Fix merge conflict

* Almost working!

* It fully works but not clean enought

* Working but not clean engouth

* Everything is workign

* WIP. Working on adding and & or to flows. In the middle of setting up template for flow as well

* template working

* Everything is working

* More changes and todos

* Add more support for @start

* Router working now

* minor tweak to

* minor tweak to conditions and event handling

* Update logs

* Too trigger happy with cleanup

* Added in Thiago fix

* Flow passing results again

* Working on docs.

* made more progress updates on docs

* Finished talking about controlling flows

* add flow output

* fixed flow output section

* add crews to flows section is looking good now

* more flow doc changes

* Update docs and add more examples

* drop visualizer

* save visualizer

* pyvis is beginning to work

* pyvis working

* it is working

* regular methods and triggers working. Need to work on router next.

* properly identifying router and router children nodes. Need to fix color

* children router working. Need to support loops

* curving cycles but need to add curve conditionals

* everythin is showing up properly need to fix curves

* all working. needs to be cleaned up

* adjust padding

* drop lib

* clean up prior to PR

* incorporate joao feedback

* final tweaks for joao

.github/ISSUE_TEMPLATE/bug_report.md

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

.github/ISSUE_TEMPLATE/config.yml

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

.github/ISSUE_TEMPLATE/custom.md

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

.github/workflows/mkdocs.yml

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

.github/workflows/security-checker.yml

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

.github/workflows/stale.yml

@@ -24,3 +24,4 @@ jobs:
         stale-pr-message: 'This PR is stale because it has been open for 45 days with no activity.'
         days-before-pr-stale: 45
         days-before-pr-close: -1
+        operations-per-run: 1200

.github/workflows/tests.yml

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

.gitignore

@@ -2,6 +2,7 @@
 .pytest_cache
 __pycache__
 dist/
+lib/
 .env
 assets/*
 .idea
@@ -15,4 +16,4 @@ rc-tests/*
 *.pkl
 temp/*
 .vscode/*
-crew_tasks_output.json
+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',
@@ -153,12 +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
 
@@ -166,19 +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")
 
@@ -190,13 +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:
@@ -284,3 +267,39 @@ Users can opt-in to Further Telemetry, sharing the complete telemetry data by se
 ## 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

@@ -11,31 +11,33 @@ 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                  | 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.             |
+| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **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 `True`.                                                                                                       |
+| **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`.                                                                                                                                                                  |
+| **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
 
@@ -63,7 +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
@@ -74,8 +76,10 @@ agent = Agent(
   tools_handler=my_tools_handler,  # Optional
   cache_handler=my_cache_handler,  # Optional
   callbacks=[callback1, callback2],  # Optional
-  allow_code_execution=True,  # Optiona
+  allow_code_execution=True,  # Optional
   max_retry_limit=2,  # Optional
+  use_system_prompt=True,  # Optional
+  respect_context_window=True,  # Optional
 )
 ```
 
@@ -105,7 +109,7 @@ agent = Agent(
 
     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.
+    CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
 
 
 ```py

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 +27,7 @@ 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.
 

docs/core-concepts/Crews.md

@@ -13,18 +13,18 @@ A crew in crewAI represents a collaborative group of agents working together to
 | :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | **Tasks**                             | `tasks`                | A list of tasks assigned to the crew.                                                                                                                                                                                                                     |
 | **Agents**                            | `agents`               | A list of agents that are part of the crew.                                                                                                                                                                                                               |
-| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows.                                                                                                                                                                                       |
-| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution.                                                                                                                                                                                                         |
+| **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.                                                                                                                                                                                         |
+| **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).                                                                                                                                                                           |
-| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution.                                                                                                                                                                             |
-| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                                                                                                                                                                     |
-| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output.                                                                                                                                                           |
+| **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.                                                                                                      |
@@ -38,65 +38,6 @@ A crew in crewAI represents a collaborative group of agents working together to
 !!! 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.
 
-## 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.
-
-### Example: Assembling a Crew
-
-```python
-from crewai import Crew, Agent, Task, Process
-from langchain_community.tools import DuckDuckGoSearchRun
-from crewai_tools import tool
-
-@tool('DuckDuckGoSearch')
-def search(search_query: str):
-    """Search the web for information on a given topic"""
-    return DuckDuckGoSearchRun().run(search_query)
-
-# Define agents with specific roles and tools
-researcher = Agent(
-    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=[search]
-)
-
-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.""",
-    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'
-)
-
-# 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,
-)
-```
 
 ## Crew Output
 

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 remember 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
@@ -27,12 +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 EmbedChain package.
-The **Long-Term Memory** uses SQLLite3 to store task results.  Currently, there is no way to override these storage implementations.
-The data storage files are saved into a platform specific location found using the appdirs package
-and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
+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
 
@@ -49,6 +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)
@@ -56,17 +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'
+        }
+    }
 )
 ```
 
@@ -75,19 +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"
+        }
+    }
 )
 ```
 
@@ -96,18 +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": "your_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"
+        }
+    }
 )
 ```
 
@@ -116,14 +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"
+    }
 )
 ```
 
@@ -132,17 +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'
+        }
+    }
 )
 ```
 
@@ -151,18 +191,18 @@ my_crew = Crew(
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "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
+        }
+    }
 )
 ```
 

docs/core-concepts/Pipeline.md

@@ -12,7 +12,7 @@ A pipeline in crewAI represents a structured workflow that allows for the sequen
 Understanding the following terms is crucial for working effectively with pipelines:
 
 - **Stage**: A distinct part of the pipeline, which can be either sequential (a single crew) or parallel (multiple crews executing concurrently).
-- **Run**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
+- **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.
 
@@ -28,13 +28,13 @@ This represents a pipeline with three stages:
 2. A parallel stage with two branches (crew2 and crew3 executing concurrently)
 3. Another sequential stage (crew4)
 
-Each input creates its own run, flowing through all stages of the pipeline. Multiple runs can be processed concurrently, each following the defined pipeline structure.
+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 crews, lists of crews, or routers representing the stages to be executed in sequence. |
+| 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
 
@@ -43,7 +43,7 @@ When creating a pipeline, you define a series of stages, each consisting of eith
 ### Example: Assembling a Pipeline
 
 ```python
-from crewai import Crew, Agent, Task, Pipeline
+from crewai import Crew, Process, Pipeline
 
 # Define your crews
 research_crew = Crew(
@@ -74,7 +74,8 @@ my_pipeline = Pipeline(
 
 | Method           | Description                                                                                                                                                                    |
 | :--------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **process_runs** | Executes the pipeline, processing all stages and returning the results. This method initiates one or more runs through the pipeline, handling the flow of data between stages. |
+| **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
 
@@ -99,12 +100,12 @@ The output of a pipeline in the crewAI framework is encapsulated within the `Pip
 | Attribute         | Parameters      | Type                       | Description                                                                                   |
 | :---------------- | :-------------- | :------------------------- | :-------------------------------------------------------------------------------------------- |
 | **ID**            | `id`            | `UUID4`                    | A unique identifier for the run result.                                                       |
-| **Raw**           | `raw`           | `str`                      | The raw output of the final stage in the pipeline run.                                        |
-| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the final stage, if applicable. |
-| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the final stage, if applicable.                  |
-| **Token Usage**   | `token_usage`   | `Dict[str, Any]`           | A summary of token usage across all stages of the pipeline run.                               |
-| **Trace**         | `trace`         | `List[Any]`                | A trace of the journey of inputs through the pipeline run.                                    |
-| **Crews Outputs** | `crews_outputs` | `List[CrewOutput]`         | A list of `CrewOutput` objects, representing the outputs from each crew in the pipeline run.  |
+| **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
 
@@ -112,7 +113,7 @@ The output of a pipeline in the crewAI framework is encapsulated within the `Pip
 | :-------------- | :------------------------------------------------------------------------------------------------------- |
 | **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.         |
+| **str**         | Returns the string representation of the run result, prioritizing Pydantic, then JSON, then raw.         |
 
 ### Accessing Pipeline Outputs
 
@@ -239,7 +240,7 @@ email_router = Router(
             pipeline=normal_pipeline
         )
     },
-    default=Pipeline(stages=[normal_pipeline])  # Default to just classification if no urgency score
+    default=Pipeline(stages=[normal_pipeline])  # Default to just normal if no urgency score
 )
 
 # Use the router in a main pipeline
@@ -261,7 +262,7 @@ In this example, the router decides between an urgent pipeline and a normal pipe
 
 ### Error Handling and Validation
 
-The Pipeline class includes validation mechanisms to ensure the robustness of the pipeline structure:
+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

@@ -43,7 +43,7 @@ my_crew = Crew(
 
 ### Example
 
-When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
+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
@@ -96,7 +96,7 @@ A list with 10 bullet points of the most relevant information about AI LLMs.
 
 **Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings
 
-**Task Expected Output:** A fully fledge report with the main topics, each with a full section of information. Formatted as markdown without '```'
+**Task 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
 
@@ -130,5 +130,4 @@ A list with 10 bullet points of the most relevant information about AI LLMs.
    - 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 '```'.
-```
+A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.

docs/core-concepts/Tasks.md

@@ -12,22 +12,22 @@ Tasks within crewAI can be collaborative, requiring multiple agents to work toge
 
 ## Task Attributes
 
-| Attribute                        | Parameters        | Description                                                                                                          |
-| :------------------------------- | :---------------- | :------------------------------------------------------------------------------------------------------------------- |
-| **Description**                  | `description`     | A clear, concise statement of what the task entails.                                                                 |
-| **Agent**                        | `agent`           | The agent responsible for the task, assigned either directly or by the crew's process.                               |
-| **Expected Output**              | `expected_output` | A detailed description of what the task's completion looks like.                                                     |
-| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list.                  |
-| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False.    |
-| **Context** _(optional)_         | `context`         | Specifies tasks whose outputs are used as context for this task.                                                     |
-| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
-| **Output JSON** _(optional)_     | `output_json`     | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
-| **Output Pydantic** _(optional)_ | `output_pydantic` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
-| **Output File** _(optional)_     | `output_file`     | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Output** _(optional)_          | `output`          | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details.                  |
-| **Callback** _(optional)_        | `callback`        | A callable that is executed with the task's output upon completion.                                                  |
-| **Human Input** _(optional)_     | `human_input`     | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. Defaults to False.|
-| **Converter Class** _(optional)_ | `converter_cls`   | A converter class used to export structured output. Defaults to None.                                                |
+| 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
 
@@ -49,28 +49,28 @@ Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's pr
 ## Task Output
 
 !!! note "Understanding Task Outputs"
-The output of a task in the crewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw strings, JSON, and Pydantic models.
+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`                      | A brief description of the task.                                                                   |
-| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the first 10 words of the description.            |
+| **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 Output Methods and Properties
+### 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. |
+| **str**         | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
 
 ### Accessing Task Outputs
 
@@ -131,6 +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(
@@ -233,7 +234,7 @@ def callback_function(output: TaskOutput):
     print(f"""
         Task completed!
         Task: {output.description}
-        Output: {output.raw_output}
+        Output: {output.raw}
     """)
 
 research_task = Task(
@@ -274,7 +275,7 @@ result = crew.kickoff()
 print(f"""
     Task completed!
     Task: {task1.output.description}
-    Output: {task1.output.raw_output}
+    Output: {task1.output.raw}
 """)
 ```
 
@@ -312,4 +313,4 @@ save_output_task = Task(
 
 ## Conclusion
 
-Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
+Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/core-concepts/Testing.md

@@ -9,7 +9,7 @@ Testing is a crucial part of the development process, and it is essential to ens
 
 ### Using the Testing Feature
 
-We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
+We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model`, which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
 
 ```bash
 crewai test
@@ -21,20 +21,36 @@ If you want to run more iterations or use a different model, you can specify the
 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:
 
 ```
-                Task Scores
-          (1-10 Higher is better)
-┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
-┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
-┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
-│ Task 1     │ 10.0  │ 9.0   │ 9.5        │
-│ Task 2     │ 9.0   │ 9.0   │ 9.0        │
-│ Crew       │ 9.5   │ 9.0   │ 9.2        │
-└────────────┴───────┴───────┴────────────┘
+                                                     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

@@ -106,7 +106,7 @@ Here is a list of the available tools and their descriptions:
 | **CodeInterpreterTool**     | A tool for interpreting python code.                                                          |
 | **ComposioTool**            | Enables use of Composio tools.                                                                |
 | **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
-| **DALL-E Tool**            | A tool for generating images using the DALL-E API.                                        |
+| **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.                |
@@ -114,7 +114,7 @@ Here is a list of the available tools and their descriptions:
 | **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.               |
+| **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.     |
@@ -123,14 +123,14 @@ Here is a list of the available tools and their descriptions:
 | **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
 | **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
 | **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
-| **Vision Tool**            | A tool for generating images using the DALL-E API.                                        |
-| **RagTool**                 | A general-purpose RAG tool capable of handling various data sources and types.                 |
-| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.     |
-| **ScrapeWebsiteTool**       | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
-| **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.          |
+| **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
 
@@ -144,6 +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

docs/core-concepts/Training-Crew.md

@@ -16,7 +16,7 @@ To use the training feature, follow these steps:
 3. Run the following command:
 
 ```shell
-crewai train -n <n_iterations> <filename>
+crewai train -n <n_iterations> <filename> (optional)
 ```
 
 !!! note "Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`."

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/Start-a-New-CrewAI-Project-Template-Method.md

@@ -1,5 +1,7 @@
 ---
+
 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.
 ---
 
@@ -17,40 +19,13 @@ Before we start, there are a couple of things to note:
 Before getting started with CrewAI, make sure that you have installed it via pip:
 
 ```shell
-$ pip install crewai crewai-tools
+$ pip install 'crewai[tools]'
 ```
 
-### Virtual Environments
-It is highly recommended that you use virtual environments to ensure that your CrewAI project is isolated from other projects and dependencies. Virtual environments provide a clean, separate workspace for each project, preventing conflicts between different versions of packages and libraries. This isolation is crucial for maintaining consistency and reproducibility in your development process. You have multiple options for setting up virtual environments depending on your operating system and Python version:
-
-1. Use venv (Python's built-in virtual environment tool):
-   venv is included with Python 3.3 and later, making it a convenient choice for many developers. It's lightweight and easy to use, perfect for simple project setups.
-
-   To set up virtual environments with venv, refer to the official [Python documentation](https://docs.python.org/3/tutorial/venv.html).
-
-2. Use Conda (A Python virtual environment manager):
-   Conda is an open-source package manager and environment management system for Python. It's widely used by data scientists, developers, and researchers to manage dependencies and environments in a reproducible way.
-
-   To set up virtual environments with Conda, refer to the official [Conda documentation](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).
-
-3. Use Poetry (A Python package manager and dependency management tool):
-   Poetry is an open-source Python package manager that simplifies the installation of packages and their dependencies. Poetry offers a convenient way to manage virtual environments and dependencies.
-   Poetry is CrewAI's preferred tool for package / dependency management in CrewAI.
-
-### Code IDEs
-
-Most users of CrewAI use a Code Editor / Integrated Development Environment (IDE) for building their Crews. You can use any code IDE of your choice. See below for some popular options for Code Editors / Integrated Development Environments (IDE):
-
-- [Visual Studio Code](https://code.visualstudio.com/) - Most popular
-- [PyCharm](https://www.jetbrains.com/pycharm/)
-- [Cursor AI](https://cursor.com)
-
-Pick one that suits your style and needs.
-
 ## Creating a New Project
-In this example, we will be using Venv as our virtual environment manager.
 
-To set up a virtual environment, run the following CLI command:
+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
@@ -123,10 +98,13 @@ research_candidates_task:
 ```
 
 ### Referencing Variables:
-Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from task.yaml file. Ensure your annotated agent and function name is the same otherwise your task won't recognize the reference properly.
+
+Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from `tasks.yaml` file. Ensure your annotated agent and function name are the same; otherwise, your task won't recognize the reference properly.
 
 #### Example References
-agent.yaml
+
+`agents.yaml`
+
 ```yaml
 email_summarizer:
     role: >
@@ -138,7 +116,8 @@ email_summarizer:
     llm: mixtal_llm
 ```
 
-task.yaml
+`tasks.yaml`
+
 ```yaml
 email_summarizer_task:
     description: >
@@ -151,37 +130,34 @@ email_summarizer_task:
       - research_task
 ```
 
-Use the annotations to properly reference the agent and task in the crew.py file.
+Use the annotations to properly reference the agent and task in the `crew.py` file.
 
 ### Annotations include:
-* [@agent](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L17)
-* [@task](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L4)
-* [@crew](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L69)
-* [@llm](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L23)
-* [@tool](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L39)
-* [@callback](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L44)
-* [@output_json](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L29)
-* [@output_pydantic](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L34)
-* [@cache_handler](https://github.com/crewAIInc/crewAI/blob/97d7bfb52ad49a9f04db360e1b6612d98c91971e/src/crewai/project/annotations.py#L49)
 
-crew.py
-```py
+* `@agent`
+* `@task`
+* `@crew`
+* `@tool`
+* `@callback`
+* `@output_json`
+* `@output_pydantic`
+* `@cache_handler`
+
+`crew.py`
+
+```python
 # ...
-    @llm
-    def mixtal_llm(self):
-        return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
+@agent
+def email_summarizer(self) -> Agent:
+    return Agent(
+        config=self.agents_config["email_summarizer"],
+    )
 
-    @agent
-    def email_summarizer(self) -> Agent:
-        return Agent(
-            config=self.agents_config["email_summarizer"],
-        )
-    ## ...other tasks defined
-    @task
-    def email_summarizer_task(self) -> Task:
-        return Task(
-            config=self.tasks_config["email_summarizer_task"],
-        )
+@task
+def email_summarizer_task(self) -> Task:
+    return Task(
+        config=self.tasks_config["email_summarizer_task"],
+    )
 # ...
 ```
 
@@ -191,8 +167,7 @@ To install the dependencies for your project, you can use Poetry. First, navigat
 
 ```shell
 $ cd my_project
-$ poetry lock
-$ poetry install
+$ crewai install
 ```
 
 This will install the dependencies specified in the `pyproject.toml` file.
@@ -201,7 +176,7 @@ This will install the dependencies specified in the `pyproject.toml` file.
 
 Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
 
-#### agents.yaml
+#### tasks.yaml
 
 ```yaml
 research_task:
@@ -233,10 +208,6 @@ To run your project, use the following command:
 ```shell
 $ crewai run
 ```
-or
-```shell
-$ poetry run my_project
-```
 
 This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
 

docs/how-to/Conditional-Tasks.md

@@ -19,7 +19,7 @@ from crewai.task import Task
 from crewai_tools import SerperDevTool
 
 # Define a condition function for the conditional task
-# if false task will be skipped, true, then execute task
+# 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
 
@@ -29,21 +29,21 @@ data_fetcher_agent = Agent(
     goal="Fetch data online using Serper tool",
     backstory="Backstory 1",
     verbose=True,
-    tools=[SerperDevTool()],
+    tools=[SerperDevTool()]
 )
 
 data_processor_agent = Agent(
     role="Data Processor",
     goal="Process fetched data",
     backstory="Backstory 2",
-    verbose=True,
+    verbose=True
 )
 
 summary_generator_agent = Agent(
     role="Summary Generator",
     goal="Generate summary from fetched data",
     backstory="Backstory 3",
-    verbose=True,
+    verbose=True
 )
 
 class EventOutput(BaseModel):
@@ -69,7 +69,7 @@ conditional_task = ConditionalTask(
 
 task3 = Task(
     description="Generate summary of events in San Francisco from fetched data",
-    expected_output="summary_generated",
+    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,
 )
 
@@ -78,7 +78,7 @@ crew = Crew(
     agents=[data_fetcher_agent, data_processor_agent, summary_generator_agent],
     tasks=[task1, conditional_task, task3],
     verbose=True,
-    planning=True  # Enable planning feature
+    planning=True
 )
 
 # Run the crew

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

@@ -1,27 +1,31 @@
 ---
 title: Forcing Tool Output as Result
-description: Learn how to force tool output as the result in of an Agent's task in CrewAI.
+description: Learn how to force tool output as the result in an Agent's task in CrewAI.
 ---
 
 ## Introduction
-In CrewAI, you can force the output of a tool as the result of an agent's task. This feature is useful when you want to ensure that the tool output is captured and returned as the task result, and avoid the agent modifying the output during the task execution.
+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 can set the `result_as_answer` parameter to `True` when creating the agent. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
+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
 
-# Define a custom tool that returns the result as the answer
+# 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

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
@@ -54,6 +67,7 @@ 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

@@ -74,7 +74,8 @@ 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

docs/how-to/Kickoff-async.md

@@ -4,9 +4,11 @@ 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
@@ -23,10 +25,20 @@ def kickoff_async(self, inputs: dict) -> CrewOutput:
 
 - `CrewOutput`: An object representing the result of the crew execution.
 
-## Example
-Here's an example of how to kickoff a crew asynchronously:
+## 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
@@ -49,6 +61,57 @@ analysis_crew = Crew(
     tasks=[data_analysis_task]
 )
 
-# Execute the crew asynchronously
-result = analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
+# Async function to kickoff the crew asynchronously
+async def async_crew_execution():
+    result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
+    print("Crew Result:", result)
+
+# Run the async function
+asyncio.run(async_crew_execution())
+```
+
+## Example: Multiple Asynchronous Crew Executions
+
+In this example, we'll show how to kickoff multiple crews asynchronously and wait for all of them to complete using `asyncio.gather()`:
+
+```python
+import asyncio
+from crewai import Crew, Agent, Task
+
+# Create an agent with code execution enabled
+coding_agent = Agent(
+    role="Python Data Analyst",
+    goal="Analyze data and provide insights using Python",
+    backstory="You are an experienced data analyst with strong Python skills.",
+    allow_code_execution=True
+)
+
+# Create tasks that require code execution
+task_1 = Task(
+    description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
+    agent=coding_agent
+)
+
+task_2 = Task(
+    description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
+    agent=coding_agent
+)
+
+# Create two crews and add tasks
+crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
+crew_2 = Crew(agents=[coding_agent], tasks=[task_2])
+
+# Async function to kickoff multiple crews asynchronously and wait for all to finish
+async def async_multiple_crews():
+    result_1 = crew_1.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
+    result_2 = crew_2.kickoff_async(inputs={"ages": [20, 22, 24, 28, 30]})
+
+    # Wait for both crews to finish
+    results = await asyncio.gather(result_1, result_2)
+
+    for i, result in enumerate(results, 1):
+        print(f"Crew {i} Result:", result)
+
+# Run the async function
+asyncio.run(async_multiple_crews())
 ```

docs/how-to/Kickoff-for-each.md

@@ -25,13 +25,17 @@ coding_agent = Agent(
 # 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
+    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]
+    tasks=[data_analysis_task],
+    verbose=True,
+    memory=False,
+    respect_context_window=True  # enable by default
 )
 
 datasets = [
@@ -42,4 +46,4 @@ datasets = [
 
 # Execute the crew
 result = analysis_crew.kickoff_for_each(inputs=datasets)
-```
+```

docs/how-to/LLM-Connections.md

@@ -1,197 +1,163 @@
 ---
 title: Connect CrewAI to LLMs
-description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs), including detailed class attributes, methods, and configuration options.
+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-4o model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4o") for language processing. You can configure your agents to use a different model or API as described in this guide.
-    By default, CrewAI uses OpenAI's GPT-4 model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4") for language processing. You can configure your agents to use a different model or API as described in this guide.
+    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 provides extensive versatility in integrating with various Language Models (LLMs), including local options through Ollama such as Llama and Mixtral to cloud-based solutions like Azure. Its compatibility extends to all [LangChain LLM components](https://python.langchain.com/v0.2/docs/integrations/llms/), offering a wide range of integration possibilities for customized AI applications.
+## Supported Providers
 
-The platform supports connections to an array of Generative AI models, including:
+LiteLLM supports a wide range of providers, including but not limited to:
 
- - OpenAI's suite of advanced language models
- - Anthropic's cutting-edge AI offerings
- - Ollama's diverse range of locally-hosted generative model & embeddings
- - LM Studio's diverse range of locally hosted generative models & embeddings
- - Groq's Super Fast LLM offerings
- - Azures' generative AI offerings
- - HuggingFace's generative AI offerings
+- 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!
 
-This broad spectrum of LLM options enables users to select the most suitable model for their specific needs, whether prioritizing local deployment, specialized capabilities, or cloud-based scalability.
+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:
 
-## Changing the default LLM
-The default LLM is provided through the `langchain openai` package, which is installed by default when you install CrewAI. You can change this default LLM to a different model or API by setting the `OPENAI_MODEL_NAME` environment variable. This straightforward process allows you to harness the power of different OpenAI models, enhancing the flexibility and capabilities of your CrewAI implementation.
 ```python
-# Required
-os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
+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 Local Integration
-Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, you will need the `langchain-ollama` package. You can then set the following environment variables to connect to your Ollama instance running locally on port 11434.
+### 2. Using the LLM Class
 
-```sh
-os.environ[OPENAI_API_BASE]='http://localhost:11434'
-os.environ[OPENAI_MODEL_NAME]='llama2'  # Adjust based on available model
-os.environ[OPENAI_API_KEY]='' # No API Key required for Ollama
-```
+For more detailed configuration, use the LLM class:
 
-## Ollama Integration Step by Step (ex. for using Llama 3.1 8B locally)
-1. [Download and install Ollama](https://ollama.com/download).
-2. After setting up the Ollama, Pull the Llama3.1 8B model by typing following lines into your terminal ```ollama run llama3.1```.
-3. Llama3.1 should now be served locally on `http://localhost:11434`
-```
-from crewai import Agent, Task, Crew
-from langchain_ollama import ChatOllama
-import os
-os.environ["OPENAI_API_KEY"] = "NA"
-
-llm = ChatOllama(
-    model = "llama3.1",
-    base_url = "http://localhost:11434")
-
-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=True
-        )
-
-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_huggingface import HuggingFaceEndpoint,
+from crewai import Agent, LLM
 
-llm = HuggingFaceEndpoint(
-    repo_id="microsoft/Phi-3-mini-4k-instruct",
-    task="text-generation",
-    max_new_tokens=512,
-    do_sample=False,
-    repetition_penalty=1.03,
+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
 )
 ```
 
-## OpenAI Compatible API Endpoints
-Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, Groq, and Mistral AI.
+## Configuration Options
 
-### Configuration Examples
-#### FastChat
-```sh
-os.environ[OPENAI_API_BASE]="http://localhost:8001/v1"
-os.environ[OPENAI_MODEL_NAME]='oh-2.5m7b-q51'
-os.environ[OPENAI_API_KEY]=NA
-```
+When configuring an LLM for your agent, you have access to a wide range of parameters:
 
-#### 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
-os.environ[OPENAI_API_BASE]="http://localhost:1234/v1"
-os.environ[OPENAI_API_KEY]="lm-studio"
-```
+| 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 |
 
-#### Groq API
-```sh
-os.environ[OPENAI_API_KEY]=your-groq-api-key
-os.environ[OPENAI_MODEL_NAME]='llama3-8b-8192'
-os.environ[OPENAI_API_BASE]=https://api.groq.com/openai/v1
-```
+For a complete list of parameters and their descriptions, refer to the LLM class documentation.
 
-#### Mistral API
-```sh
-os.environ[OPENAI_API_KEY]=your-mistral-api-key
-os.environ[OPENAI_API_BASE]=https://api.mistral.ai/v1
-os.environ[OPENAI_MODEL_NAME]="mistral-small"
-```
+## Connecting to OpenAI-Compatible LLMs
 
-### Solar
-```sh
-from langchain_community.chat_models.solar import SolarChat
-```
-```sh
-os.environ[SOLAR_API_BASE]="https://api.upstage.ai/v1/solar"
-os.environ[SOLAR_API_KEY]="your-solar-api-key"
-```
+You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
 
-# Free developer API key available here: https://console.upstage.ai/services/solar
-# Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
+### Using Environment Variables
 
-
-### Cohere
 ```python
-from langchain_cohere import ChatCohere
-# Initialize language model
-os.environ["COHERE_API_KEY"] = "your-cohere-api-key"
-llm = ChatCohere()
+import os
 
-# Free developer API key available here: https://cohere.com/
-# Langchain Documentation: https://python.langchain.com/docs/integrations/chat/cohere
+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"
 ```
 
-### Azure Open AI Configuration
-For Azure OpenAI API integration, set the following environment variables:
-```sh
+### Using LLM Class Attributes
 
-os.environ[AZURE_OPENAI_DEPLOYMENT] = "Your deployment"
-os.environ["OPENAI_API_VERSION"] = "2023-12-01-preview"
-os.environ["AZURE_OPENAI_ENDPOINT"] = "Your Endpoint"
-os.environ["AZURE_OPENAI_API_KEY"] = "<Your API Key>"
-```
-
-### Example Agent with Azure LLM
 ```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")
+llm = LLM(
+    model="custom-model-name",
+    api_key="your-api-key",
+    base_url="https://api.your-provider.com/v1"
 )
+agent = Agent(llm=llm, ...)
+```
 
-azure_agent = Agent(
-  role='Example Agent',
-  goal='Demonstrate custom LLM configuration',
-  backstory='A diligent explorer of GitHub docs.',
-  llm=azure_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")
 )
 ```
 
+## 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.
+
 ## 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

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

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

@@ -1,6 +1,7 @@
 ---
 title: Replay Tasks from Latest Crew Kickoff
 description: Replay tasks from the latest crew.kickoff(...)
+
 ---
 
 ## Introduction
@@ -16,22 +17,24 @@ 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:
+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 from use:
+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.
+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
@@ -49,4 +52,7 @@ To replay from a task programmatically, use the following steps:
 
     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

@@ -52,14 +52,17 @@ report_crew = Crew(
 # Execute the crew
 result = report_crew.kickoff()
 
-# Accessing the type safe output
+# 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.
 
 ## Advanced Features
@@ -87,4 +90,6 @@ CrewAI tracks token usage across all tasks and agents. You can access these metr
 1. **Order Matters**: Arrange tasks in a logical sequence where each task builds upon the previous one.
 2. **Clear Task Descriptions**: Provide detailed descriptions for each task to guide the agents effectively.
 3. **Appropriate Agent Selection**: Match agents' skills and roles to the requirements of each task.
-4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones.
+4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones.
+
+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/index.md

@@ -8,13 +8,20 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
     <div style="width:25%">
         <h2>Getting Started</h2>
         <ul>
-            <li><a href='./getting-started/Installing-CrewAI'>
+            <li>
+                <a href='./getting-started/Installing-CrewAI'>
                    Installing CrewAI
-                 </a>
+                </a>
             </li>
-            <li><a href='./getting-started/Start-a-New-CrewAI-Project-Template-Method'>
+            <li>
+                <a href='./getting-started/Start-a-New-CrewAI-Project-Template-Method'>
                    Start a New CrewAI Project: Template Method
-                 </a>
+                </a>
+            </li>
+            <li>
+                <a href='./getting-started/Create-a-New-CrewAI-Pipeline-Template-Method'>
+                    Create a New CrewAI Pipeline: Template Method
+                </a>
             </li>
         </ul>
     </div>
@@ -46,6 +53,16 @@ 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/Flows">
+                    Flows
+                </a>
+            </li> -->
             <li>
                 <a href="./core-concepts/Pipeline">
                     Pipeline
@@ -73,7 +90,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
             </li>
         </ul>
     </div>
-    <div style="width:30%">
+    <div style="width:25%">
         <h2>How-To Guides</h2>
         <ul>
             <li>
@@ -148,7 +165,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
             </li>
         </ul>
     </div>
-    <div style="width:30%">
+    <!-- <div style="width:25%">
         <h2>Examples</h2>
         <ul>
             <li>
@@ -186,6 +203,26 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Landing Page Generator
                 </a>
             </li>
+            <li>
+                <a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow">
+                    Email Auto Responder Flow
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow">
+                    Lead Score Flow
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows">
+                    Write a Book Flow
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow">
+                    Meeting Assistant Flow
+                </a>
+            </li>
         </ul>
-    </div>
+    </div> -->
 </div>

docs/telemetry/Telemetry.md

@@ -5,24 +5,39 @@ description: Understanding the telemetry data collected by CrewAI and how it con
 
 ## Telemetry
 
-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. We don't offer a way to disable it now, but we will in the future.
+!!! 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.
 
-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.
+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.
 
-### 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.
+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 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. 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/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.

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
@@ -129,6 +129,7 @@ nav:
     - Processes: 'core-concepts/Processes.md'
     - Crews: 'core-concepts/Crews.md'
     - Collaboration: 'core-concepts/Collaboration.md'
+    - Pipeline: 'core-concepts/Pipeline.md'
     - Training: 'core-concepts/Training-Crew.md'
     - Memory: 'core-concepts/Memory.md'
     - Planning: 'core-concepts/Planning.md'
@@ -161,7 +162,7 @@ nav:
     - Directory RAG Search: 'tools/DirectorySearchTool.md'
     - Directory Read: 'tools/DirectoryReadTool.md'
     - Docx Rag Search: 'tools/DOCXSearchTool.md'
-    - EXA Serch Web Loader: 'tools/EXASearchTool.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'
@@ -177,6 +178,7 @@ nav:
     - 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'
@@ -208,6 +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 = "0.51.1"
+version = "0.66.0"
 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"
@@ -8,20 +8,20 @@ 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 = ">0.2,<=0.3"
+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 = "^0.8.3", optional = true }
+regex = "^2024.9.11"
+crewai-tools = { version = "^0.12.1", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -29,6 +29,10 @@ 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"
+pyvis = "^0.3.2"
 
 [tool.poetry.extras]
 tools = ["crewai-tools"]
@@ -46,7 +50,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.8.3"
+crewai-tools = "^0.12.1"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"
@@ -62,6 +66,9 @@ ignore_missing_imports = true
 disable_error_code = 'import-untyped'
 exclude = ["cli/templates"]
 
+[tool.bandit]
+exclude_dirs = ["src/crewai/cli/templates"]
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

src/crewai/__init__.py

@@ -1,7 +1,19 @@
+import warnings
+
 from crewai.agent import Agent
 from crewai.crew import Crew
+from crewai.flow.flow import Flow
+from crewai.llm import LLM
 from crewai.pipeline import Pipeline
 from crewai.process import Process
+from crewai.routers import Router
 from crewai.task import Task
 
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline"]
+warnings.filterwarnings(
+    "ignore",
+    message="Pydantic serializer warnings:",
+    category=UserWarning,
+    module="pydantic.main",
+)
+
+__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]

src/crewai/agent.py

@@ -1,23 +1,18 @@
 import os
 from inspect import signature
-from typing import Any, List, Optional, Tuple
-
-from langchain.agents.agent import RunnableAgent
-from langchain.agents.tools import BaseTool
-from langchain.agents.tools import tool as LangChainTool
-from langchain_core.agents import AgentAction
-from langchain_core.callbacks import BaseCallbackHandler
-from langchain_openai import ChatOpenAI
+from typing import Any, List, Optional, Union
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
-from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser
+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.tools.agent_tools import AgentTools
-from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
-from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
+from crewai.utilities.token_counter_callback import TokenCalcHandler
+from crewai.llm import LLM
 
 
 def mock_agent_ops_provider():
@@ -34,7 +29,6 @@ agentops = None
 
 if os.environ.get("AGENTOPS_API_KEY"):
     try:
-        import agentops  # type: ignore # Name "agentops" already defined on line 21
         from agentops import track_agent
     except ImportError:
         track_agent = mock_agent_ops_provider()
@@ -64,7 +58,6 @@ class Agent(BaseAgent):
             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
     """
 
     _times_executed: int = PrivateAttr(default=0)
@@ -81,18 +74,16 @@ class Agent(BaseAgent):
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    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."
     )
@@ -108,45 +99,86 @@ class Agent(BaseAgent):
     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.",
     )
 
-    def __init__(__pydantic_self__, **data):
-        config = data.pop("config", {})
-        super().__init__(**config, **data)
-        __pydantic_self__.agent_ops_agent_name = __pydantic_self__.role
-
     @model_validator(mode="after")
-    def set_agent_executor(self) -> "Agent":
-        """Ensure agent executor and token process are set."""
-        if hasattr(self.llm, "model_name"):
-            token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)
+    def post_init_setup(self):
+        self.agent_ops_agent_name = self.role
 
-            # Ensure self.llm.callbacks is a list
-            if not isinstance(self.llm.callbacks, list):
-                self.llm.callbacks = []
+        # 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}
 
-            # 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)
+            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
+                "OPENAI_BASE_URL"
+            )
+            if api_base:
+                llm_params["base_url"] = api_base
 
-            if agentops and not any(
-                isinstance(handler, agentops.LangchainCallbackHandler)
-                for handler in self.llm.callbacks
-            ):
-                agentops.stop_instrumenting()
-                self.llm.callbacks.append(agentops.LangchainCallbackHandler())
+            api_key = os.environ.get("OPENAI_API_KEY")
+            if api_key:
+                llm_params["api_key"] = api_key
+
+            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)
+
+        # 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,
@@ -184,15 +216,7 @@ class Agent(BaseAgent):
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
         tools = tools or self.tools or []
-        parsed_tools = self._parse_tools(tools)
-        self.create_agent_executor(tools=tools)
-        self.agent_executor.tools = parsed_tools
-        self.agent_executor.task = task
-
-        self.agent_executor.tools_description = self._render_text_description_and_args(
-            parsed_tools
-        )
-        self.agent_executor.tools_names = self.__tools_names(parsed_tools)
+        self.create_agent_executor(tools=tools, task=task)
 
         if self.crew and self.crew._train:
             task_prompt = self._training_handler(task_prompt=task_prompt)
@@ -205,6 +229,7 @@ class Agent(BaseAgent):
                     "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:
@@ -213,7 +238,7 @@ class Agent(BaseAgent):
                 raise e
             result = self.execute_task(task, context, tools)
 
-        if self.max_rpm:
+        if self.max_rpm and self._rpm_controller:
             self._rpm_controller.stop_rpm_counter()
 
         # If there was any tool in self.tools_results that had result_as_answer
@@ -225,73 +250,25 @@ class Agent(BaseAgent):
 
         return result
 
-    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 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 or []
-
-        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,
-            "max_tokens": self.max_tokens,
-        }
-
-        if self._rpm_controller:
-            executor_args["request_within_rpm_limit"] = (
-                self._rpm_controller.check_or_wait
-            )
+        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:
@@ -299,11 +276,26 @@ class Agent(BaseAgent):
                 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 get_delegation_tools(self, agents: List[BaseAgent]):
@@ -324,7 +316,7 @@ class Agent(BaseAgent):
     def get_output_converter(self, llm, text, model, instructions):
         return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
-    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:  # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
+    def _parse_tools(self, tools: List[Any]) -> List[Any]:  # type: ignore
         """Parse tools to be used for the task."""
         tools_list = []
         try:
@@ -352,8 +344,9 @@ class Agent(BaseAgent):
                 human_feedbacks = [
                     i["human_feedback"] for i in data.get(agent_id, {}).values()
                 ]
-                task_prompt += "You MUST follow these feedbacks: \n " + "\n - ".join(
-                    human_feedbacks
+                task_prompt += (
+                    "\n\nYou MUST follow these instructions: \n "
+                    + "\n - ".join(human_feedbacks)
                 )
 
         return task_prompt
@@ -362,12 +355,13 @@ class Agent(BaseAgent):
         """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 += "You MUST follow these feedbacks: \n " + "\n - ".join(
-                    trained_data_output["suggestions"]
+                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[BaseTool]) -> str:
+    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:
@@ -386,7 +380,7 @@ class Agent(BaseAgent):
 
         return description
 
-    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
+    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:

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

@@ -7,7 +7,6 @@ from typing import Any, Dict, List, Optional, TypeVar
 from pydantic import (
     UUID4,
     BaseModel,
-    ConfigDict,
     Field,
     InstanceOf,
     PrivateAttr,
@@ -20,6 +19,7 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.utilities import I18N, Logger, RPMController
+from crewai.utilities.config import process_config
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -74,21 +74,26 @@ class BaseAgent(ABC, BaseModel):
     """
 
     __hash__ = object.__hash__  # type: ignore
-    _logger: Logger = PrivateAttr()
-    _rpm_controller: RPMController = PrivateAttr(default=None)
+    _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
+    _rpm_controller: Optional[RPMController] = PrivateAttr(default=None)
     _request_within_rpm_limit: Any = PrivateAttr(default=None)
-    formatting_errors: int = 0
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    _original_role: Optional[str] = PrivateAttr(default=None)
+    _original_goal: Optional[str] = PrivateAttr(default=None)
+    _original_backstory: Optional[str] = PrivateAttr(default=None)
+    _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
+    formatting_errors: int = Field(
+        default=0, description="Number of formatting errors."
+    )
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Objective of the agent")
     backstory: str = Field(description="Backstory of the agent")
+    config: Optional[Dict[str, Any]] = Field(
+        description="Configuration for the agent", default=None, exclude=True
+    )
     cache: bool = Field(
         default=True, description="Whether the agent should use a cache for tool usage."
     )
-    config: Optional[Dict[str, Any]] = Field(
-        description="Configuration for the agent", default=None
-    )
     verbose: bool = Field(
         default=False, description="Verbose mode for the Agent Execution"
     )
@@ -97,7 +102,8 @@ class BaseAgent(ABC, BaseModel):
         description="Maximum number of requests per minute for the agent execution to be respected.",
     )
     allow_delegation: bool = Field(
-        default=True, description="Allow delegation of tasks to agents"
+        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"
@@ -123,20 +129,29 @@ class BaseAgent(ABC, BaseModel):
         default=None, description="Maximum number of tokens for the agent's execution."
     )
 
-    _original_role: str | None = None
-    _original_goal: str | None = None
-    _original_backstory: str | None = None
-    _token_process: TokenProcess = TokenProcess()
-
-    def __init__(__pydantic_self__, **data):
-        config = data.pop("config", {})
-        super().__init__(**config, **data)
+    @model_validator(mode="before")
+    @classmethod
+    def process_model_config(cls, values):
+        return process_config(values, cls)
 
     @model_validator(mode="after")
-    def set_config_attributes(self):
-        if self.config:
-            for key, value in self.config.items():
-                setattr(self, key, value)
+    def validate_and_set_attributes(self):
+        # Validate required fields
+        for field in ["role", "goal", "backstory"]:
+            if getattr(self, field) is None:
+                raise ValueError(
+                    f"{field} must be provided either directly or through config"
+                )
+
+        # Set private attributes
+        self._logger = Logger(verbose=self.verbose)
+        if self.max_rpm and not self._rpm_controller:
+            self._rpm_controller = RPMController(
+                max_rpm=self.max_rpm, logger=self._logger
+            )
+        if not self._token_process:
+            self._token_process = TokenProcess()
+
         return self
 
     @field_validator("id", mode="before")
@@ -147,14 +162,6 @@ class BaseAgent(ABC, BaseModel):
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
 
-    @model_validator(mode="after")
-    def set_attributes_based_on_config(self) -> "BaseAgent":
-        """Set attributes based on the agent configuration."""
-        if self.config:
-            for key, value in self.config.items():
-                setattr(self, key, value)
-        return self
-
     @model_validator(mode="after")
     def set_private_attrs(self):
         """Set private attributes."""
@@ -169,8 +176,12 @@ class BaseAgent(ABC, BaseModel):
 
     @property
     def key(self):
-        source = [self.role, self.goal, self.backstory]
-        return md5("|".join(source).encode()).hexdigest()
+        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(
@@ -218,10 +229,8 @@ class BaseAgent(ABC, BaseModel):
 
         # Copy llm and clear callbacks
         existing_llm = shallow_copy(self.llm)
-        existing_llm.callbacks = []
         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

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -6,6 +6,7 @@ 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:
@@ -19,15 +20,14 @@ class CrewAgentExecutorMixin:
     crew_agent: Optional["BaseAgent"]
     task: Optional["Task"]
     iterations: int
-    force_answer_max_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.force_answer_max_iterations
-        ) and not self.have_forced_answer
+        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."""
@@ -102,6 +102,12 @@ class CrewAgentExecutorMixin:
 
     def _ask_human_input(self, final_answer: dict) -> str:
         """Prompt human input for final decision making."""
-        return input(
-            self._i18n.slice("getting_input").format(final_answer=final_answer)
+        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_output_converter.py

@@ -39,9 +39,3 @@ class OutputConverter(BaseModel, ABC):
     def to_json(self, current_attempt=1):
         """Convert text to json."""
         pass
-
-    @property
-    @abstractmethod
-    def is_gpt(self) -> bool:
-        """Return if llm provided is of gpt from openai."""
-        pass

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 +1,357 @@
+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}
+        if self.llm.stop:
+            self.llm.stop = list(set(self.llm.stop + self.stop))
+        else:
+            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

@@ -1,405 +0,0 @@
-import threading
-import time
-from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
-import click
-
-
-from langchain.agents import AgentExecutor
-from langchain.agents.agent import ExceptionTool
-from langchain.callbacks.manager import CallbackManagerForChainRun
-from langchain_core.agents import AgentAction, AgentFinish, AgentStep
-from langchain_core.exceptions import OutputParserException
-from langchain_core.tools import BaseTool
-from langchain_core.utils.input import get_color_mapping
-from pydantic import InstanceOf
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.summarize import load_summarize_chain
-
-from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
-from crewai.agents.tools_handler import ToolsHandler
-
-
-from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
-from crewai.utilities import I18N
-from crewai.utilities.constants import TRAINING_DATA_FILE
-from crewai.utilities.exceptions.context_window_exceeding_exception import (
-    LLMContextLengthExceededException,
-)
-from crewai.utilities.training_handler import CrewTrainingHandler
-from crewai.utilities.logger import Logger
-
-
-class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
-    _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  # type: ignore # Incompatible types in assignment (expression has type "int | None", base class "CrewAgentExecutorMixin" defined the type as "int")
-    step_callback: Optional[Any] = None
-    system_template: Optional[str] = None
-    prompt_template: Optional[str] = None
-    response_template: Optional[str] = None
-    _logger: Logger = Logger()
-    _fit_context_window_strategy: Optional[Literal["summarize"]] = "summarize"
-
-    def _call(
-        self,
-        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 and 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(
-                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
-
-        except Exception as e:
-            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
-                str(e)
-            ):
-                output = self._handle_context_length_error(
-                    intermediate_steps, run_manager, inputs
-                )
-
-                if isinstance(output, AgentFinish):
-                    yield output
-                elif isinstance(output, list):
-                    for step in output:
-                        yield step
-                return
-
-            yield AgentStep(
-                action=AgentAction("_Exception", str(e), str(e)),
-                observation=str(e),
-            )
-            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:
-                human_feedback = self._ask_human_input(output.return_values["output"])
-
-                if self.crew and self.crew._train:
-                    self._handle_crew_training_output(output, human_feedback)
-
-                # Making sure we only ask for it once, so disabling for the next thought loop
-                self.should_ask_for_human_input = False
-                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:
-                if self.crew and self.crew._train:
-                    self._handle_crew_training_output(output)
-
-                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,
-                agent=self.crew_agent,
-                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
-                ] or tool_calling.tool_name.casefold().replace("_", " ") in [
-                    name.casefold().strip() for name in name_to_tool_map
-                ]:
-                    observation = tool_usage.use(tool_calling, agent_action.log)
-                else:
-                    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 _handle_crew_training_output(
-        self, output: AgentFinish, human_feedback: str | None = None
-    ) -> None:
-        """Function to handle the process of the training data."""
-        agent_id = str(self.crew_agent.id)
-
-        if (
-            CrewTrainingHandler(TRAINING_DATA_FILE).load()
-            and not self.should_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"
-                ] = output.return_values["output"]
-                CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
-
-        if self.should_ask_for_human_input and human_feedback is not None:
-            training_data = {
-                "initial_output": output.return_values["output"],
-                "human_feedback": human_feedback,
-                "agent": agent_id,
-                "agent_role": self.crew_agent.role,
-            }
-            CrewTrainingHandler(TRAINING_DATA_FILE).append(
-                self.crew._train_iteration, agent_id, training_data
-            )
-
-    def _handle_context_length(
-        self, intermediate_steps: List[Tuple[AgentAction, str]]
-    ) -> List[Tuple[AgentAction, str]]:
-        text = intermediate_steps[0][1]
-        original_action = intermediate_steps[0][0]
-
-        text_splitter = RecursiveCharacterTextSplitter(
-            separators=["\n\n", "\n"],
-            chunk_size=8000,
-            chunk_overlap=500,
-        )
-
-        if self._fit_context_window_strategy == "summarize":
-            docs = text_splitter.create_documents([text])
-            self._logger.log(
-                "debug",
-                "Summarizing Content, it is recommended to use a RAG tool",
-                color="bold_blue",
-            )
-            summarize_chain = load_summarize_chain(
-                self.llm, chain_type="map_reduce", verbose=True
-            )
-            summarized_docs = []
-            for doc in docs:
-                summary = summarize_chain.invoke(
-                    {"input_documents": [doc]}, return_only_outputs=True
-                )
-
-                summarized_docs.append(summary["output_text"])
-
-            formatted_results = "\n\n".join(summarized_docs)
-            summary_step = AgentStep(
-                action=AgentAction(
-                    tool=original_action.tool,
-                    tool_input=original_action.tool_input,
-                    log=original_action.log,
-                ),
-                observation=formatted_results,
-            )
-            summary_tuple = (summary_step.action, summary_step.observation)
-            return [summary_tuple]
-
-        return intermediate_steps
-
-    def _handle_context_length_error(
-        self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        run_manager: Optional[CallbackManagerForChainRun],
-        inputs: Dict[str, str],
-    ) -> Union[AgentFinish, List[AgentStep]]:
-        self._logger.log(
-            "debug",
-            "Context length exceeded. Asking user if they want to use summarize prompt to fit, this will reduce context length.",
-            color="yellow",
-        )
-        user_choice = click.confirm(
-            "Context length exceeded. Do you want to summarize the text to fit models context window?"
-        )
-        if user_choice:
-            self._logger.log(
-                "debug",
-                "Context length exceeded. Using summarize prompt to fit, this will reduce context length.",
-                color="bold_blue",
-            )
-            intermediate_steps = self._handle_context_length(intermediate_steps)
-
-            output = self.agent.plan(
-                intermediate_steps,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **inputs,
-            )
-
-            if isinstance(output, AgentFinish):
-                return output
-            elif isinstance(output, AgentAction):
-                return [AgentStep(action=output, observation=None)]
-            else:
-                return [AgentStep(action=action, observation=None) for action in output]
-        else:
-            self._logger.log(
-                "debug",
-                "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
-                color="red",
-            )
-            raise SystemExit(
-                "Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
-            )

src/crewai/agents/parser.py

@@ -1,10 +1,6 @@
 import re
 from typing import Any, Union
-
 from json_repair import repair_json
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
-from langchain_core.agents import AgentAction, AgentFinish
-from langchain_core.exceptions import OutputParserException
 
 from crewai.utilities import I18N
 
@@ -14,7 +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.
@@ -38,7 +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]*(.*)"
@@ -47,7 +79,7 @@ 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)
             clean_action = self._clean_action(action)
@@ -57,30 +89,23 @@ class CrewAgentParser(ReActSingleInputOutputParser):
             tool_input = action_input.strip(" ").strip('"')
             safe_tool_input = self._safe_repair_json(tool_input)
 
-            return AgentAction(clean_action, safe_tool_input, text)
+            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")
@@ -88,11 +113,15 @@ 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()

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

@@ -1,16 +1,25 @@
+from typing import Optional
+
 import click
 import pkg_resources
 
 from crewai.cli.create_crew import create_crew
+from crewai.cli.create_flow import create_flow
 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 .evaluate_crew import evaluate_crew
+from .install_crew import install_crew
+from .plot_flow import plot_flow
 from .replay_from_task import replay_task_command
 from .reset_memories_command import reset_memories_command
 from .run_crew import run_crew
+from .run_flow import run_flow
+from .tools.main import ToolCommand
 from .train_crew import train_crew
 
 
@@ -20,19 +29,20 @@ def crewai():
 
 
 @crewai.command()
-@click.argument("type", type=click.Choice(["crew", "pipeline"]))
+@click.argument("type", type=click.Choice(["crew", "pipeline", "flow"]))
 @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."""
+def create(type, name):
+    """Create a new crew, pipeline, or flow."""
     if type == "crew":
         create_crew(name)
     elif type == "pipeline":
-        create_pipeline(name, router)
+        create_pipeline(name)
+    elif type == "flow":
+        create_flow(name)
     else:
-        click.secho("Error: Invalid type. Must be 'crew' or 'pipeline'.", fg="red")
+        click.secho(
+            "Error: Invalid type. Must be 'crew', 'pipeline', or 'flow'.", fg="red"
+        )
 
 
 @crewai.command()
@@ -165,12 +175,125 @@ def test(n_iterations: int, model: str):
     evaluate_crew(n_iterations, model)
 
 
+@crewai.command()
+def install():
+    """Install the Crew."""
+    install_crew()
+
+
 @crewai.command()
 def run():
-    """Run the crew."""
-    click.echo("Running the crew")
+    """Run the Crew."""
+    click.echo("Running the Crew")
     run_crew()
 
 
+@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)
+
+
+@crewai.group()
+def flow():
+    """Flow related commands."""
+    pass
+
+
+@flow.command(name="run")
+def flow_run():
+    """Run the Flow."""
+    click.echo("Running the Flow")
+    run_flow()
+
+
+@flow.command(name="plot")
+def flow_plot():
+    """Plot the Flow."""
+    click.echo("Plotting the Flow")
+    plot_flow()
+
+
 if __name__ == "__main__":
     crewai()

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_flow.py

@@ -0,0 +1,93 @@
+from pathlib import Path
+
+import click
+
+
+def create_flow(name):
+    """Create a new flow."""
+    folder_name = name.replace(" ", "_").replace("-", "_").lower()
+    class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
+
+    click.secho(f"Creating flow {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 / "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
+    templates_dir = package_dir / "templates" / "flow"
+
+    # 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"]
+
+    crew_folders = [
+        "poem_crew",
+    ]
+
+    def process_file(src_file, dst_file):
+        if src_file.suffix in [".pyc", ".pyo", ".pyd"]:
+            return
+
+        try:
+            with open(src_file, "r", encoding="utf-8") as file:
+                content = file.read()
+        except Exception as e:
+            click.secho(f"Error processing file {src_file}: {e}", fg="red")
+            return
+
+        content = content.replace("{{name}}", name)
+        content = content.replace("{{flow_name}}", class_name)
+        content = content.replace("{{folder_name}}", folder_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
+        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():
+            for src_file in src_crew_folder.rglob("*"):
+                if src_file.is_file():
+                    relative_path = src_file.relative_to(src_crew_folder)
+                    dst_file = dst_crew_folder / relative_path
+                    dst_file.parent.mkdir(parents=True, exist_ok=True)
+                    process_file(src_file, dst_file)
+        else:
+            click.secho(
+                f"Warning: Crew folder {crew_folder} not found in template.",
+                fg="yellow",
+            )
+
+    click.secho(f"Flow {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/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/plot_flow.py

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

src/crewai/cli/run_flow.py

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

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

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

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

@@ -10,8 +10,6 @@ from crewai.project import CrewBase, agent, crew, task
 @CrewBase
 class {{crew_name}}Crew():
 	"""{{crew_name}} crew"""
-	agents_config = 'config/agents.yaml'
-	tasks_config = 'config/tasks.yaml'
 
 	@agent
 	def researcher(self) -> Agent:

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

@@ -6,7 +6,8 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.51.0" }
+crewai = { extras = ["tools"], version = ">=0.66.0,<1.0.0" }
+
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"

src/crewai/cli/templates/flow/.gitignore

@@ -0,0 +1,3 @@
+.env
+__pycache__/
+lib/

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

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

src/crewai/cli/templates/flow/crews/poem_crew/config/agents.yaml

@@ -0,0 +1,11 @@
+poem_writer:
+  role: >
+    CrewAI Poem Writer
+  goal: >
+    Generate a funny, light heartedpoem about how CrewAI 
+    is awesome with a sentence count of {sentence_count}
+  backstory: >
+    You're a creative poet with a talent for capturing the essence of any topic
+    in a beautiful and engaging way. Known for your ability to craft poems that
+    resonate with readers, you bring a unique perspective and artistic flair to
+    every piece you write.

src/crewai/cli/templates/flow/crews/poem_crew/config/tasks.yaml

@@ -0,0 +1,7 @@
+write_poem:
+  description: >
+    Write a poem about how CrewAI is awesome.
+    Ensure the poem is engaging and adheres to the specified sentence count of {sentence_count}.
+  expected_output: >
+    A beautifully crafted poem about CrewAI, with exactly {sentence_count} sentences.
+  agent: poem_writer

src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py

@@ -0,0 +1,31 @@
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+
+@CrewBase
+class PoemCrew():
+	"""Poem Crew"""
+
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
+
+	@agent
+	def poem_writer(self) -> Agent:
+		return Agent(
+			config=self.agents_config['poem_writer'],
+		)
+
+	@task
+	def write_poem(self) -> Task:
+		return Task(
+			config=self.tasks_config['write_poem'],
+		)
+
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the Research Crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+		)

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

@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+import asyncio
+from random import randint
+
+from pydantic import BaseModel
+from crewai.flow.flow import Flow, listen, start
+from .crews.poem_crew.poem_crew import PoemCrew
+
+class PoemState(BaseModel):
+    sentence_count: int = 1
+    poem: str = ""
+
+class PoemFlow(Flow[PoemState]):
+
+    @start()
+    def generate_sentence_count(self):
+        print("Generating sentence count")
+        # Generate a number between 1 and 5
+        self.state.sentence_count = randint(1, 5)  
+
+    @listen(generate_sentence_count)
+    def generate_poem(self):
+        print("Generating poem")
+        print(f"State before poem: {self.state}")
+        result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count})
+        
+        print("Poem generated", result.raw)
+        self.state.poem = result.raw
+        
+        print(f"State after generate_poem: {self.state}")
+
+    @listen(generate_poem)
+    def save_poem(self):
+        print("Saving poem")
+        print(f"State before save_poem: {self.state}")
+        with open("poem.txt", "w") as f:
+            f.write(self.state.poem)
+        print(f"State after save_poem: {self.state}")
+
+async def run_flow():
+    """
+    Run the flow.
+    """
+    poem_flow = PoemFlow()
+    await poem_flow.kickoff()
+
+async def plot_flow():
+    """
+    Plot the flow.
+    """
+    poem_flow = PoemFlow()
+    poem_flow.plot()
+
+
+def main():
+    asyncio.run(run_flow())
+
+
+def plot():
+    asyncio.run(plot_flow())
+
+
+
+if __name__ == "__main__":
+    main()

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

@@ -0,0 +1,19 @@
+[tool.poetry]
+name = "{{folder_name}}"
+version = "0.1.0"
+description = "{{name}} using crewAI"
+authors = ["Your Name <you@example.com>"]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<=3.13"
+crewai = { extras = ["tools"], version = ">=0.66.0,<1.0.0" }
+asyncio = "*"
+
+[tool.poetry.scripts]
+{{folder_name}} = "{{folder_name}}.main:main"
+run_flow = "{{folder_name}}.main:main"
+plot_flow = "{{folder_name}}.main:plot"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

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

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

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

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

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.51.0" }
+crewai = { extras = ["tools"], version = ">=0.66.0,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

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

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

@@ -6,7 +6,8 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.51.0" }
+crewai = { extras = ["tools"], version = ">=0.66.0,<1.0.0" }
+
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:main"

src/crewai/cli/tools/main.py

@@ -0,0 +1,168 @@
+import base64
+import click
+import os
+import subprocess
+import tempfile
+
+from crewai.cli.command import BaseCommand, PlusAPIMixin
+from crewai.cli.utils import (
+    get_project_name,
+    get_project_description,
+    get_project_version,
+)
+from rich.console import Console
+
+console = Console()
+
+
+class ToolCommand(BaseCommand, PlusAPIMixin):
+    """
+    A class to handle tool repository related operations for CrewAI projects.
+    """
+
+    def __init__(self):
+        BaseCommand.__init__(self)
+        PlusAPIMixin.__init__(self, telemetry=self._telemetry)
+
+    def publish(self, is_public: bool):
+        project_name = get_project_name(require=True)
+        assert isinstance(project_name, str)
+
+        project_version = get_project_version(require=True)
+        assert isinstance(project_version, str)
+
+        project_description = get_project_description(require=False)
+        encoded_tarball = None
+
+        with tempfile.TemporaryDirectory() as temp_build_dir:
+            subprocess.run(
+                ["poetry", "build", "-f", "sdist", "--output", temp_build_dir],
+                check=True,
+                capture_output=False,
+            )
+
+            tarball_filename = next(
+                (f for f in os.listdir(temp_build_dir) if f.endswith(".tar.gz")), None
+            )
+            if not tarball_filename:
+                console.print(
+                    "Project build failed. Please ensure that the command `poetry build -f sdist` completes successfully.",
+                    style="bold red",
+                )
+                raise SystemExit
+
+            tarball_path = os.path.join(temp_build_dir, tarball_filename)
+            with open(tarball_path, "rb") as file:
+                tarball_contents = file.read()
+
+            encoded_tarball = base64.b64encode(tarball_contents).decode("utf-8")
+
+        publish_response = self.plus_api_client.publish_tool(
+            handle=project_name,
+            is_public=is_public,
+            version=project_version,
+            description=project_description,
+            encoded_file=f"data:application/x-gzip;base64,{encoded_tarball}",
+        )
+        if publish_response.status_code == 422:
+            console.print(
+                "[bold red]Failed to publish tool. Please fix the following errors:[/bold red]"
+            )
+            for field, messages in publish_response.json().items():
+                for message in messages:
+                    console.print(
+                        f"* [bold red]{field.capitalize()}[/bold red] {message}"
+                    )
+
+            raise SystemExit
+        elif publish_response.status_code != 200:
+            self._handle_plus_api_error(publish_response.json())
+            console.print(
+                "Failed to publish tool. Please try again later.", style="bold red"
+            )
+            raise SystemExit
+
+        published_handle = publish_response.json()["handle"]
+        console.print(
+            f"Succesfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
+            style="bold green",
+        )
+
+    def install(self, handle: str):
+        get_response = self.plus_api_client.get_tool(handle)
+
+        if get_response.status_code == 404:
+            console.print(
+                "No tool found with this name. Please ensure the tool was published and you have access to it.",
+                style="bold red",
+            )
+            raise SystemExit
+        elif get_response.status_code != 200:
+            console.print(
+                "Failed to get tool details. Please try again later.", style="bold red"
+            )
+            raise SystemExit
+
+        self._add_repository_to_poetry(get_response.json())
+        self._add_package(get_response.json())
+
+        console.print(f"Succesfully installed {handle}", style="bold green")
+
+    def _add_repository_to_poetry(self, tool_details):
+        repository_handle = f"crewai-{tool_details['repository']['handle']}"
+        repository_url = tool_details["repository"]["url"]
+        repository_credentials = tool_details["repository"]["credentials"]
+
+        add_repository_command = [
+            "poetry",
+            "source",
+            "add",
+            "--priority=explicit",
+            repository_handle,
+            repository_url,
+        ]
+        add_repository_result = subprocess.run(
+            add_repository_command, text=True, check=True
+        )
+
+        if add_repository_result.stderr:
+            click.echo(add_repository_result.stderr, err=True)
+            raise SystemExit
+
+        add_repository_credentials_command = [
+            "poetry",
+            "config",
+            f"http-basic.{repository_handle}",
+            repository_credentials,
+            '""',
+        ]
+        add_repository_credentials_result = subprocess.run(
+            add_repository_credentials_command,
+            capture_output=False,
+            text=True,
+            check=True,
+        )
+
+        if add_repository_credentials_result.stderr:
+            click.echo(add_repository_credentials_result.stderr, err=True)
+            raise SystemExit
+
+    def _add_package(self, tool_details):
+        tool_handle = tool_details["handle"]
+        repository_handle = tool_details["repository"]["handle"]
+        pypi_index_handle = f"crewai-{repository_handle}"
+
+        add_package_command = [
+            "poetry",
+            "add",
+            "--source",
+            pypi_index_handle,
+            tool_handle,
+        ]
+        add_package_result = subprocess.run(
+            add_package_command, capture_output=False, text=True, check=True
+        )
+
+        if add_package_result.stderr:
+            click.echo(add_package_result.stderr, err=True)
+            raise SystemExit

src/crewai/cli/utils.py

@@ -1,4 +1,17 @@
 import click
+import re
+import subprocess
+import sys
+
+from crewai.cli.authentication.utils import TokenManager
+from functools import reduce
+from rich.console import Console
+from typing import Any, Dict, List
+
+if sys.version_info >= (3, 11):
+    import tomllib
+
+console = Console()
 
 
 def copy_template(src, dst, name, class_name, folder_name):
@@ -16,3 +29,191 @@ def copy_template(src, dst, name, class_name, folder_name):
         file.write(content)
 
     click.secho(f"  - Created {dst}", fg="green")
+
+
+# Drop the simple_toml_parser when we move to python3.11
+def simple_toml_parser(content):
+    result = {}
+    current_section = result
+    for line in content.split("\n"):
+        line = line.strip()
+        if line.startswith("[") and line.endswith("]"):
+            # New section
+            section = line[1:-1].split(".")
+            current_section = result
+            for key in section:
+                current_section = current_section.setdefault(key, {})
+        elif "=" in line:
+            key, value = line.split("=", 1)
+            key = key.strip()
+            value = value.strip().strip('"')
+            current_section[key] = value
+    return result
+
+
+def parse_toml(content):
+    if sys.version_info >= (3, 11):
+        return tomllib.loads(content)
+    else:
+        return simple_toml_parser(content)
+
+
+def get_git_remote_url() -> str | None:
+    """Get the Git repository's remote URL."""
+    try:
+        # Run the git remote -v command
+        result = subprocess.run(
+            ["git", "remote", "-v"], capture_output=True, text=True, check=True
+        )
+
+        # Get the output
+        output = result.stdout
+
+        # Parse the output to find the origin URL
+        matches = re.findall(r"origin\s+(.*?)\s+\(fetch\)", output)
+
+        if matches:
+            return matches[0]  # Return the first match (origin URL)
+        else:
+            console.print("No origin remote found.", style="bold red")
+
+    except subprocess.CalledProcessError as e:
+        console.print(
+            f"Error running trying to fetch the Git Repository: {e}", style="bold red"
+        )
+    except FileNotFoundError:
+        console.print(
+            "Git command not found. Make sure Git is installed and in your PATH.",
+            style="bold red",
+        )
+
+    return None
+
+
+def get_project_name(
+    pyproject_path: str = "pyproject.toml", require: bool = False
+) -> str | None:
+    """Get the project name from the pyproject.toml file."""
+    return _get_project_attribute(
+        pyproject_path, ["tool", "poetry", "name"], require=require
+    )
+
+
+def get_project_version(
+    pyproject_path: str = "pyproject.toml", require: bool = False
+) -> str | None:
+    """Get the project version from the pyproject.toml file."""
+    return _get_project_attribute(
+        pyproject_path, ["tool", "poetry", "version"], require=require
+    )
+
+
+def get_project_description(
+    pyproject_path: str = "pyproject.toml", require: bool = False
+) -> str | None:
+    """Get the project description from the pyproject.toml file."""
+    return _get_project_attribute(
+        pyproject_path, ["tool", "poetry", "description"], require=require
+    )
+
+
+def _get_project_attribute(
+    pyproject_path: str, keys: List[str], require: bool
+) -> Any | None:
+    """Get an attribute from the pyproject.toml file."""
+    attribute = None
+
+    try:
+        with open(pyproject_path, "r") as f:
+            pyproject_content = parse_toml(f.read())
+
+        dependencies = (
+            _get_nested_value(pyproject_content, ["tool", "poetry", "dependencies"])
+            or {}
+        )
+        if "crewai" not in dependencies:
+            raise Exception("crewai is not in the dependencies.")
+
+        attribute = _get_nested_value(pyproject_content, keys)
+    except FileNotFoundError:
+        print(f"Error: {pyproject_path} not found.")
+    except KeyError:
+        print(f"Error: {pyproject_path} is not a valid pyproject.toml file.")
+    except tomllib.TOMLDecodeError if sys.version_info >= (3, 11) else Exception as e:  # type: ignore
+        print(
+            f"Error: {pyproject_path} is not a valid TOML file."
+            if sys.version_info >= (3, 11)
+            else f"Error reading the pyproject.toml file: {e}"
+        )
+    except Exception as e:
+        print(f"Error reading the pyproject.toml file: {e}")
+
+    if require and not attribute:
+        console.print(
+            f"Unable to read '{'.'.join(keys)}' in the pyproject.toml file. Please verify that the file exists and contains the specified attribute.",
+            style="bold red",
+        )
+        raise SystemExit
+
+    return attribute
+
+
+def _get_nested_value(data: Dict[str, Any], keys: List[str]) -> Any:
+    return reduce(dict.__getitem__, keys, data)
+
+
+def get_crewai_version(poetry_lock_path: str = "poetry.lock") -> str:
+    """Get the version number of crewai from the poetry.lock file."""
+    try:
+        with open(poetry_lock_path, "r") as f:
+            lock_content = f.read()
+
+        match = re.search(
+            r'\[\[package\]\]\s*name\s*=\s*"crewai"\s*version\s*=\s*"([^"]+)"',
+            lock_content,
+            re.DOTALL,
+        )
+        if match:
+            return match.group(1)
+        else:
+            print("crewai package not found in poetry.lock")
+            return "no-version-found"
+
+    except FileNotFoundError:
+        print(f"Error: {poetry_lock_path} not found.")
+    except Exception as e:
+        print(f"Error reading the poetry.lock file: {e}")
+
+    return "no-version-found"
+
+
+def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
+    """Fetch the environment variables from a .env file and return them as a dictionary."""
+    try:
+        # Read the .env file
+        with open(env_file_path, "r") as f:
+            env_content = f.read()
+
+        # Parse the .env file content to a dictionary
+        env_dict = {}
+        for line in env_content.splitlines():
+            if line.strip() and not line.strip().startswith("#"):
+                key, value = line.split("=", 1)
+                env_dict[key.strip()] = value.strip()
+
+        return env_dict
+
+    except FileNotFoundError:
+        print(f"Error: {env_file_path} not found.")
+    except Exception as e:
+        print(f"Error reading the .env file: {e}")
+
+    return {}
+
+
+def get_auth_token() -> str:
+    """Get the authentication token."""
+    access_token = TokenManager().get_token()
+    if not access_token:
+        raise Exception()
+    return access_token

src/crewai/crew.py

@@ -1,16 +1,14 @@
 import asyncio
 import json
+import os
 import uuid
 from concurrent.futures import Future
 from hashlib import md5
-import os
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
-from langchain_core.callbacks import BaseCallbackHandler
 from pydantic import (
     UUID4,
     BaseModel,
-    ConfigDict,
     Field,
     InstanceOf,
     Json,
@@ -24,6 +22,7 @@ from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
 from crewai.crews.crew_output import CrewOutput
+from crewai.llm import LLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
@@ -48,11 +47,10 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-
 agentops = None
 if os.environ.get("AGENTOPS_API_KEY"):
     try:
-        import agentops
+        import agentops  # type: ignore
     except ImportError:
         pass
 
@@ -70,7 +68,6 @@ class Crew(BaseModel):
         manager_llm: The language model that will run manager agent.
         manager_agent: Custom agent that will be used as manager.
         memory: Whether the crew should use memory to store memories of it's execution.
-        manager_callbacks: The callback handlers to be executed by the manager agent when hierarchical process is used
         cache: Whether the crew should use a cache to store the results of the tools execution.
         function_calling_llm: The language model that will run the tool calling for all the agents.
         process: The process flow that the crew will follow (e.g., sequential, hierarchical).
@@ -106,7 +103,6 @@ class Crew(BaseModel):
 
     name: Optional[str] = Field(default=None)
     cache: bool = Field(default=True)
-    model_config = ConfigDict(arbitrary_types_allowed=True)
     tasks: List[Task] = Field(default_factory=list)
     agents: List[BaseAgent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
@@ -115,6 +111,18 @@ class Crew(BaseModel):
         default=False,
         description="Whether the crew should use memory to store memories of it's execution",
     )
+    short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
+        default=None,
+        description="An Instance of the ShortTermMemory to be used by the Crew",
+    )
+    long_term_memory: Optional[InstanceOf[LongTermMemory]] = Field(
+        default=None,
+        description="An Instance of the LongTermMemory to be used by the Crew",
+    )
+    entity_memory: Optional[InstanceOf[EntityMemory]] = Field(
+        default=None,
+        description="An Instance of the EntityMemory to be used by the Crew",
+    )
     embedder: Optional[dict] = Field(
         default={"provider": "openai"},
         description="Configuration for the embedder to be used for the crew.",
@@ -129,10 +137,6 @@ class Crew(BaseModel):
     manager_agent: Optional[BaseAgent] = Field(
         description="Custom agent that will be used as manager.", default=None
     )
-    manager_callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field(
-        default=None,
-        description="A list of callback handlers to be executed by the manager agent when hierarchical process is used",
-    )
     function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
@@ -208,6 +212,15 @@ class Crew(BaseModel):
         if self.output_log_file:
             self._file_handler = FileHandler(self.output_log_file)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
+        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)
+                )
         self._telemetry = Telemetry()
         self._telemetry.set_tracer()
         return self
@@ -216,11 +229,19 @@ class Crew(BaseModel):
     def create_crew_memory(self) -> "Crew":
         """Set private attributes."""
         if self.memory:
-            self._long_term_memory = LongTermMemory()
-            self._short_term_memory = ShortTermMemory(
-                crew=self, embedder_config=self.embedder
+            self._long_term_memory = (
+                self.long_term_memory if self.long_term_memory else LongTermMemory()
+            )
+            self._short_term_memory = (
+                self.short_term_memory
+                if self.short_term_memory
+                else ShortTermMemory(crew=self, embedder_config=self.embedder)
+            )
+            self._entity_memory = (
+                self.entity_memory
+                if self.entity_memory
+                else EntityMemory(crew=self, embedder_config=self.embedder)
             )
-            self._entity_memory = EntityMemory(crew=self, embedder_config=self.embedder)
         return self
 
     @model_validator(mode="after")
@@ -364,7 +385,7 @@ class Crew(BaseModel):
         source = [agent.key for agent in self.agents] + [
             task.key for task in self.tasks
         ]
-        return md5("|".join(source).encode()).hexdigest()
+        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
 
     def _setup_from_config(self):
         assert self.config is not None, "Config should not be None."
@@ -517,10 +538,6 @@ class Crew(BaseModel):
             asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
             for i in range(len(inputs))
         ]
-        tasks = [
-            asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
-            for i in range(len(inputs))
-        ]
 
         results = await asyncio.gather(*tasks)
 
@@ -541,7 +558,7 @@ class Crew(BaseModel):
         )._handle_crew_planning()
 
         for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
-            task.description += step_plan
+            task.description += step_plan.plan
 
     def _store_execution_log(
         self,
@@ -587,9 +604,18 @@ class Crew(BaseModel):
             self.manager_agent.allow_delegation = True
             manager = self.manager_agent
             if manager.tools is not None and len(manager.tools) > 0:
+                self._logger.log(
+                    "warning", "Manager agent should not have tools", color="orange"
+                )
+                manager.tools = []
                 raise Exception("Manager agent should not have tools")
             manager.tools = self.manager_agent.get_delegation_tools(self.agents)
         else:
+            self.manager_llm = (
+                getattr(self.manager_llm, "model_name", None)
+                or getattr(self.manager_llm, "deployment_name", None)
+                or self.manager_llm
+            )
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
                 goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
@@ -599,6 +625,7 @@ class Crew(BaseModel):
                 verbose=self.verbose,
             )
             self.manager_agent = manager
+        manager.crew = self
 
     def _execute_tasks(
         self,
@@ -743,9 +770,6 @@ class Crew(BaseModel):
                     task.tools.append(new_tool)
 
     def _log_task_start(self, task: Task, role: str = "None"):
-        color = self._logging_color
-        self._logger.log("debug", f"== Working Agent: {role}", color=color)
-        self._logger.log("info", f"== Starting Task: {task.description}", color=color)
         if self.output_log_file:
             self._file_handler.log(agent=role, task=task.description, status="started")
 
@@ -768,7 +792,6 @@ class Crew(BaseModel):
 
     def _process_task_result(self, task: Task, output: TaskOutput) -> None:
         role = task.agent.role if task.agent is not None else "None"
-        self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
         if self.output_log_file:
             self._file_handler.log(agent=role, task=output, status="completed")
 
@@ -921,29 +944,30 @@ class Crew(BaseModel):
     def calculate_usage_metrics(self) -> UsageMetrics:
         """Calculates and returns the usage metrics."""
         total_usage_metrics = UsageMetrics()
-
         for agent in self.agents:
             if hasattr(agent, "_token_process"):
                 token_sum = agent._token_process.get_summary()
                 total_usage_metrics.add_usage_metrics(token_sum)
-
         if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
             token_sum = self.manager_agent._token_process.get_summary()
             total_usage_metrics.add_usage_metrics(token_sum)
-
+        self.usage_metrics = total_usage_metrics
         return total_usage_metrics
 
     def test(
         self,
         n_iterations: int,
-        openai_model_name: str,
+        openai_model_name: Optional[str] = None,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations."""
+        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         self._test_execution_span = self._telemetry.test_execution_span(
-            self, n_iterations, inputs, openai_model_name
-        )
-        evaluator = CrewEvaluator(self, openai_model_name)
+            self,
+            n_iterations,
+            inputs,
+            openai_model_name,  # type: ignore[arg-type]
+        )  # type: ignore[arg-type]
+        evaluator = CrewEvaluator(self, openai_model_name)  # type: ignore[arg-type]
 
         for i in range(1, n_iterations + 1):
             evaluator.set_iteration(i)

src/crewai/crews/__init__.py

@@ -1 +1,3 @@
 from .crew_output import CrewOutput
+
+__all__ = ["CrewOutput"]

src/crewai/crews/crew_output.py

@@ -41,6 +41,14 @@ class CrewOutput(BaseModel):
             output_dict.update(self.pydantic.model_dump())
         return output_dict
 
+    def __getitem__(self, key):
+        if self.pydantic and hasattr(self.pydantic, key):
+            return getattr(self.pydantic, key)
+        elif self.json_dict and key in self.json_dict:
+            return self.json_dict[key]
+        else:
+            raise KeyError(f"Key '{key}' not found in CrewOutput.")
+
     def __str__(self):
         if self.pydantic:
             return str(self.pydantic)

src/crewai/flow/__init__.py

@@ -0,0 +1,3 @@
+from crewai.flow.flow import Flow
+
+__all__ = ["Flow"]

src/crewai/flow/assets/crewai_flow_visual_template.html

@@ -0,0 +1,93 @@
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <title>{{ title }}</title>
+    <script
+      src="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/vis-network.min.js"
+      integrity="sha512-LnvoEWDFrqGHlHmDD2101OrLcbsfkrzoSpvtSQtxK3RMnRV0eOkhhBN2dXHKRrUU8p2DGRTk35n4O8nWSVe1mQ=="
+      crossorigin="anonymous"
+      referrerpolicy="no-referrer"
+    ></script>
+    <link
+      rel="stylesheet"
+      href="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/dist/vis-network.min.css"
+      integrity="sha512-WgxfT5LWjfszlPHXRmBWHkV2eceiWTOBvrKCNbdgDYTHrT2AeLCGbF4sZlZw3UMN3WtL0tGUoIAKsu8mllg/XA=="
+      crossorigin="anonymous"
+      referrerpolicy="no-referrer"
+    />
+    <style type="text/css">
+      body {
+        font-family: verdana;
+        margin: 0;
+        padding: 0;
+      }
+      .container {
+        display: flex;
+        flex-direction: column;
+        height: 100vh;
+      }
+      #mynetwork {
+        flex-grow: 1;
+        width: 100%;
+        height: 750px;
+        background-color: #ffffff;
+      }
+      .card {
+        border: none;
+      }
+      .legend-container {
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        padding: 10px;
+        background-color: #f8f9fa;
+        position: fixed; /* Make the legend fixed */
+        bottom: 0; /* Position it at the bottom */
+        width: 100%; /* Make it span the full width */
+      }
+      .legend-item {
+        display: flex;
+        align-items: center;
+        margin-right: 20px;
+      }
+      .legend-color-box {
+        width: 20px;
+        height: 20px;
+        margin-right: 5px;
+      }
+      .logo {
+        height: 50px;
+        margin-right: 20px;
+      }
+      .legend-dashed {
+        border-bottom: 2px dashed #666666;
+        width: 20px;
+        height: 0;
+        margin-right: 5px;
+      }
+      .legend-solid {
+        border-bottom: 2px solid #666666;
+        width: 20px;
+        height: 0;
+        margin-right: 5px;
+      }
+    </style>
+  </head>
+  <body>
+    <div class="container">
+      <div class="card" style="width: 100%">
+        <div id="mynetwork" class="card-body"></div>
+      </div>
+      <div class="legend-container">
+        <img
+          src="data:image/svg+xml;base64,{{ logo_svg_base64 }}"
+          alt="CrewAI logo"
+          class="logo"
+        />
+        <!-- LEGEND_ITEMS_PLACEHOLDER -->
+      </div>
+    </div>
+    {{ network_content }}
+  </body>
+</html>