agent.md'
     - Connecting to any LLM: 'how-to/LLM-Connections.md'
     - Customizing Agents: 'how-to/Customizing-Agents.md'
+    - Coding Agents: 'how-to/Coding-Agents.md'
+    - Forcing Tool Output as Result: 'how-to/Force-Tool-Ouput-as-Result.md'
     - Human Input on Execution: 'how-to/Human-Input-on-Execution.md'
+    - 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'
-    - 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 Search 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"
@@ -172,6 +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
@@ -187,6 +210,6 @@ extra:
     property: G-N3Q505TMQ6
   social:
     - icon: fontawesome/brands/twitter
-      link: https://twitter.com/joaomdmoura
+      link: https://x.com/crewAIInc
     - icon: fontawesome/brands/github
-      link: https://github.com/joaomdmoura/crewAI
+      link: https://github.com/crewAIInc/crewAI

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.32.2"
+version = "0.64.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
@@ -8,28 +8,34 @@ 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"
 pydantic = "^2.4.2"
-langchain = "^0.1.10"
+langchain = "^0.2.16"
 openai = "^1.13.3"
 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 = "^0.3.0", optional = true }
+regex = "^2024.9.11"
+crewai-tools = { version = "^0.12.1", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
-embedchain = "0.1.109"
 appdirs = "^1.4.4"
 jsonref = "^1.1.0"
+agentops = { version = "^0.3.0", optional = true }
+embedchain = "^0.1.114"
+json-repair = "^0.25.2"
+auth0-python = "^4.7.1"
+poetry = "^1.8.3"
+litellm = "^1.44.22"
 
 [tool.poetry.extras]
 tools = ["crewai-tools"]
+agentops = ["agentops"]
 
 [tool.poetry.group.dev.dependencies]
 isort = "^5.13.2"
@@ -43,12 +49,13 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.3.0"
+crewai-tools = "^0.12.1"
 
 [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"
@@ -56,7 +63,10 @@ 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

@@ -1,4 +1,18 @@
+import warnings
 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
+from crewai.llm import LLM
+
+
+warnings.filterwarnings(
+    "ignore",
+    message="Pydantic serializer warnings:",
+    category=UserWarning,
+    module="pydantic.main",
+)
+
+__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM"]

src/crewai/agent.py

@@ -1,33 +1,43 @@
-from copy import deepcopy
 import os
-import uuid
-from typing import Any, Dict, List, Optional, Tuple
+from inspect import signature
+from typing import Any, List, Optional, Union
+from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
-from langchain.agents.agent import RunnableAgent
-from langchain.agents.tools import tool as LangChainTool
-from langchain.tools.render import render_text_description
-from langchain_core.agents import AgentAction
-from langchain_core.callbacks import BaseCallbackHandler
-from langchain_openai import ChatOpenAI
-from pydantic import (
-    UUID4,
-    BaseModel,
-    ConfigDict,
-    Field,
-    InstanceOf,
-    PrivateAttr,
-    field_validator,
-    model_validator,
-)
-from pydantic_core import PydanticCustomError
-
-from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler
+from crewai.agents import CacheHandler
+from crewai.utilities import Converter, Prompts
+from crewai.tools.agent_tools import AgentTools
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.memory.contextual.contextual_memory import ContextualMemory
-from crewai.utilities import I18N, Logger, Prompts, RPMController
-from crewai.utilities.token_counter_callback import TokenCalcHandler, TokenProcess
+from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
+from crewai.utilities.training_handler import CrewTrainingHandler
+from crewai.utilities.token_counter_callback import TokenCalcHandler
+from crewai.llm import LLM
 
 
-class Agent(BaseModel):
+def mock_agent_ops_provider():
+    def track_agent(*args, **kwargs):
+        def noop(f):
+            return f
+
+        return noop
+
+    return track_agent
+
+
+agentops = None
+
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        from agentops import track_agent
+    except ImportError:
+        track_agent = mock_agent_ops_provider()
+else:
+    track_agent = mock_agent_ops_provider()
+
+
+@track_agent()
+class Agent(BaseAgent):
     """Represents an agent in a system.
 
     Each agent has a role, a goal, a backstory, and an optional language model (llm).
@@ -48,61 +58,15 @@ class Agent(BaseModel):
             allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
             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
     """
 
-    __hash__ = object.__hash__  # type: ignore
-    _logger: Logger = PrivateAttr()
-    _rpm_controller: RPMController = PrivateAttr(default=None)
-    _request_within_rpm_limit: Any = PrivateAttr(default=None)
-    _token_process: TokenProcess = TokenProcess()
-
-    formatting_errors: int = 0
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-    role: str = Field(description="Role of the agent")
-    goal: str = Field(description="Objective of the agent")
-    backstory: str = Field(description="Backstory of the agent")
-    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,
-    )
-    max_rpm: Optional[int] = Field(
-        default=None,
-        description="Maximum number of requests per minute for the agent execution to be respected.",
-    )
-    verbose: bool = Field(
-        default=False, description="Verbose mode for the Agent Execution"
-    )
-    allow_delegation: bool = Field(
-        default=True, description="Allow delegation of tasks to agents"
-    )
-    tools: Optional[List[Any]] = Field(
-        default_factory=list, description="Tools at agents disposal"
-    )
-    max_iter: Optional[int] = Field(
-        default=25, description="Maximum iterations for an agent to execute a task"
-    )
+    _times_executed: int = PrivateAttr(default=0)
     max_execution_time: Optional[int] = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
     )
-    agent_executor: InstanceOf[CrewAgentExecutor] = Field(
-        default=None, description="An instance of the CrewAgentExecutor class."
-    )
-    crew: Any = Field(
-        default=None, description="Crew to which the agent belongs.")
-    tools_handler: InstanceOf[ToolsHandler] = Field(
-        default=None, description="An instance of the ToolsHandler class."
-    )
+    agent_ops_agent_name: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
+    agent_ops_agent_id: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
     cache_handler: InstanceOf[CacheHandler] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
@@ -110,20 +74,16 @@ class Agent(BaseModel):
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    i18n: I18N = Field(
-        default=I18N(), description="Internationalization settings.")
-    llm: Any = Field(
-        default_factory=lambda: ChatOpenAI(
-            model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4o")
-        ),
-        description="Language model that will run the agent.",
+    use_system_prompt: Optional[bool] = Field(
+        default=True,
+        description="Use system prompt for the agent.",
+    )
+    llm: Union[str, InstanceOf[LLM], Any] = Field(
+        description="Language model that will run the agent.", default=None
     )
     function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field(
-        default=None, description="Callback to be executed"
-    )
     system_template: Optional[str] = Field(
         default=None, description="System format for the agent."
     )
@@ -133,64 +93,92 @@ class Agent(BaseModel):
     response_template: Optional[str] = Field(
         default=None, description="Response format for the agent."
     )
+    tools_results: Optional[List[Any]] = Field(
+        default=[], description="Results of the tools used by the agent."
+    )
+    allow_code_execution: Optional[bool] = Field(
+        default=False, description="Enable code execution for the agent."
+    )
+    respect_context_window: bool = Field(
+        default=True,
+        description="Keep messages under the context window size by summarizing content.",
+    )
+    max_iter: int = Field(
+        default=20,
+        description="Maximum number of iterations for an agent to execute a task before giving it's best answer",
+    )
+    max_retry_limit: int = Field(
+        default=2,
+        description="Maximum number of retries for an agent to execute a task when an error occurs.",
+    )
 
-    _original_role: str | None = None
-    _original_goal: str | None = None
-    _original_backstory: str | None = None
+    @model_validator(mode="after")
+    def post_init_setup(self):
+        self.agent_ops_agent_name = self.role
 
-    def __init__(__pydantic_self__, **data):
-        config = data.pop("config", {})
-        super().__init__(**config, **data)
+        # Handle different cases for self.llm
+        if isinstance(self.llm, str):
+            # If it's a string, create an LLM instance
+            self.llm = LLM(model=self.llm)
+        elif isinstance(self.llm, LLM):
+            # If it's already an LLM instance, keep it as is
+            pass
+        elif self.llm is None:
+            # If it's None, use environment variables or default
+            model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
+            llm_params = {"model": model_name}
 
-    @field_validator("id", mode="before")
-    @classmethod
-    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
-        if v:
-            raise PydanticCustomError(
-                "may_not_set_field", "This field is not to be set by the user.", {}
+            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
+                "OPENAI_BASE_URL"
             )
+            if api_base:
+                llm_params["base_url"] = api_base
 
-    @model_validator(mode="after")
-    def set_attributes_based_on_config(self) -> "Agent":
-        """Set attributes based on the agent configuration."""
-        if self.config:
-            for key, value in self.config.items():
-                setattr(self, key, value)
-        return self
+            api_key = os.environ.get("OPENAI_API_KEY")
+            if api_key:
+                llm_params["api_key"] = api_key
 
-    @model_validator(mode="after")
-    def set_private_attrs(self):
-        """Set private attributes."""
-        self._logger = Logger(self.verbose)
-        if self.max_rpm and not self._rpm_controller:
-            self._rpm_controller = RPMController(
-                max_rpm=self.max_rpm, logger=self._logger
-            )
-        return self
+            self.llm = LLM(**llm_params)
+        else:
+            # For any other type, attempt to extract relevant attributes
+            llm_params = {
+                "model": getattr(self.llm, "model_name", None)
+                or getattr(self.llm, "deployment_name", None)
+                or str(self.llm),
+                "temperature": getattr(self.llm, "temperature", None),
+                "max_tokens": getattr(self.llm, "max_tokens", None),
+                "logprobs": getattr(self.llm, "logprobs", None),
+                "timeout": getattr(self.llm, "timeout", None),
+                "max_retries": getattr(self.llm, "max_retries", None),
+                "api_key": getattr(self.llm, "api_key", None),
+                "base_url": getattr(self.llm, "base_url", None),
+                "organization": getattr(self.llm, "organization", None),
+            }
+            # Remove None values to avoid passing unnecessary parameters
+            llm_params = {k: v for k, v in llm_params.items() if v is not None}
+            self.llm = LLM(**llm_params)
 
-    @model_validator(mode="after")
-    def set_agent_executor(self) -> "Agent":
-        """set agent executor is set."""
-        if hasattr(self.llm, "model_name"):
-            token_handler = TokenCalcHandler(
-                self.llm.model_name, self._token_process)
-
-            # Ensure self.llm.callbacks is a list
-            if not isinstance(self.llm.callbacks, list):
-                self.llm.callbacks = []
-
-            # 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)
+        # Similar handling for function_calling_llm
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, str):
+                self.function_calling_llm = LLM(model=self.function_calling_llm)
+            elif not isinstance(self.function_calling_llm, LLM):
+                self.function_calling_llm = LLM(
+                    model=getattr(self.function_calling_llm, "model_name", None)
+                    or getattr(self.function_calling_llm, "deployment_name", None)
+                    or str(self.function_calling_llm)
+                )
 
         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_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,
@@ -208,8 +196,7 @@ class Agent(BaseModel):
             Output of the agent
         """
         if self.tools_handler:
-            # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
-            self.tools_handler.last_used_tool = {}
+            self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
 
         task_prompt = task.prompt()
 
@@ -228,178 +215,112 @@ class Agent(BaseModel):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        tools = tools or self.tools
-        # type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]"
-        parsed_tools = self._parse_tools(tools)
+        tools = tools or self.tools or []
+        self.create_agent_executor(tools=tools, task=task)
 
-        self.create_agent_executor(tools=tools)
-        self.agent_executor.tools = parsed_tools
-        self.agent_executor.task = task
+        if self.crew and self.crew._train:
+            task_prompt = self._training_handler(task_prompt=task_prompt)
+        else:
+            task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
-        self.agent_executor.tools_description = render_text_description(
-            parsed_tools)
-        self.agent_executor.tools_names = self.__tools_names(parsed_tools)
+        try:
+            result = self.agent_executor.invoke(
+                {
+                    "input": task_prompt,
+                    "tool_names": self.agent_executor.tools_names,
+                    "tools": self.agent_executor.tools_description,
+                    "ask_for_human_input": task.human_input,
+                }
+            )["output"]
+        except Exception as e:
+            self._times_executed += 1
+            if self._times_executed > self.max_retry_limit:
+                raise e
+            result = self.execute_task(task, context, tools)
 
-        result = self.agent_executor.invoke(
-            {
-                "input": task_prompt,
-                "tool_names": self.agent_executor.tools_names,
-                "tools": self.agent_executor.tools_description,
-            }
-        )["output"]
-
-        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
+        # set to True, return the results of the last tool that had
+        # result_as_answer set to True
+        for tool_result in self.tools_results:  # type: ignore # Item "None" of "list[Any] | None" has no attribute "__iter__" (not iterable)
+            if tool_result.get("result_as_answer", False):
+                result = tool_result["result"]
+
         return result
 
-    def set_cache_handler(self, cache_handler: CacheHandler) -> None:
-        """Set the cache handler for the agent.
-
-        Args:
-            cache_handler: An instance of the CacheHandler class.
-        """
-        self.tools_handler = ToolsHandler()
-        if self.cache:
-            self.cache_handler = cache_handler
-            self.tools_handler.cache = cache_handler
-        self.create_agent_executor()
-
-    def set_rpm_controller(self, rpm_controller: RPMController) -> None:
-        """Set the rpm controller for the agent.
-
-        Args:
-            rpm_controller: An instance of the RPMController class.
-        """
-        if not self._rpm_controller:
-            self._rpm_controller = rpm_controller
-            self.create_agent_executor()
-
-    def create_agent_executor(self, tools=None) -> None:
+    def create_agent_executor(self, tools=None, task=None) -> None:
         """Create an agent executor for the agent.
 
         Returns:
             An instance of the CrewAgentExecutor class.
         """
-        tools = tools or self.tools
-
-        agent_args = {
-            "input": lambda x: x["input"],
-            "tools": lambda x: x["tools"],
-            "tool_names": lambda x: x["tool_names"],
-            "agent_scratchpad": lambda x: self.format_log_to_str(
-                x["intermediate_steps"]
-            ),
-        }
-
-        executor_args = {
-            "llm": self.llm,
-            "i18n": self.i18n,
-            "crew": self.crew,
-            "crew_agent": self,
-            "tools": self._parse_tools(tools),
-            "verbose": self.verbose,
-            "original_tools": tools,
-            "handle_parsing_errors": True,
-            "max_iterations": self.max_iter,
-            "max_execution_time": self.max_execution_time,
-            "step_callback": self.step_callback,
-            "tools_handler": self.tools_handler,
-            "function_calling_llm": self.function_calling_llm,
-            "callbacks": self.callbacks,
-        }
-
-        if self._rpm_controller:
-            executor_args["request_within_rpm_limit"] = (
-                self._rpm_controller.check_or_wait
-            )
+        tools = tools or self.tools or []
+        parsed_tools = self._parse_tools(tools)
 
         prompt = Prompts(
-            i18n=self.i18n,
+            agent=self,
             tools=tools,
+            i18n=self.i18n,
+            use_system_prompt=self.use_system_prompt,
             system_template=self.system_template,
             prompt_template=self.prompt_template,
             response_template=self.response_template,
         ).task_execution()
 
-        execution_prompt = prompt.partial(
-            goal=self.goal,
-            role=self.role,
-            backstory=self.backstory,
-        )
-
         stop_words = [self.i18n.slice("observation")]
+
         if self.response_template:
             stop_words.append(
                 self.response_template.split("{{ .Response }}")[1].strip()
             )
 
-        bind = self.llm.bind(stop=stop_words)
-        inner_agent = agent_args | execution_prompt | bind | CrewAgentParser(
-            agent=self)
         self.agent_executor = CrewAgentExecutor(
-            agent=RunnableAgent(runnable=inner_agent), **executor_args
+            llm=self.llm,
+            task=task,
+            agent=self,
+            crew=self.crew,
+            tools=parsed_tools,
+            prompt=prompt,
+            original_tools=tools,
+            stop_words=stop_words,
+            max_iter=self.max_iter,
+            tools_handler=self.tools_handler,
+            tools_names=self.__tools_names(parsed_tools),
+            tools_description=self._render_text_description_and_args(parsed_tools),
+            step_callback=self.step_callback,
+            function_calling_llm=self.function_calling_llm,
+            respect_context_window=self.respect_context_window,
+            request_within_rpm_limit=self._rpm_controller.check_or_wait
+            if self._rpm_controller
+            else None,
+            callbacks=[TokenCalcHandler(self._token_process)],
         )
 
-    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
-        """Interpolate inputs into the agent description and backstory."""
-        if self._original_role is None:
-            self._original_role = self.role
-        if self._original_goal is None:
-            self._original_goal = self.goal
-        if self._original_backstory is None:
-            self._original_backstory = self.backstory
+    def get_delegation_tools(self, agents: List[BaseAgent]):
+        agent_tools = AgentTools(agents=agents)
+        tools = agent_tools.tools()
+        return tools
 
-        if inputs:
-            self.role = self._original_role.format(**inputs)
-            self.goal = self._original_goal.format(**inputs)
-            self.backstory = self._original_backstory.format(**inputs)
+    def get_code_execution_tools(self):
+        try:
+            from crewai_tools import CodeInterpreterTool
 
-    def increment_formatting_errors(self) -> None:
-        """Count the formatting errors of the agent."""
-        self.formatting_errors += 1
+            return [CodeInterpreterTool()]
+        except ModuleNotFoundError:
+            self._logger.log(
+                "info", "Coding tools not available. Install crewai_tools. "
+            )
 
-    def format_log_to_str(
-        self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        observation_prefix: str = "Observation: ",
-        llm_prefix: str = "",
-    ) -> str:
-        """Construct the scratchpad that lets the agent continue its thought process."""
-        thoughts = ""
-        for action, observation in intermediate_steps:
-            thoughts += action.log
-            thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}"
-        return thoughts
-    
-    def copy(self):
-        """Create a deep copy of the Agent."""
-        exclude = {
-            "id",
-            "_logger",
-            "_rpm_controller",
-            "_request_within_rpm_limit",
-            "_token_process",      
-            "agent_executor",  
-            "tools",
-            "tools_handler",
-            "cache_handler",
-        }
+    def get_output_converter(self, llm, text, model, instructions):
+        return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
-        copied_data = self.model_dump(exclude=exclude)
-        copied_data = {k: v for k, v in copied_data.items() if v is not None}
-
-        copied_agent = Agent(**copied_data)
-        copied_agent.tools = deepcopy(self.tools)
-
-        return copied_agent
-
-    # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
-    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:
+    def _parse_tools(self, tools: List[Any]) -> List[Any]:  # type: ignore
         """Parse tools to be used for the task."""
-        # tentatively try to import from crewai_tools import BaseTool as CrewAITool
         tools_list = []
         try:
+            # tentatively try to import from crewai_tools import BaseTool as CrewAITool
             from crewai_tools import BaseTool as CrewAITool
 
             for tool in tools:
@@ -408,10 +329,84 @@ class Agent(BaseModel):
                 else:
                     tools_list.append(tool)
         except ModuleNotFoundError:
+            tools_list = []
             for tool in tools:
                 tools_list.append(tool)
+
         return tools_list
 
+    def _training_handler(self, task_prompt: str) -> str:
+        """Handle training data for the agent task prompt to improve output on Training."""
+        if data := CrewTrainingHandler(TRAINING_DATA_FILE).load():
+            agent_id = str(self.id)
+
+            if data.get(agent_id):
+                human_feedbacks = [
+                    i["human_feedback"] for i in data.get(agent_id, {}).values()
+                ]
+                task_prompt += (
+                    "\n\nYou MUST follow these instructions: \n "
+                    + "\n - ".join(human_feedbacks)
+                )
+
+        return task_prompt
+
+    def _use_trained_data(self, task_prompt: str) -> str:
+        """Use trained data for the agent task prompt to improve output."""
+        if data := CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).load():
+            if trained_data_output := data.get(self.role):
+                task_prompt += (
+                    "\n\nYou MUST follow these instructions: \n - "
+                    + "\n - ".join(trained_data_output["suggestions"])
+                )
+        return task_prompt
+
+    def _render_text_description(self, tools: List[Any]) -> str:
+        """Render the tool name and description in plain text.
+
+        Output will be in the format of:
+
+        .. code-block:: markdown
+
+            search: This tool is used for search
+            calculator: This tool is used for math
+        """
+        description = "\n".join(
+            [
+                f"Tool name: {tool.name}\nTool description:\n{tool.description}"
+                for tool in tools
+            ]
+        )
+
+        return description
+
+    def _render_text_description_and_args(self, tools: List[Any]) -> str:
+        """Render the tool name, description, and args in plain text.
+
+        Output will be in the format of:
+
+        .. code-block:: markdown
+
+            search: This tool is used for search, args: {"query": {"type": "string"}}
+            calculator: This tool is used for math, \
+    args: {"expression": {"type": "string"}}
+        """
+        tool_strings = []
+        for tool in tools:
+            args_schema = str(tool.args)
+            if hasattr(tool, "func") and tool.func:
+                sig = signature(tool.func)
+                description = (
+                    f"Tool Name: {tool.name}{sig}\nTool Description: {tool.description}"
+                )
+            else:
+                description = (
+                    f"Tool Name: {tool.name}\nTool Description: {tool.description}"
+                )
+            tool_strings.append(f"{description}\nTool Arguments: {args_schema}")
+
+        return "\n".join(tool_strings)
+
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])

src/crewai/agents/__init__.py

@@ -1,4 +1,5 @@
 from .cache.cache_handler import CacheHandler
-from .executor import CrewAgentExecutor
 from .parser import CrewAgentParser
 from .tools_handler import ToolsHandler
+
+__all__ = ["CacheHandler", "CrewAgentParser", "ToolsHandler"]

src/crewai/agents/agent_builder/base_agent.py

@@ -0,0 +1,275 @@
+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,
+    Field,
+    InstanceOf,
+    PrivateAttr,
+    field_validator,
+    model_validator,
+)
+from pydantic_core import PydanticCustomError
+
+from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
+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")
+
+
+class BaseAgent(ABC, BaseModel):
+    """Abstract Base Class for all third party agents compatible with CrewAI.
+
+    Attributes:
+        id (UUID4): Unique identifier for the agent.
+        role (str): Role of the agent.
+        goal (str): Objective of the agent.
+        backstory (str): Backstory of the agent.
+        cache (bool): Whether the agent should use a cache for tool usage.
+        config (Optional[Dict[str, Any]]): Configuration for the agent.
+        verbose (bool): Verbose mode for the Agent Execution.
+        max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution.
+        allow_delegation (bool): Allow delegation of tasks to agents.
+        tools (Optional[List[Any]]): Tools at the agent's disposal.
+        max_iter (Optional[int]): Maximum iterations for an agent to execute a task.
+        agent_executor (InstanceOf): An instance of the CrewAgentExecutor class.
+        llm (Any): Language model that will run the agent.
+        crew (Any): Crew to which the agent belongs.
+        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:
+        execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[Any]] = None) -> str:
+            Abstract method to execute a task.
+        create_agent_executor(tools=None) -> None:
+            Abstract method to create an agent executor.
+        _parse_tools(tools: List[Any]) -> List[Any]:
+            Abstract method to parse tools.
+        get_delegation_tools(agents: List["BaseAgent"]):
+            Abstract method to set the agents task tools for handling delegation and question asking to other agents in crew.
+        get_output_converter(llm, model, instructions):
+            Abstract method to get the converter class for the agent to create json/pydantic outputs.
+        interpolate_inputs(inputs: Dict[str, Any]) -> None:
+            Interpolate inputs into the agent description and backstory.
+        set_cache_handler(cache_handler: CacheHandler) -> None:
+            Set the cache handler for the agent.
+        increment_formatting_errors() -> None:
+            Increment formatting errors.
+        copy() -> "BaseAgent":
+            Create a copy of the agent.
+        set_rpm_controller(rpm_controller: RPMController) -> None:
+            Set the rpm controller for the agent.
+        set_private_attrs() -> "BaseAgent":
+            Set private attributes.
+    """
+
+    __hash__ = object.__hash__  # type: ignore
+    _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
+    _rpm_controller: Optional[RPMController] = PrivateAttr(default=None)
+    _request_within_rpm_limit: Any = PrivateAttr(default=None)
+    _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."
+    )
+    verbose: bool = Field(
+        default=False, description="Verbose mode for the Agent Execution"
+    )
+    max_rpm: Optional[int] = Field(
+        default=None,
+        description="Maximum number of requests per minute for the agent execution to be respected.",
+    )
+    allow_delegation: bool = Field(
+        default=False,
+        description="Enable agent to delegate and ask questions among each other.",
+    )
+    tools: Optional[List[Any]] = Field(
+        default_factory=list, description="Tools at agents' disposal"
+    )
+    max_iter: Optional[int] = Field(
+        default=25, description="Maximum iterations for an agent to execute a task"
+    )
+    agent_executor: InstanceOf = Field(
+        default=None, description="An instance of the CrewAgentExecutor class."
+    )
+    llm: Any = Field(
+        default=None, description="Language model that will run the agent."
+    )
+    crew: Any = Field(default=None, description="Crew to which the agent belongs.")
+    i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
+    cache_handler: InstanceOf[CacheHandler] = Field(
+        default=None, description="An instance of the CacheHandler class."
+    )
+    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."
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def process_model_config(cls, values):
+        return process_config(values, cls)
+
+    @model_validator(mode="after")
+    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")
+    @classmethod
+    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
+        if v:
+            raise PydanticCustomError(
+                "may_not_set_field", "This field is not to be set by the user.", {}
+            )
+
+    @model_validator(mode="after")
+    def set_private_attrs(self):
+        """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
+
+    @property
+    def key(self):
+        source = [
+            self._original_role or self.role,
+            self._original_goal or self.goal,
+            self._original_backstory or self.backstory,
+        ]
+        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
+
+    @abstractmethod
+    def execute_task(
+        self,
+        task: Any,
+        context: Optional[str] = None,
+        tools: Optional[List[Any]] = None,
+    ) -> str:
+        pass
+
+    @abstractmethod
+    def create_agent_executor(self, tools=None) -> None:
+        pass
+
+    @abstractmethod
+    def _parse_tools(self, tools: List[Any]) -> List[Any]:
+        pass
+
+    @abstractmethod
+    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[Any]:
+        """Set the task tools that init BaseAgenTools class."""
+        pass
+
+    @abstractmethod
+    def get_output_converter(
+        self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
+    ):
+        """Get the converter class for the agent to create json/pydantic outputs."""
+        pass
+
+    def copy(self: T) -> T:  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"
+        """Create a deep copy of the Agent."""
+        exclude = {
+            "id",
+            "_logger",
+            "_rpm_controller",
+            "_request_within_rpm_limit",
+            "_token_process",
+            "agent_executor",
+            "tools",
+            "tools_handler",
+            "cache_handler",
+            "llm",
+        }
+
+        # Copy llm and clear callbacks
+        existing_llm = shallow_copy(self.llm)
+        copied_data = self.model_dump(exclude=exclude)
+        copied_data = {k: v for k, v in copied_data.items() if v is not None}
+        copied_agent = type(self)(**copied_data, llm=existing_llm, tools=self.tools)
+
+        return copied_agent
+
+    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
+        """Interpolate inputs into the agent description and backstory."""
+        if self._original_role is None:
+            self._original_role = self.role
+        if self._original_goal is None:
+            self._original_goal = self.goal
+        if self._original_backstory is None:
+            self._original_backstory = self.backstory
+
+        if inputs:
+            self.role = self._original_role.format(**inputs)
+            self.goal = self._original_goal.format(**inputs)
+            self.backstory = self._original_backstory.format(**inputs)
+
+    def set_cache_handler(self, cache_handler: CacheHandler) -> None:
+        """Set the cache handler for the agent.
+
+        Args:
+            cache_handler: An instance of the CacheHandler class.
+        """
+        self.tools_handler = ToolsHandler()
+        if self.cache:
+            self.cache_handler = cache_handler
+            self.tools_handler.cache = cache_handler
+        self.create_agent_executor()
+
+    def increment_formatting_errors(self) -> None:
+        self.formatting_errors += 1
+
+    def set_rpm_controller(self, rpm_controller: RPMController) -> None:
+        """Set the rpm controller for the agent.
+
+        Args:
+            rpm_controller: An instance of the RPMController class.
+        """
+        if not self._rpm_controller:
+            self._rpm_controller = rpm_controller
+            self.create_agent_executor()

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -0,0 +1,113 @@
+import time
+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.utilities.converter import ConverterError
+from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
+from crewai.utilities import I18N
+from crewai.utilities.printer import Printer
+
+
+if TYPE_CHECKING:
+    from crewai.crew import Crew
+    from crewai.task import Task
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+
+
+class CrewAgentExecutorMixin:
+    crew: Optional["Crew"]
+    crew_agent: Optional["BaseAgent"]
+    task: Optional["Task"]
+    iterations: int
+    have_forced_answer: bool
+    max_iter: int
+    _i18n: I18N
+    _printer: Printer = Printer()
+
+    def _should_force_answer(self) -> bool:
+        """Determine if a forced answer is required based on iteration count."""
+        return (self.iterations >= self.max_iter) and not self.have_forced_answer
+
+    def _create_short_term_memory(self, output) -> None:
+        """Create and save a short-term memory item if conditions are met."""
+        if (
+            self.crew
+            and self.crew_agent
+            and self.task
+            and "Action: Delegate work to coworker" not in output.log
+        ):
+            try:
+                if (
+                    hasattr(self.crew, "_short_term_memory")
+                    and self.crew._short_term_memory
+                ):
+                    self.crew._short_term_memory.save(
+                        value=output.log,
+                        metadata={
+                            "observation": self.task.description,
+                        },
+                        agent=self.crew_agent.role,
+                    )
+            except Exception as e:
+                print(f"Failed to add to short term memory: {e}")
+                pass
+
+    def _create_long_term_memory(self, output) -> None:
+        """Create and save long-term and entity memory items based on evaluation."""
+        if (
+            self.crew
+            and self.crew.memory
+            and self.crew._long_term_memory
+            and self.crew._entity_memory
+            and self.task
+            and self.crew_agent
+        ):
+            try:
+                ltm_agent = TaskEvaluator(self.crew_agent)
+                evaluation = ltm_agent.evaluate(self.task, output.log)
+
+                if isinstance(evaluation, ConverterError):
+                    return
+
+                long_term_memory = LongTermMemoryItem(
+                    task=self.task.description,
+                    agent=self.crew_agent.role,
+                    quality=evaluation.quality,
+                    datetime=str(time.time()),
+                    expected_output=self.task.expected_output,
+                    metadata={
+                        "suggestions": evaluation.suggestions,
+                        "quality": evaluation.quality,
+                    },
+                )
+                self.crew._long_term_memory.save(long_term_memory)
+
+                for entity in evaluation.entities:
+                    entity_memory = EntityMemoryItem(
+                        name=entity.name,
+                        type=entity.type,
+                        description=entity.description,
+                        relationships="\n".join(
+                            [f"- {r}" for r in entity.relationships]
+                        ),
+                    )
+                    self.crew._entity_memory.save(entity_memory)
+            except AttributeError as e:
+                print(f"Missing attributes for long term memory: {e}")
+                pass
+            except Exception as e:
+                print(f"Failed to add to long term memory: {e}")
+                pass
+
+    def _ask_human_input(self, final_answer: dict) -> str:
+        """Prompt human input for final decision making."""
+        self._printer.print(
+            content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
+        )
+
+        self._printer.print(
+            content="\n\n=====\n## Please provide feedback on the Final Result and the Agent's actions:",
+            color="bold_yellow",
+        )
+        return input()

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

@@ -0,0 +1,86 @@
+from abc import ABC, abstractmethod
+from typing import List, Optional, Union
+
+from pydantic import BaseModel, Field
+
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.task import Task
+from crewai.utilities import I18N
+
+
+class BaseAgentTools(BaseModel, ABC):
+    """Default tools around agent delegation"""
+
+    agents: List[BaseAgent] = Field(description="List of agents in this crew.")
+    i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
+
+    @abstractmethod
+    def tools(self):
+        pass
+
+    def _get_coworker(self, coworker: Optional[str], **kwargs) -> Optional[str]:
+        coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker")
+        if coworker:
+            is_list = coworker.startswith("[") and coworker.endswith("]")
+            if is_list:
+                coworker = coworker[1:-1].split(",")[0]
+
+        return coworker
+
+    def delegate_work(
+        self, task: str, context: str, coworker: Optional[str] = None, **kwargs
+    ):
+        """Useful to delegate a specific task to a coworker passing all necessary context and names."""
+        coworker = self._get_coworker(coworker, **kwargs)
+        return self._execute(coworker, task, context)
+
+    def ask_question(
+        self, question: str, context: str, coworker: Optional[str] = None, **kwargs
+    ):
+        """Useful to ask a question, opinion or take from a coworker passing all necessary context and names."""
+        coworker = self._get_coworker(coworker, **kwargs)
+        return self._execute(coworker, question, context)
+
+    def _execute(
+        self, agent_name: Union[str, None], task: str, context: Union[str, None]
+    ):
+        """Execute the command."""
+        try:
+            if agent_name is None:
+                agent_name = ""
+
+            # It is important to remove the quotes from the agent name.
+            # The reason we have to do this is because less-powerful LLM's
+            # have difficulty producing valid JSON.
+            # As a result, we end up with invalid JSON that is truncated like this:
+            # {"task": "....", "coworker": "....
+            # when it should look like this:
+            # {"task": "....", "coworker": "...."}
+            agent_name = agent_name.casefold().replace('"', "").replace("\n", "")
+
+            agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
+                available_agent
+                for available_agent in self.agents
+                if available_agent.role.casefold().replace("\n", "") == agent_name
+            ]
+        except Exception as _:
+            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
+                coworkers="\n".join(
+                    [f"- {agent.role.casefold()}" for agent in self.agents]
+                )
+            )
+
+        if not agent:
+            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
+                coworkers="\n".join(
+                    [f"- {agent.role.casefold()}" for agent in self.agents]
+                )
+            )
+
+        agent = agent[0]
+        task_with_assigned_agent = Task(  # type: ignore # Incompatible types in assignment (expression has type "Task", variable has type "str")
+            description=task,
+            agent=agent,
+            expected_output="Your best answer to your coworker asking you this, accounting for the context shared.",
+        )
+        return agent.execute_task(task_with_assigned_agent, context)

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

@@ -0,0 +1,41 @@
+from abc import ABC, abstractmethod
+from typing import Any, Optional
+
+from pydantic import BaseModel, Field
+
+
+class OutputConverter(BaseModel, ABC):
+    """
+    Abstract base class for converting task results into structured formats.
+
+    This class provides a framework for converting unstructured text into
+    either Pydantic models or JSON, tailored for specific agent requirements.
+    It uses a language model to interpret and structure the input text based
+    on given instructions.
+
+    Attributes:
+        text (str): The input text to be converted.
+        llm (Any): The language model used for conversion.
+        model (Any): The target model for structuring the output.
+        instructions (str): Specific instructions for the conversion process.
+        max_attempts (int): Maximum number of conversion attempts (default: 3).
+    """
+
+    text: str = Field(description="Text to be converted.")
+    llm: Any = Field(description="The language model to be used to convert the text.")
+    model: Any = Field(description="The model to be used to convert the text.")
+    instructions: str = Field(description="Conversion instructions to the LLM.")
+    max_attempts: Optional[int] = Field(
+        description="Max number of attempts to try to get the output formatted.",
+        default=3,
+    )
+
+    @abstractmethod
+    def to_pydantic(self, current_attempt=1):
+        """Convert text to pydantic."""
+        pass
+
+    @abstractmethod
+    def to_json(self, current_attempt=1):
+        """Convert text to json."""
+        pass

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

@@ -0,0 +1,27 @@
+from crewai.types.usage_metrics import UsageMetrics
+
+
+class TokenProcess:
+    total_tokens: int = 0
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    successful_requests: int = 0
+
+    def sum_prompt_tokens(self, tokens: int):
+        self.prompt_tokens = self.prompt_tokens + tokens
+        self.total_tokens = self.total_tokens + tokens
+
+    def sum_completion_tokens(self, tokens: int):
+        self.completion_tokens = self.completion_tokens + tokens
+        self.total_tokens = self.total_tokens + tokens
+
+    def sum_successful_requests(self, requests: int):
+        self.successful_requests = self.successful_requests + requests
+
+    def get_summary(self) -> UsageMetrics:
+        return UsageMetrics(
+            total_tokens=self.total_tokens,
+            prompt_tokens=self.prompt_tokens,
+            completion_tokens=self.completion_tokens,
+            successful_requests=self.successful_requests,
+        )

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/crew_agent_executor.py

@@ -0,0 +1,354 @@
+import json
+import re
+from typing import Any, Dict, List, Union
+
+from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
+from crewai.agents.parser import CrewAgentParser
+from crewai.agents.tools_handler import ToolsHandler
+from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
+from crewai.utilities import I18N, Printer
+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
+from crewai.agents.parser import (
+    AgentAction,
+    AgentFinish,
+    OutputParserException,
+    FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE,
+)
+
+
+class CrewAgentExecutor(CrewAgentExecutorMixin):
+    _logger: Logger = Logger()
+
+    def __init__(
+        self,
+        llm: Any,
+        task: Any,
+        crew: Any,
+        agent: Any,
+        prompt: dict[str, str],
+        max_iter: int,
+        tools: List[Any],
+        tools_names: str,
+        stop_words: List[str],
+        tools_description: str,
+        tools_handler: ToolsHandler,
+        step_callback: Any = None,
+        original_tools: List[Any] = [],
+        function_calling_llm: Any = None,
+        respect_context_window: bool = False,
+        request_within_rpm_limit: Any = None,
+        callbacks: List[Any] = [],
+    ):
+        self._i18n: I18N = I18N()
+        self.llm = llm
+        self.task = task
+        self.agent = agent
+        self.crew = crew
+        self.prompt = prompt
+        self.tools = tools
+        self.tools_names = tools_names
+        self.stop = stop_words
+        self.max_iter = max_iter
+        self.callbacks = callbacks
+        self._printer: Printer = Printer()
+        self.tools_handler = tools_handler
+        self.original_tools = original_tools
+        self.step_callback = step_callback
+        self.use_stop_words = self.llm.supports_stop_words()
+        self.tools_description = tools_description
+        self.function_calling_llm = function_calling_llm
+        self.respect_context_window = respect_context_window
+        self.request_within_rpm_limit = request_within_rpm_limit
+        self.ask_for_human_input = False
+        self.messages: List[Dict[str, str]] = []
+        self.iterations = 0
+        self.log_error_after = 3
+        self.have_forced_answer = False
+        self.name_to_tool_map = {tool.name: tool for tool in self.tools}
+        self.llm.stop = self.stop
+
+    def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
+        if "system" in self.prompt:
+            system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs)
+            user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs)
+
+            self.messages.append(self._format_msg(system_prompt, role="system"))
+            self.messages.append(self._format_msg(user_prompt))
+        else:
+            user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
+            self.messages.append(self._format_msg(user_prompt))
+
+        self._show_start_logs()
+
+        self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
+        formatted_answer = self._invoke_loop()
+
+        if self.ask_for_human_input:
+            human_feedback = self._ask_human_input(formatted_answer.output)
+            if self.crew and self.crew._train:
+                self._handle_crew_training_output(formatted_answer, human_feedback)
+
+            # Making sure we only ask for it once, so disabling for the next thought loop
+            self.ask_for_human_input = False
+            self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
+            formatted_answer = self._invoke_loop()
+
+            if self.crew and self.crew._train:
+                self._handle_crew_training_output(formatted_answer)
+
+        return {"output": formatted_answer.output}
+
+    def _invoke_loop(self, formatted_answer=None):
+        try:
+            while not isinstance(formatted_answer, AgentFinish):
+                if not self.request_within_rpm_limit or self.request_within_rpm_limit():
+                    answer = self.llm.call(
+                        self.messages,
+                        callbacks=self.callbacks,
+                    )
+
+                    if not self.use_stop_words:
+                        try:
+                            self._format_answer(answer)
+                        except OutputParserException as e:
+                            if (
+                                FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE
+                                in e.error
+                            ):
+                                answer = answer.split("Observation:")[0].strip()
+
+                    self.iterations += 1
+                    formatted_answer = self._format_answer(answer)
+
+                    if isinstance(formatted_answer, AgentAction):
+                        action_result = self._use_tool(formatted_answer)
+                        formatted_answer.text += f"\nObservation: {action_result}"
+                        formatted_answer.result = action_result
+                        self._show_logs(formatted_answer)
+
+                        if self.step_callback:
+                            self.step_callback(formatted_answer)
+
+                        if self._should_force_answer():
+                            if self.have_forced_answer:
+                                return AgentFinish(
+                                    output=self._i18n.errors(
+                                        "force_final_answer_error"
+                                    ).format(formatted_answer.text),
+                                    text=formatted_answer.text,
+                                )
+                            else:
+                                formatted_answer.text += (
+                                    f'\n{self._i18n.errors("force_final_answer")}'
+                                )
+                                self.have_forced_answer = True
+                        self.messages.append(
+                            self._format_msg(formatted_answer.text, role="user")
+                        )
+
+        except OutputParserException as e:
+            self.messages.append({"role": "user", "content": e.error})
+            if self.iterations > self.log_error_after:
+                self._printer.print(
+                    content=f"Error parsing LLM output, agent will retry: {e.error}",
+                    color="red",
+                )
+            return self._invoke_loop(formatted_answer)
+
+        except Exception as e:
+            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
+                str(e)
+            ):
+                self._handle_context_length()
+                return self._invoke_loop(formatted_answer)
+            else:
+                raise e
+
+        self._show_logs(formatted_answer)
+        return formatted_answer
+
+    def _show_start_logs(self):
+        if self.agent.verbose or (
+            hasattr(self, "crew") and getattr(self.crew, "verbose", False)
+        ):
+            agent_role = self.agent.role.split("\n")[0]
+            self._printer.print(
+                content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
+            )
+            self._printer.print(
+                content=f"\033[95m## Task:\033[00m \033[92m{self.task.description}\033[00m"
+            )
+
+    def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
+        if self.agent.verbose or (
+            hasattr(self, "crew") and getattr(self.crew, "verbose", False)
+        ):
+            agent_role = self.agent.role.split("\n")[0]
+            if isinstance(formatted_answer, AgentAction):
+                thought = re.sub(r"\n+", "\n", formatted_answer.thought)
+                formatted_json = json.dumps(
+                    formatted_answer.tool_input,
+                    indent=2,
+                    ensure_ascii=False,
+                )
+                self._printer.print(
+                    content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
+                )
+                if thought and thought != "":
+                    self._printer.print(
+                        content=f"\033[95m## Thought:\033[00m \033[92m{thought}\033[00m"
+                    )
+                self._printer.print(
+                    content=f"\033[95m## Using tool:\033[00m \033[92m{formatted_answer.tool}\033[00m"
+                )
+                self._printer.print(
+                    content=f"\033[95m## Tool Input:\033[00m \033[92m\n{formatted_json}\033[00m"
+                )
+                self._printer.print(
+                    content=f"\033[95m## Tool Output:\033[00m \033[92m\n{formatted_answer.result}\033[00m"
+                )
+            elif isinstance(formatted_answer, AgentFinish):
+                self._printer.print(
+                    content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
+                )
+                self._printer.print(
+                    content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
+                )
+
+    def _use_tool(self, agent_action: AgentAction) -> Any:
+        tool_usage = ToolUsage(
+            tools_handler=self.tools_handler,
+            tools=self.tools,
+            original_tools=self.original_tools,
+            tools_description=self.tools_description,
+            tools_names=self.tools_names,
+            function_calling_llm=self.function_calling_llm,
+            task=self.task,  # type: ignore[arg-type]
+            agent=self.agent,
+            action=agent_action,
+        )
+        tool_calling = tool_usage.parse(agent_action.text)
+
+        if isinstance(tool_calling, ToolUsageErrorException):
+            tool_result = tool_calling.message
+        else:
+            if tool_calling.tool_name.casefold().strip() in [
+                name.casefold().strip() for name in self.name_to_tool_map
+            ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+                name.casefold().strip() for name in self.name_to_tool_map
+            ]:
+                tool_result = tool_usage.use(tool_calling, agent_action.text)
+            else:
+                tool_result = self._i18n.errors("wrong_tool_name").format(
+                    tool=tool_calling.tool_name,
+                    tools=", ".join([tool.name.casefold() for tool in self.tools]),
+                )
+        return tool_result
+
+    def _summarize_messages(self) -> None:
+        messages_groups = []
+        for message in self.messages:
+            content = message["content"]
+            cut_size = self.llm.get_context_window_size()
+            for i in range(0, len(content), cut_size):
+                messages_groups.append(content[i : i + cut_size])
+
+        summarized_contents = []
+        for group in messages_groups:
+            summary = self.llm.call(
+                [
+                    self._format_msg(
+                        self._i18n.slice("summarizer_system_message"), role="system"
+                    ),
+                    self._format_msg(
+                        self._i18n.slice("sumamrize_instruction").format(group=group),
+                    ),
+                ],
+                callbacks=self.callbacks,
+            )
+            summarized_contents.append(summary)
+
+        merged_summary = " ".join(str(content) for content in summarized_contents)
+
+        self.messages = [
+            self._format_msg(
+                self._i18n.slice("summary").format(merged_summary=merged_summary)
+            )
+        ]
+
+    def _handle_context_length(self) -> None:
+        if self.respect_context_window:
+            self._logger.log(
+                "debug",
+                "Context length exceeded. Summarizing content to fit the model context window.",
+                color="yellow",
+            )
+            self._summarize_messages()
+        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."
+            )
+
+    def _handle_crew_training_output(
+        self, result: AgentFinish, human_feedback: str | None = None
+    ) -> None:
+        """Function to handle the process of the training data."""
+        agent_id = str(self.agent.id)
+        if (
+            CrewTrainingHandler(TRAINING_DATA_FILE).load()
+            and not self.ask_for_human_input
+        ):
+            training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
+            if training_data.get(agent_id):
+                training_data[agent_id][self.crew._train_iteration][
+                    "improved_output"
+                ] = result.output
+                CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
+
+        if self.ask_for_human_input and human_feedback is not None:
+            training_data = {
+                "initial_output": result.output,
+                "human_feedback": human_feedback,
+                "agent": agent_id,
+                "agent_role": self.agent.role,
+            }
+            if self.crew is not None and hasattr(self.crew, "_train_iteration"):
+                train_iteration = self.crew._train_iteration
+                if isinstance(train_iteration, int):
+                    CrewTrainingHandler(TRAINING_DATA_FILE).append(
+                        train_iteration, agent_id, training_data
+                    )
+                else:
+                    self._logger.log(
+                        "error",
+                        "Invalid train iteration type. Expected int.",
+                        color="red",
+                    )
+            else:
+                self._logger.log(
+                    "error",
+                    "Crew is None or does not have _train_iteration attribute.",
+                    color="red",
+                )
+
+    def _format_prompt(self, prompt: str, inputs: Dict[str, str]) -> str:
+        prompt = prompt.replace("{input}", inputs["input"])
+        prompt = prompt.replace("{tool_names}", inputs["tool_names"])
+        prompt = prompt.replace("{tools}", inputs["tools"])
+        return prompt
+
+    def _format_answer(self, answer: str) -> Union[AgentAction, AgentFinish]:
+        return CrewAgentParser(agent=self.agent).parse(answer)
+
+    def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
+        return {"role": role, "content": prompt}

src/crewai/agents/executor.py

@@ -1,307 +0,0 @@
-import threading
-import time
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
-
-from langchain.agents import AgentExecutor
-from langchain.agents.agent import ExceptionTool
-from langchain.callbacks.manager import CallbackManagerForChainRun
-from langchain_core.agents import AgentAction, AgentFinish, AgentStep
-from langchain_core.exceptions import OutputParserException
-from langchain_core.pydantic_v1 import root_validator
-from langchain_core.tools import BaseTool
-from langchain_core.utils.input import get_color_mapping
-from pydantic import InstanceOf
-
-from crewai.agents.tools_handler import ToolsHandler
-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.tools.tool_usage import ToolUsage, ToolUsageErrorException
-from crewai.utilities import I18N
-from crewai.utilities.converter import ConverterError
-from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-
-
-class CrewAgentExecutor(AgentExecutor):
-    _i18n: I18N = I18N()
-    should_ask_for_human_input: bool = False
-    llm: Any = None
-    iterations: int = 0
-    task: Any = None
-    tools_description: str = ""
-    tools_names: str = ""
-    original_tools: List[Any] = []
-    crew_agent: Any = None
-    crew: Any = None
-    function_calling_llm: Any = None
-    request_within_rpm_limit: Any = None
-    tools_handler: Optional[InstanceOf[ToolsHandler]] = None
-    max_iterations: Optional[int] = 15
-    have_forced_answer: bool = False
-    force_answer_max_iterations: Optional[int] = None
-    step_callback: Optional[Any] = None
-    system_template: Optional[str] = None
-    prompt_template: Optional[str] = None
-    response_template: Optional[str] = None
-
-    @root_validator()
-    def set_force_answer_max_iterations(cls, values: Dict) -> Dict:
-        values["force_answer_max_iterations"] = values["max_iterations"] - 2
-        return values
-
-    def _should_force_answer(self) -> bool:
-        return (
-            self.iterations == self.force_answer_max_iterations
-        ) and not self.have_forced_answer
-
-    def _create_short_term_memory(self, output) -> None:
-        if (
-            self.crew
-            and self.crew.memory
-            and "Action: Delegate work to coworker" not in output.log
-        ):
-            memory = ShortTermMemoryItem(
-                data=output.log,
-                agent=self.crew_agent.role,
-                metadata={
-                    "observation": self.task.description,
-                },
-            )
-            self.crew._short_term_memory.save(memory)
-
-    def _create_long_term_memory(self, output) -> None:
-        if self.crew and self.crew.memory:
-            ltm_agent = TaskEvaluator(self.crew_agent)
-            evaluation = ltm_agent.evaluate(self.task, output.log)
-
-            if isinstance(evaluation, ConverterError):
-                return
-
-            long_term_memory = LongTermMemoryItem(
-                task=self.task.description,
-                agent=self.crew_agent.role,
-                quality=evaluation.quality,
-                datetime=str(time.time()),
-                expected_output=self.task.expected_output,
-                metadata={
-                    "suggestions": evaluation.suggestions,
-                    "quality": evaluation.quality,
-                },
-            )
-            self.crew._long_term_memory.save(long_term_memory)
-
-            for entity in evaluation.entities:
-                entity_memory = EntityMemoryItem(
-                    name=entity.name,
-                    type=entity.type,
-                    description=entity.description,
-                    relationships="\n".join([f"- {r}" for r in entity.relationships]),
-                )
-                self.crew._entity_memory.save(entity_memory)
-
-    def _call(
-        self,
-        inputs: Dict[str, str],
-        run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Dict[str, Any]:
-        """Run text through and get agent response."""
-        # Construct a mapping of tool name to tool for easy lookup
-        name_to_tool_map = {tool.name: tool for tool in self.tools}
-        # We construct a mapping from each tool to a color, used for logging.
-        color_mapping = get_color_mapping(
-            [tool.name.casefold() for tool in self.tools],
-            excluded_colors=["green", "red"],
-        )
-        intermediate_steps: List[Tuple[AgentAction, str]] = []
-        # Allowing human input given task setting
-        if self.task.human_input:
-            self.should_ask_for_human_input = True
-
-        # Let's start tracking the number of iterations and time elapsed
-        self.iterations = 0
-        time_elapsed = 0.0
-        start_time = time.time()
-
-        # We now enter the agent loop (until it returns something).
-        while self._should_continue(self.iterations, time_elapsed):
-            if not self.request_within_rpm_limit or self.request_within_rpm_limit():
-                next_step_output = self._take_next_step(
-                    name_to_tool_map,
-                    color_mapping,
-                    inputs,
-                    intermediate_steps,
-                    run_manager=run_manager,
-                )
-
-                if self.step_callback:
-                    self.step_callback(next_step_output)
-
-                if isinstance(next_step_output, AgentFinish):
-                    # Creating long term memory
-                    create_long_term_memory = threading.Thread(
-                        target=self._create_long_term_memory, args=(next_step_output,)
-                    )
-                    create_long_term_memory.start()
-
-                    return self._return(
-                        next_step_output, intermediate_steps, run_manager=run_manager
-                    )
-
-                intermediate_steps.extend(next_step_output)
-
-                if len(next_step_output) == 1:
-                    next_step_action = next_step_output[0]
-                    # See if tool should return directly
-                    tool_return = self._get_tool_return(next_step_action)
-                    if tool_return is not None:
-                        return self._return(
-                            tool_return, intermediate_steps, run_manager=run_manager
-                        )
-
-                self.iterations += 1
-                time_elapsed = time.time() - start_time
-        output = self.agent.return_stopped_response(
-            self.early_stopping_method, intermediate_steps, **inputs
-        )
-
-        return self._return(output, intermediate_steps, run_manager=run_manager)
-
-    def _iter_next_step(
-        self,
-        name_to_tool_map: Dict[str, BaseTool],
-        color_mapping: Dict[str, str],
-        inputs: Dict[str, str],
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Iterator[Union[AgentFinish, AgentAction, AgentStep]]:
-        """Take a single step in the thought-action-observation loop.
-
-        Override this to take control of how the agent makes and acts on choices.
-        """
-        try:
-            if self._should_force_answer():
-                error = self._i18n.errors("force_final_answer")
-                output = AgentAction("_Exception", error, error)
-                self.have_forced_answer = True
-                yield AgentStep(action=output, observation=error)
-                return
-
-            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")
-                intermediate_steps,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **inputs,
-            )
-
-        except OutputParserException as e:
-            if isinstance(self.handle_parsing_errors, bool):
-                raise_error = not self.handle_parsing_errors
-            else:
-                raise_error = False
-            if raise_error:
-                raise ValueError(
-                    "An output parsing error occurred. "
-                    "In order to pass this error back to the agent and have it try "
-                    "again, pass `handle_parsing_errors=True` to the AgentExecutor. "
-                    f"This is the error: {str(e)}"
-                )
-            str(e)
-            if isinstance(self.handle_parsing_errors, bool):
-                if e.send_to_llm:
-                    observation = f"\n{str(e.observation)}"
-                    str(e.llm_output)
-                else:
-                    observation = ""
-            elif isinstance(self.handle_parsing_errors, str):
-                observation = f"\n{self.handle_parsing_errors}"
-            elif callable(self.handle_parsing_errors):
-                observation = f"\n{self.handle_parsing_errors(e)}"
-            else:
-                raise ValueError("Got unexpected type of `handle_parsing_errors`")
-            output = AgentAction("_Exception", observation, "")
-
-            if run_manager:
-                run_manager.on_agent_action(output, color="green")
-
-            tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-            observation = ExceptionTool().run(
-                output.tool_input,
-                verbose=False,
-                color=None,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **tool_run_kwargs,
-            )
-
-            if self._should_force_answer():
-                error = self._i18n.errors("force_final_answer")
-                output = AgentAction("_Exception", error, error)
-                yield AgentStep(action=output, observation=error)
-                return
-
-            yield AgentStep(action=output, observation=observation)
-            return
-
-        # If the tool chosen is the finishing tool, then we end and return.
-        if isinstance(output, AgentFinish):
-            if self.should_ask_for_human_input:
-                # Making sure we only ask for it once, so disabling for the next thought loop
-                self.should_ask_for_human_input = False
-                human_feedback = self._ask_human_input(output.return_values["output"])
-                action = AgentAction(
-                    tool="Human Input", tool_input=human_feedback, log=output.log
-                )
-                yield AgentStep(
-                    action=action,
-                    observation=self._i18n.slice("human_feedback").format(
-                        human_feedback=human_feedback
-                    ),
-                )
-                return
-
-            else:
-                yield output
-                return
-
-        self._create_short_term_memory(output)
-
-        actions: List[AgentAction]
-        actions = [output] if isinstance(output, AgentAction) else output
-        yield from actions
-
-        for agent_action in actions:
-            if run_manager:
-                run_manager.on_agent_action(agent_action, color="green")
-
-            tool_usage = ToolUsage(
-                tools_handler=self.tools_handler,  # type: ignore # Argument "tools_handler" to "ToolUsage" has incompatible type "ToolsHandler | None"; expected "ToolsHandler"
-                tools=self.tools,  # type: ignore # Argument "tools" to "ToolUsage" has incompatible type "Sequence[BaseTool]"; expected "list[BaseTool]"
-                original_tools=self.original_tools,
-                tools_description=self.tools_description,
-                tools_names=self.tools_names,
-                function_calling_llm=self.function_calling_llm,
-                task=self.task,
-                action=agent_action,
-            )
-            tool_calling = tool_usage.parse(agent_action.log)
-
-            if isinstance(tool_calling, ToolUsageErrorException):
-                observation = tool_calling.message
-            else:
-                if tool_calling.tool_name.casefold().strip() in [
-                    name.casefold().strip() for name in name_to_tool_map
-                ]:
-                    observation = tool_usage.use(tool_calling, agent_action.log)
-                else:
-                    observation = self._i18n.errors("wrong_tool_name").format(
-                        tool=tool_calling.tool_name,
-                        tools=", ".join([tool.name.casefold() for tool in self.tools]),
-                    )
-            yield AgentStep(action=agent_action, observation=observation)
-
-    def _ask_human_input(self, final_answer: dict) -> str:
-        """Get human input."""
-        return input(
-            self._i18n.slice("getting_input").format(final_answer=final_answer)
-        )

src/crewai/agents/parser.py

@@ -1,9 +1,6 @@
 import re
 from typing import Any, Union
-
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
-from langchain_core.agents import AgentAction, AgentFinish
-from langchain_core.exceptions import OutputParserException
+from json_repair import repair_json
 
 from crewai.utilities import I18N
 
@@ -13,7 +10,39 @@ MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE = "I did it wrong. Invalid Forma
 FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = "I did it wrong. Tried to both perform Action and give a Final Answer at the same time, I must do one or the other"
 
 
-class CrewAgentParser(ReActSingleInputOutputParser):
+class AgentAction:
+    thought: str
+    tool: str
+    tool_input: str
+    text: str
+    result: str
+
+    def __init__(self, thought: str, tool: str, tool_input: str, text: str):
+        self.thought = thought
+        self.tool = tool
+        self.tool_input = tool_input
+        self.text = text
+
+
+class AgentFinish:
+    thought: str
+    output: str
+    text: str
+
+    def __init__(self, thought: str, output: str, text: str):
+        self.thought = thought
+        self.output = output
+        self.text = text
+
+
+class OutputParserException(Exception):
+    error: str
+
+    def __init__(self, error: str):
+        self.error = error
+
+
+class CrewAgentParser:
     """Parses ReAct-style LLM calls that have a single tool input.
 
     Expects output to be in one of two formats.
@@ -37,7 +66,11 @@ class CrewAgentParser(ReActSingleInputOutputParser):
     _i18n: I18N = I18N()
     agent: Any = None
 
+    def __init__(self, agent: Any):
+        self.agent = agent
+
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
+        thought = self._extract_thought(text)
         includes_answer = FINAL_ANSWER_ACTION in text
         regex = (
             r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
@@ -46,36 +79,33 @@ class CrewAgentParser(ReActSingleInputOutputParser):
         if action_match:
             if includes_answer:
                 raise OutputParserException(
-                    f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
+                    f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}"
                 )
-            action = action_match.group(1).strip()
-            action_input = action_match.group(2)
-            tool_input = action_input.strip(" ")
-            tool_input = tool_input.strip('"')
-            return AgentAction(action, tool_input, text)
+            action = action_match.group(1)
+            clean_action = self._clean_action(action)
+
+            action_input = action_match.group(2).strip()
+
+            tool_input = action_input.strip(" ").strip('"')
+            safe_tool_input = self._safe_repair_json(tool_input)
+
+            return AgentAction(thought, clean_action, safe_tool_input, text)
 
         elif includes_answer:
-            return AgentFinish(
-                {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
-            )
+            final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
+            return AgentFinish(thought, final_answer, text)
 
         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
             self.agent.increment_formatting_errors()
             raise OutputParserException(
-                f"Could not parse LLM output: `{text}`",
-                observation=f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
-                llm_output=text,
-                send_to_llm=True,
+                f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
             )
         elif not re.search(
             r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
         ):
             self.agent.increment_formatting_errors()
             raise OutputParserException(
-                f"Could not parse LLM output: `{text}`",
-                observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
-                llm_output=text,
-                send_to_llm=True,
+                MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
             )
         else:
             format = self._i18n.slice("format_without_tools")
@@ -83,7 +113,38 @@ class CrewAgentParser(ReActSingleInputOutputParser):
             self.agent.increment_formatting_errors()
             raise OutputParserException(
                 error,
-                observation=error,
-                llm_output=text,
-                send_to_llm=True,
             )
+
+    def _extract_thought(self, text: str) -> str:
+        regex = r"(.*?)(?:\n\nAction|\n\nFinal Answer)"
+        thought_match = re.search(regex, text, re.DOTALL)
+        if thought_match:
+            return thought_match.group(1).strip()
+        return ""
+
+    def _clean_action(self, text: str) -> str:
+        """Clean action string by removing non-essential formatting characters."""
+        return re.sub(r"^\s*\*+\s*|\s*\*+\s*$", "", text).strip()
+
+    def _safe_repair_json(self, tool_input: str) -> str:
+        UNABLE_TO_REPAIR_JSON_RESULTS = ['""', "{}"]
+
+        # Skip repair if the input starts and ends with square brackets
+        # Explanation: The JSON parser has issues handling inputs that are enclosed in square brackets ('[]').
+        # These are typically valid JSON arrays or strings that do not require repair. Attempting to repair such inputs
+        # might lead to unintended alterations, such as wrapping the entire input in additional layers or modifying
+        # the structure in a way that changes its meaning. By skipping the repair for inputs that start and end with
+        # square brackets, we preserve the integrity of these valid JSON structures and avoid unnecessary modifications.
+        if tool_input.startswith("[") and tool_input.endswith("]"):
+            return tool_input
+
+        # Before repair, handle common LLM issues:
+        # 1. Replace """ with " to avoid JSON parser errors
+
+        tool_input = tool_input.replace('"""', '"')
+
+        result = repair_json(tool_input)
+        if result in UNABLE_TO_REPAIR_JSON_RESULTS:
+            return tool_input
+
+        return str(result)

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)  # type: ignore
+        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

@@ -1,7 +1,22 @@
+from typing import Optional
+
 import click
 import pkg_resources
 
-from .create_crew import create_crew
+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 .authentication.main import AuthenticationCommand
+from .deploy.main import DeployCommand
+from .tools.main import ToolCommand
+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
 
 
@@ -11,10 +26,19 @@ 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()
@@ -42,10 +66,209 @@ 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()
+@click.option(
+    "-t",
+    "--task_id",
+    type=str,
+    help="Replay the crew from this task ID, including all subsequent tasks.",
+)
+def replay(task_id: str) -> None:
+    """
+    Replay the crew execution from a specific task.
+
+    Args:
+        task_id (str): The ID of the task to replay from.
+    """
+    try:
+        click.echo(f"Replaying the crew from task {task_id}")
+        replay_task_command(task_id)
+    except Exception as e:
+        click.echo(f"An error occurred while replaying: {e}", err=True)
+
+
+@crewai.command()
+def log_tasks_outputs() -> None:
+    """
+    Retrieve your latest crew.kickoff() task outputs.
+    """
+    try:
+        storage = KickoffTaskOutputsSQLiteStorage()
+        tasks = storage.load()
+
+        if not tasks:
+            click.echo(
+                "No task outputs found. Only crew kickoff task outputs are logged."
+            )
+            return
+
+        for index, task in enumerate(tasks, 1):
+            click.echo(f"Task {index}: {task['task_id']}")
+            click.echo(f"Description: {task['expected_output']}")
+            click.echo("------")
+
+    except Exception as e:
+        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
+
+
+@crewai.group()
+def tool():
+    """Tool Repository related commands."""
+    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)
+
+
+@tool.command(name="install")
+@click.argument("handle")
+def tool_install(handle: str):
+    tool_cmd = ToolCommand()
+    tool_cmd.install(handle)
+
+
+@tool.command(name="publish")
+@click.option("--public", "is_public", flag_value=True, default=False)
+@click.option("--private", "is_public", flag_value=False)
+def tool_publish(is_public: bool):
+    tool_cmd = ToolCommand()
+    tool_cmd.publish(is_public)
 
 
 if __name__ == "__main__":

src/crewai/cli/command.py

@@ -0,0 +1,40 @@
+from typing import Dict, Any
+from rich.console import Console
+from crewai.cli.plus_api import PlusAPI
+from crewai.cli.utils import get_auth_token
+from crewai.telemetry.telemetry import Telemetry
+
+console = Console()
+
+
+class BaseCommand:
+    def __init__(self):
+        self._telemetry = Telemetry()
+        self._telemetry.set_tracer()
+
+
+class PlusAPIMixin:
+    def __init__(self, telemetry):
+        try:
+            telemetry.set_tracer()
+            self.plus_api_client = PlusAPI(api_key=get_auth_token())
+        except Exception:
+            self._deploy_signup_error_span = 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
+
+    def _handle_plus_api_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")

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/main.py

@@ -0,0 +1,286 @@
+from typing import Any, Dict, List, Optional
+
+from rich.console import Console
+
+from crewai.cli.command import BaseCommand, PlusAPIMixin
+from crewai.cli.utils import (
+    fetch_and_json_env_file,
+    get_git_remote_url,
+    get_project_name,
+)
+
+console = Console()
+
+
+class DeployCommand(BaseCommand, PlusAPIMixin):
+    """
+    A class to handle deployment-related operations for CrewAI projects.
+    """
+
+    def __init__(self):
+        """
+        Initialize the DeployCommand with project name and API client.
+        """
+
+        BaseCommand.__init__(self)
+        PlusAPIMixin.__init__(self, telemetry=self._telemetry)
+        self.project_name = get_project_name(require=True)
+
+    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.plus_api_client.deploy_by_uuid(uuid)
+        elif self.project_name:
+            response = self.plus_api_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_plus_api_error(json_response)
+
+    def create_crew(self, confirm: bool = False) -> 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.plus_api_client.create_crew(payload)
+        if response.status_code == 201:
+            self._display_creation_success(response.json())
+        else:
+            self._handle_plus_api_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.plus_api_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.plus_api_client.crew_status_by_uuid(uuid)
+        elif self.project_name:
+            response = self.plus_api_client.crew_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_plus_api_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.plus_api_client.crew_by_uuid(uuid, log_type)
+        elif self.project_name:
+            response = self.plus_api_client.crew_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_plus_api_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.plus_api_client.delete_crew_by_uuid(uuid)
+        elif self.project_name:
+            response = self.plus_api_client.delete_crew_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/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/plus_api.py

@@ -0,0 +1,92 @@
+from typing import Optional
+import requests
+from os import getenv
+from crewai.cli.utils import get_crewai_version
+from urllib.parse import urljoin
+
+
+class PlusAPI:
+    """
+    This class exposes methods for working with the CrewAI+ API.
+    """
+
+    TOOLS_RESOURCE = "/crewai_plus/api/v1/tools"
+    CREWS_RESOURCE = "/crewai_plus/api/v1/crews"
+
+    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()}",
+            "X-Crewai-Version": get_crewai_version(),
+        }
+        self.base_url = getenv("CREWAI_BASE_URL", "https://app.crewai.com")
+
+    def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
+        url = urljoin(self.base_url, endpoint)
+        return requests.request(method, url, headers=self.headers, **kwargs)
+
+    def get_tool(self, handle: str):
+        return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
+
+    def publish_tool(
+        self,
+        handle: str,
+        is_public: bool,
+        version: str,
+        description: Optional[str],
+        encoded_file: str,
+    ):
+        params = {
+            "handle": handle,
+            "public": is_public,
+            "version": version,
+            "file": encoded_file,
+            "description": description,
+        }
+        return self._make_request("POST", f"{self.TOOLS_RESOURCE}", json=params)
+
+    def deploy_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request(
+            "POST", f"{self.CREWS_RESOURCE}/by-name/{project_name}/deploy"
+        )
+
+    def deploy_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("POST", f"{self.CREWS_RESOURCE}/{uuid}/deploy")
+
+    def crew_status_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request(
+            "GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/status"
+        )
+
+    def crew_status_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("GET", f"{self.CREWS_RESOURCE}/{uuid}/status")
+
+    def crew_by_name(
+        self, project_name: str, log_type: str = "deployment"
+    ) -> requests.Response:
+        return self._make_request(
+            "GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/logs/{log_type}"
+        )
+
+    def crew_by_uuid(
+        self, uuid: str, log_type: str = "deployment"
+    ) -> requests.Response:
+        return self._make_request(
+            "GET", f"{self.CREWS_RESOURCE}/{uuid}/logs/{log_type}"
+        )
+
+    def delete_crew_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request(
+            "DELETE", f"{self.CREWS_RESOURCE}/by-name/{project_name}"
+        )
+
+    def delete_crew_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("DELETE", f"{self.CREWS_RESOURCE}/{uuid}")
+
+    def list_crews(self) -> requests.Response:
+        return self._make_request("GET", self.CREWS_RESOURCE)
+
+    def create_crew(self, payload) -> requests.Response:
+        return self._make_request("POST", self.CREWS_RESOURCE, json=payload)