docs: fix references to annotations

.github/ISSUE_TEMPLATE/bug_report.md

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

.github/ISSUE_TEMPLATE/bug_report.yml

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

.github/ISSUE_TEMPLATE/config.yml

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

.github/ISSUE_TEMPLATE/custom.md

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

.github/ISSUE_TEMPLATE/feature_request.yml

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

.github/workflows/mkdocs.yml

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

.github/workflows/security-checker.yml

@@ -1,23 +0,0 @@
-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

@@ -24,4 +24,3 @@ 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

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

README.md

@@ -8,11 +8,11 @@
 
 <h3>
 
-[Homepage](https://www.crewai.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)
+[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)
 
 </h3>
 
-[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/crewAIInc/crewAI)
+[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/joaomdmoura/crewAI)
 [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 
 </div>
@@ -64,8 +64,24 @@ 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(
@@ -78,7 +94,7 @@ 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=[SerperDevTool()]
+  tools=[search_tool]
 )
 writer = Agent(
   role='Tech Content Strategist',
@@ -137,12 +153,12 @@ 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/crewAIInc/crewAI-examples?tab=readme-ov-file):
+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):
 
-- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
+- [Landing Page Generator](https://github.com/joaomdmoura/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/crewAIInc/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
+- [Trip Planner](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
+- [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
 
 ### Quick Tutorial
 
@@ -150,19 +166,19 @@ 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/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/joaomdmoura/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/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/joaomdmoura/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/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/joaomdmoura/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")
 
@@ -174,12 +190,13 @@ 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:
@@ -267,39 +284,3 @@ 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

@@ -17,7 +17,7 @@ description: What are crewAI Agents and how to use them.
 ## 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.                                                                                                                                           |
@@ -28,17 +28,14 @@ description: What are crewAI Agents and how to use them.
 | **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
 | **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
 | **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
-| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.
+| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
 | **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
 | **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
 | **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
-| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
-| **Use Stop Words** *(optional)*     | `use_stop_words`  | Adds the ability to not use stop words (to support o1 models). Default is `True`.                                                                                                                                                                  |
-| **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`.                                                                                                                                                                  |
+| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.                                                                                                                                                                  |
 
 ## Creating an Agent
 
@@ -66,7 +63,7 @@ agent = Agent(
   max_rpm=None, # Optional
   max_execution_time=None, # Optional
   verbose=True,  # Optional
-  allow_delegation=False,  # Optional
+  allow_delegation=True,  # Optional
   step_callback=my_intermediate_step_callback,  # Optional
   cache=True,  # Optional
   system_template=my_system_template,  # Optional
@@ -77,11 +74,8 @@ agent = Agent(
   tools_handler=my_tools_handler,  # Optional
   cache_handler=my_cache_handler,  # Optional
   callbacks=[callback1, callback2],  # Optional
-  allow_code_execution=True,  # Optional
+  allow_code_execution=True,  # Optiona
   max_retry_limit=2,  # Optional
-  use_stop_words=True,  # Optional
-  use_system_prompt=True,  # Optional
-  respect_context_window=True,  # Optional
 )
 ```
 
@@ -111,7 +105,7 @@ 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

@@ -1,142 +0,0 @@
-# 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's execution.
+- **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew execution.
 - **Planning Mode (`planning`)**: Allows crews to plan their actions before executing tasks by setting `planning=True` when creating the `Crew` instance. This feature enhances coordination and efficiency.
 - **Replay Feature**: Introduces a new CLI for listing tasks from the last run and replaying from a specific task, enhancing task management and troubleshooting.
 

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. Default is `sequential`.                                                                                                                                                              |
-| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution. Defaults to `False`.                                                                                                                                                                                    |
+| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows.                                                                                                                                                                                       |
+| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution.                                                                                                                                                                                                         |
 | **Manager LLM** _(optional)_          | `manager_llm`          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.**                                                                                                                                   |
 | **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
 | **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
-| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
+| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution.                                                                                                                                                                                         |
 | **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
 | **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
-| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory). 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`.                                                                                                                                       |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution.                                                                                                                                                                             |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                                                                                                                                                                     |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output.                                                                                                                                                           |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
@@ -38,6 +38,65 @@ 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

@@ -1,155 +0,0 @@
-# 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

@@ -4,17 +4,16 @@ 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 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. |
+| :------------------- | :----------------------------------------------------------- |
+| **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. |
 | **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
@@ -28,12 +27,12 @@ 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. It's also possible to initialize the memory instance with your own instance.
+By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
 
-The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using 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.
+The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.
+The **Long-Term Memory** uses SQLLite3 to store task results.  Currently, there is no way to override these storage implementations.
+The data storage files are saved into a platform specific location found using the appdirs package
+and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
 
 ### Example: Configuring Memory for a Crew
 
@@ -50,45 +49,6 @@ 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)

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).
-- **Kickoff**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
+- **Run**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
 - **Branch**: Parallel executions within a stage (e.g., concurrent crew operations).
 - **Trace**: The journey of an individual input through the entire pipeline, capturing the path and transformations it undergoes.
 
@@ -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 kickoff, flowing through all stages of the pipeline. Multiple kickoffs can be processed concurrently, each following the defined pipeline structure.
+Each input creates its own run, flowing through all stages of the pipeline. Multiple runs can be processed concurrently, each following the defined pipeline structure.
 
 ## Pipeline Attributes
 
 | Attribute  | Parameters | Description                                                                           |
-| :--------- | :---------- | :----------------------------------------------------------------------------------------------------------------- |
-| **Stages** | `stages`   | A list of `PipelineStage` (crews, lists of crews, or routers) representing the stages to be executed in sequence. |
+| :--------- | :--------- | :------------------------------------------------------------------------------------ |
+| **Stages** | `stages`   | A list of crews, lists of crews, or routers representing the stages to be executed in sequence. |
 
 ## Creating a Pipeline
 
@@ -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, Process, Pipeline
+from crewai import Crew, Agent, Task, Pipeline
 
 # Define your crews
 research_crew = Crew(
@@ -74,8 +74,7 @@ my_pipeline = Pipeline(
 
 | Method           | Description                                                                                                                                                                    |
 | :--------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **kickoff**      | Executes the pipeline, processing all stages and returning the results. This method initiates one or more kickoffs through the pipeline, handling the flow of data between stages. |
-| **process_runs** | Runs the pipeline for each input provided, handling the flow and transformation of data between stages. |
+| **process_runs** | Executes the pipeline, processing all stages and returning the results. This method initiates one or more runs through the pipeline, handling the flow of data between stages. |
 
 ## Pipeline Output
 
@@ -100,12 +99,12 @@ 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 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.  |
+| **Raw**           | `raw`           | `str`                      | The raw output of the final stage in the pipeline run.                                        |
+| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the final stage, if applicable. |
+| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the final stage, if applicable.                  |
+| **Token Usage**   | `token_usage`   | `Dict[str, Any]`           | A summary of token usage across all stages of the pipeline run.                               |
+| **Trace**         | `trace`         | `List[Any]`                | A trace of the journey of inputs through the pipeline run.                                    |
+| **Crews Outputs** | `crews_outputs` | `List[CrewOutput]`         | A list of `CrewOutput` objects, representing the outputs from each crew in the pipeline run.  |
 
 ### Pipeline Run Result Methods and Properties
 
@@ -113,7 +112,7 @@ 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
 
@@ -240,7 +239,7 @@ email_router = Router(
             pipeline=normal_pipeline
         )
     },
-    default=Pipeline(stages=[normal_pipeline])  # Default to just normal if no urgency score
+    default=Pipeline(stages=[normal_pipeline])  # Default to just classification if no urgency score
 )
 
 # Use the router in a main pipeline
@@ -248,7 +247,7 @@ main_pipeline = Pipeline(stages=[classification_crew, email_router])
 
 inputs = [{"email": "..."}, {"email": "..."}]  # List of email data
 
-main_pipeline.kickoff(inputs=inputs=inputs)
+main_pipeline.kickoff(inputs=inputs)
 ```
 
 In this example, the router decides between an urgent pipeline and a normal pipeline based on the urgency score of the email. If the urgency score is greater than 7, it routes to the urgent pipeline; otherwise, it uses the normal pipeline. If the input doesn't include an urgency score, it defaults to just the classification crew.
@@ -262,7 +261,7 @@ 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 fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'
+**Task Expected Output:** A fully fledge report with the main topics, each with a full section of information. Formatted as markdown without '```'
 
 **Task Tools:** None specified
 
@@ -130,4 +130,5 @@ 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        | 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.                                                |
+| Attribute                        | Parameters        | Description                                                                                                          |
+| :------------------------------- | :---------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Description**                  | `description`     | A clear, concise statement of what the task entails.                                                                 |
+| **Agent**                        | `agent`           | The agent responsible for the task, assigned either directly or by the crew's process.                               |
+| **Expected Output**              | `expected_output` | A detailed description of what the task's completion looks like.                                                     |
+| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list.                  |
+| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False.    |
+| **Context** _(optional)_         | `context`         | Specifies tasks whose outputs are used as context for this task.                                                     |
+| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
+| **Output JSON** _(optional)_     | `output_json`     | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
+| **Output Pydantic** _(optional)_ | `output_pydantic` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
+| **Output File** _(optional)_     | `output_file`     | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
+| **Output** _(optional)_          | `output`          | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details.                  |
+| **Callback** _(optional)_        | `callback`        | A callable that is executed with the task's output upon completion.                                                  |
+| **Human Input** _(optional)_     | `human_input`     | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. Defaults to False.|
+| **Converter Class** _(optional)_ | `converter_cls`   | A converter class used to export structured output. Defaults to None.                                                |
 
 ## Creating a Task
 
@@ -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 output, 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 strings, JSON, and Pydantic models.
 By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
 
 ### Task Output Attributes
 
 | Attribute         | Parameters      | Type                       | Description                                                                                        |
 | :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
-| **Description**   | `description`   | `str`                      | Description of the task.                                                                           |
-| **Summary**       | `summary`       | `Optional[str]`            | Summary of the task, auto-generated from the first 10 words of the description.                    |
+| **Description**   | `description`   | `str`                      | A brief description of the task.                                                                   |
+| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the first 10 words of the description.            |
 | **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
 | **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
 | **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
 | **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
 | **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
 
-### Task Methods and Properties
+### Task Output Methods and Properties
 
 | Method/Property | Description                                                                                       |
 | :-------------- | :------------------------------------------------------------------------------------------------ |
 | **json**        | Returns the JSON string representation of the task output if the output format is JSON.           |
 | **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
-| **str**         | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
+| \***\*str\*\*** | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
 
 ### Accessing Task Outputs
 
@@ -131,7 +131,6 @@ 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(
@@ -234,7 +233,7 @@ def callback_function(output: TaskOutput):
     print(f"""
         Task completed!
         Task: {output.description}
-        Output: {output.raw}
+        Output: {output.raw_output}
     """)
 
 research_task = Task(
@@ -275,7 +274,7 @@ result = crew.kickoff()
 print(f"""
     Task completed!
     Task: {task1.output.description}
-    Output: {task1.output.raw}
+    Output: {task1.output.raw_output}
 """)
 ```
 

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
@@ -21,36 +21,20 @@ 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:
 
 ```
-                                                     Tasks Scores
+                Task 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     │                                │                                 ┃
-┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━┷━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
+┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
+┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
+│ Task 1     │ 10.0  │ 9.0   │ 9.5        │
+│ Task 2     │ 9.0   │ 9.0   │ 9.0        │
+│ Crew       │ 9.5   │ 9.0   │ 9.2        │
+└────────────┴───────┴───────┴────────────┘
 ```
 
 The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.

docs/core-concepts/Tools.md

@@ -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.     |
@@ -144,7 +144,6 @@ 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> (optional)
+crewai train -n <n_iterations> <filename>
 ```
 
 !!! note "Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`."

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

@@ -5,10 +5,9 @@ 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 [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with crewAI.
+    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/)
 
 ```python
-import os
 from crewai import Agent
 from langchain.agents import Tool
 from langchain.utilities import GoogleSerperAPIWrapper

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

@@ -54,4 +54,4 @@ To effectively use the LlamaIndexTool, follow these steps:
     pip install 'crewai[tools]'
     ```
 
-2. **Install and Use LlamaIndex**: Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
+2. **Install and Use LlamaIndex**: Follow 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

@@ -1,163 +0,0 @@
-# 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

@@ -1,7 +1,5 @@
 ---
-
 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.
 ---
 
@@ -19,13 +17,40 @@ 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[tools]'
+$ pip install crewai 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.
 
-In this example, we will be using poetry as our virtual environment manager.
-
+To set up a virtual environment, run the following CLI command:
 To create a new CrewAI project, run the following CLI command:
 
 ```shell
@@ -98,13 +123,10 @@ 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 `tasks.yaml` file. Ensure your annotated agent and function name are the same; otherwise, your task won't recognize the reference properly.
+Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from task.yaml file. Ensure your annotated agent and function name is the same otherwise your task won't recognize the reference properly.
 
 #### Example References
-
-`agents.yaml`
-
+agent.yaml
 ```yaml
 email_summarizer:
     role: >
@@ -116,8 +138,7 @@ email_summarizer:
     llm: mixtal_llm
 ```
 
-`tasks.yaml`
-
+task.yaml
 ```yaml
 email_summarizer_task:
     description: >
@@ -130,29 +151,32 @@ 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)
 
-* `@agent`
-* `@task`
-* `@crew`
-* `@tool`
-* `@callback`
-* `@output_json`
-* `@output_pydantic`
-* `@cache_handler`
-
-`crew.py`
-
-```python
+crew.py
+```py
 # ...
+    @llm
+    def mixtal_llm(self):
+        return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
+
     @agent
     def email_summarizer(self) -> Agent:
         return Agent(
             config=self.agents_config["email_summarizer"],
         )
-
+    ## ...other tasks defined
     @task
     def email_summarizer_task(self) -> Task:
         return Task(
@@ -167,7 +191,8 @@ To install the dependencies for your project, you can use Poetry. First, navigat
 
 ```shell
 $ cd my_project
-$ crewai install
+$ poetry lock
+$ poetry install
 ```
 
 This will install the dependencies specified in the `pyproject.toml` file.
@@ -176,7 +201,7 @@ 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.
 
-#### tasks.yaml
+#### agents.yaml
 
 ```yaml
 research_task:
@@ -208,6 +233,10 @@ 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, the task will be skipped, if true, then execute the task.
+# if false task will be skipped, true, then execute task
 def is_data_missing(output: TaskOutput) -> bool:
     return len(output.pydantic.events) < 10  # this will skip this task
 
@@ -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="A complete report on the customer and their customers and competitors, including their demographics, preferences, market positioning and audience engagement.",
+    expected_output="summary_generated",
     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
+    planning=True  # Enable planning feature
 )
 
 # Run the crew

docs/how-to/Customizing-Agents.md

@@ -14,16 +14,12 @@ 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. 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.
+- **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.
 - **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 Stop Words** *(Optional)*: `use_stop_words` attribute controls whether the agent will use stop words during task execution. This is now supported to aid o1 models.
-- **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.
@@ -71,11 +67,12 @@ 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 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.
+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.
 
 ### Example: Disabling Delegation for an Agent
 ```python
@@ -83,7 +80,7 @@ agent = Agent(
   role='Content Writer',
   goal='Write engaging content on market trends',
   backstory='A seasoned writer with expertise in market analysis.',
-  allow_delegation=True # Enabling delegation
+  allow_delegation=False # Disabling delegation
 )
 ```
 

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

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

@@ -16,13 +16,6 @@ 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.
@@ -45,10 +38,6 @@ 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
-    use_stop_words=True,  # Enable or disable stop words 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',
@@ -57,10 +46,6 @@ 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
-    use_stop_words=True,  # Enable or disable stop words 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
@@ -69,7 +54,6 @@ 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

@@ -74,8 +74,7 @@ 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,
-    human_input=True
+    agent=writer
 )
 
 # Instantiate your crew with a sequential process

docs/how-to/Kickoff-async.md

@@ -4,11 +4,9 @@ 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
@@ -25,20 +23,10 @@ def kickoff_async(self, inputs: dict) -> CrewOutput:
 
 - `CrewOutput`: An object representing the result of the crew execution.
 
-## Potential Use Cases
-
-- **Parallel Content Generation**: Kickoff multiple independent crews asynchronously, each responsible for generating content on different topics. For example, one crew might research and draft an article on AI trends, while another crew generates social media posts about a new product launch. Each crew operates independently, allowing content production to scale efficiently.
-
-- **Concurrent Market Research Tasks**: Launch multiple crews asynchronously to conduct market research in parallel. One crew might analyze industry trends, while another examines competitor strategies, and yet another evaluates consumer sentiment. Each crew independently completes its task, enabling faster and more comprehensive insights.
-
-- **Independent Travel Planning Modules**: Execute separate crews to independently plan different aspects of a trip. One crew might handle flight options, another handles accommodation, and a third plans activities. Each crew works asynchronously, allowing various components of the trip to be planned simultaneously and independently for faster results.
-
-## Example: Single Asynchronous Crew Execution
-
-Here's an example of how to kickoff a crew asynchronously using asyncio and awaiting the result:
+## Example
+Here's an example of how to kickoff a crew asynchronously:
 
 ```python
-import asyncio
 from crewai import Crew, Agent, Task
 
 # Create an agent with code execution enabled
@@ -61,57 +49,6 @@ analysis_crew = Crew(
     tasks=[data_analysis_task]
 )
 
-# Async function to kickoff the crew asynchronously
-async def async_crew_execution():
-    result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
-    print("Crew Result:", result)
-
-# Run the async function
-asyncio.run(async_crew_execution())
-```
-
-## Example: Multiple Asynchronous Crew Executions
-
-In this example, we'll show how to kickoff multiple crews asynchronously and wait for all of them to complete using `asyncio.gather()`:
-
-```python
-import asyncio
-from crewai import Crew, Agent, Task
-
-# Create an agent with code execution enabled
-coding_agent = Agent(
-    role="Python Data Analyst",
-    goal="Analyze data and provide insights using Python",
-    backstory="You are an experienced data analyst with strong Python skills.",
-    allow_code_execution=True
-)
-
-# Create tasks that require code execution
-task_1 = Task(
-    description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
-    agent=coding_agent
-)
-
-task_2 = Task(
-    description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
-    agent=coding_agent
-)
-
-# Create two crews and add tasks
-crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
-crew_2 = Crew(agents=[coding_agent], tasks=[task_2])
-
-# Async function to kickoff multiple crews asynchronously and wait for all to finish
-async def async_multiple_crews():
-    result_1 = crew_1.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
-    result_2 = crew_2.kickoff_async(inputs={"ages": [20, 22, 24, 28, 30]})
-
-    # Wait for both crews to finish
-    results = await asyncio.gather(result_1, result_2)
-
-    for i, result in enumerate(results, 1):
-        print(f"Crew {i} Result:", result)
-
-# Run the async function
-asyncio.run(async_multiple_crews())
+# Execute the crew asynchronously
+result = analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
 ```

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

@@ -25,17 +25,13 @@ 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,
-    expected_output="The average age calculated from the dataset"
+    agent=coding_agent
 )
 
 # Create a crew and add the task
 analysis_crew = Crew(
     agents=[coding_agent],
-    tasks=[data_analysis_task],
-    verbose=True,
-    memory=False,
-    respect_context_window=True  # enable by default
+    tasks=[data_analysis_task]
 )
 
 datasets = [

docs/how-to/LLM-Connections.md

@@ -1,163 +1,197 @@
 ---
 title: Connect CrewAI to LLMs
-description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs) using LiteLLM, including supported providers and configuration options.
+description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs), including detailed class attributes, methods, 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 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.
+    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.
 
-## Supported Providers
+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.
 
-LiteLLM supports a wide range of providers, including but not limited to:
+The platform supports connections to an array of Generative AI models, including:
 
-- 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!
+ - 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
 
-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:
+This broad spectrum of LLM options enables users to select the most suitable model for their specific needs, whether prioritizing local deployment, specialized capabilities, or cloud-based scalability.
 
+## Changing the default LLM
+The default LLM is provided through the `langchain openai` package, which is installed by default when you install CrewAI. You can change this default LLM to a different model or API by setting the `OPENAI_MODEL_NAME` environment variable. This straightforward process allows you to harness the power of different OpenAI models, enhancing the flexibility and capabilities of your CrewAI implementation.
 ```python
-from crewai import Agent
+# Required
+os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
 
-# 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'
+# 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
 )
 ```
 
-### 2. Using the LLM Class
+## 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.
 
-For more detailed configuration, use 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
+```
 
+## 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 crewai import Agent, LLM
+from langchain_huggingface import HuggingFaceEndpoint,
 
-llm = LLM(
-    model="gpt-4",
-    temperature=0.7,
-    base_url="https://api.openai.com/v1",
-    api_key="your-api-key-here"
+llm = HuggingFaceEndpoint(
+    repo_id="microsoft/Phi-3-mini-4k-instruct",
+    task="text-generation",
+    max_new_tokens=512,
+    do_sample=False,
+    repetition_penalty=1.03,
 )
 
 agent = Agent(
-    role='Customized LLM Expert',
-    goal='Provide tailored responses',
-    backstory="An AI assistant with custom LLM settings.",
+    role="HuggingFace Agent",
+    goal="Generate text using HuggingFace",
+    backstory="A diligent explorer of GitHub docs.",
     llm=llm
 )
 ```
 
-## Configuration Options
+## OpenAI Compatible API Endpoints
+Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, Groq, and Mistral AI.
 
-When configuring an LLM for your agent, you have access to a wide range of parameters:
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `model` | str | The name of the model to use (e.g., "gpt-4", "claude-2") |
-| `temperature` | float | Controls randomness in output (0.0 to 1.0) |
-| `max_tokens` | int | Maximum number of tokens to generate |
-| `top_p` | float | Controls diversity of output (0.0 to 1.0) |
-| `frequency_penalty` | float | Penalizes new tokens based on their frequency in the text so far |
-| `presence_penalty` | float | Penalizes new tokens based on their presence in the text so far |
-| `stop` | str, List[str] | Sequence(s) to stop generation |
-| `base_url` | str | The base URL for the API endpoint |
-| `api_key` | str | Your API key for authentication |
-
-For a complete list of parameters and their descriptions, refer to the LLM class documentation.
-
-## Connecting to OpenAI-Compatible LLMs
-
-You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
-
-### Using Environment Variables
-
-```python
-import os
-
-os.environ["OPENAI_API_KEY"] = "your-api-key"
-os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
-os.environ["OPENAI_MODEL_NAME"] = "your-model-name"
+### 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
 ```
 
-### Using LLM Class Attributes
+#### 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"
+```
 
+#### 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
+```
+
+#### 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"
+```
+
+### 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"
+```
+
+# Free developer API key available here: https://console.upstage.ai/services/solar
+# Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
+
+
+### Cohere
 ```python
-llm = LLM(
-    model="custom-model-name",
-    api_key="your-api-key",
-    base_url="https://api.your-provider.com/v1"
+from langchain_cohere import ChatCohere
+# Initialize language model
+os.environ["COHERE_API_KEY"] = "your-cohere-api-key"
+llm = ChatCohere()
+
+# Free developer API key available here: https://cohere.com/
+# Langchain Documentation: https://python.langchain.com/docs/integrations/chat/cohere
+```
+
+### Azure Open AI Configuration
+For Azure OpenAI API integration, set the following environment variables:
+```sh
+
+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")
 )
-agent = Agent(llm=llm, ...)
-```
 
-## Using Local Models with Ollama
-
-For local models like those provided by Ollama:
-
-1. [Download and install Ollama](https://ollama.com/download)
-2. Pull the desired model (e.g., `ollama pull llama2`)
-3. Configure your agent:
-
-```python
-agent = Agent(
-    role='Local AI Expert',
-    goal='Process information using a local model',
-    backstory="An AI assistant running on local hardware.",
-    llm=LLM(model="ollama/llama2", base_url="http://localhost:11434")
+azure_agent = Agent(
+  role='Example Agent',
+  goal='Demonstrate custom LLM configuration',
+  backstory='A diligent explorer of GitHub docs.',
+  llm=azure_llm
 )
 ```
 
-## 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
-
-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.
+Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.

docs/how-to/Langtrace-Observability.md

@@ -7,14 +7,10 @@ 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, set the project type to crewAI & generate an API key.
+2. Create a project and generate an API key.
 3. Install Langtrace in your CrewAI project using the following commands:
 
 ```bash
@@ -36,29 +32,58 @@ 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

@@ -1,7 +1,6 @@
 ---
 title: Replay Tasks from Latest Crew Kickoff
 description: Replay tasks from the latest crew.kickoff(...)
-
 ---
 
 ## Introduction
@@ -17,24 +16,22 @@ To use the replay feature, follow these steps:
 
 1. Open your terminal or command prompt.
 2. Navigate to the directory where your CrewAI project is located.
-3. Run the following commands:
-
+3. Run the following command:
 To view the latest kickoff task_ids use:
 ```shell
 crewai log-tasks-outputs
 ```
 
-Once you have your `task_id` to replay, use:
+Once you have your task_id to replay from 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
@@ -53,6 +50,3 @@ 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

@@ -52,17 +52,14 @@ 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 directives guiding their execution.
+2. **Subsequent Tasks**: Agents pick up their tasks based on the process type, with outcomes of preceding tasks or manager directives guiding their execution.
 3. **Completion**: The process concludes once the final task is executed, leading to project completion.
 
 ## Advanced Features
@@ -91,5 +88,3 @@ CrewAI tracks token usage across all tasks and agents. You can access these metr
 2. **Clear Task Descriptions**: Provide detailed descriptions for each task to guide the agents effectively.
 3. **Appropriate Agent Selection**: Match agents' skills and roles to the requirements of each task.
 4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones.
-
-This updated documentation ensures that details accurately reflect the latest changes in the codebase and clearly describes how to leverage new features and configurations. The content is kept simple and direct to ensure easy understanding.

docs/index.md

@@ -8,21 +8,14 @@ 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>
             </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>
             </li>
-            <li>
-                <a href='./getting-started/Create-a-New-CrewAI-Pipeline-Template-Method'>
-                    Create a New CrewAI Pipeline: Template Method
-                </a>
-            </li>
         </ul>
     </div>
     <div style="width:25%">
@@ -53,11 +46,6 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Crews
                 </a>
             </li>
-            <li>
-                <a href="./core-concepts/LLMs">
-                    LLMs
-                </a>
-            </li>
             <li>
                 <a href="./core-concepts/Pipeline">
                     Pipeline

docs/telemetry/Telemetry.md

@@ -5,39 +5,24 @@ description: Understanding the telemetry data collected by CrewAI and how it con
 
 ## Telemetry
 
-!!! note "Personal Information"
-    By default, we collect no data that would be considered personal information under GDPR and other privacy regulations.
-    We do collect Tool's names and Agent's roles, so be advised not to include any personal information in the tool's names or the Agent's roles.
-		Because no personal information is collected, it's not necessary to worry about data residency.
-		When `share_crew` is enabled, additional data is collected which may contain personal information if included by the user. Users should exercise caution when enabling this feature to ensure compliance with privacy regulations.
+CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users. We don't offer a way to disable it now, but we will in the future.
 
-CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
+It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy.
 
-It's pivotal to understand that by default, **NO personal data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables.
-When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights. This expanded data collection may include personal information if users have incorporated it into their crews or tasks. Users should carefully consider the content of their crews and tasks before enabling `share_crew`. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true.
-
-### Data 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`.
+### 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.
 
 ### 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.
+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.
 
-!!! 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.
+### 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.

docs/tools/SpiderTool.md

@@ -1,81 +0,0 @@
-# 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/crewAIInc/crewAI
-site_url: https://docs.crewai.com
+repo_url: https://github.com/joaomdmoura/crewai/
+site_url: https://crewai.com
 edit_uri: edit/main/docs/
 copyright: Copyright © 2024 crewAI, Inc
 
@@ -78,14 +78,14 @@ theme:
 
   palette:
     - scheme: default
-      primary: deep orange
-      accent: deep orange
+      primary: red
+      accent: red
       toggle:
         icon: material/brightness-7
         name: Switch to dark mode
     - scheme: slate
-      primary: deep orange
-      accent: deep orange
+      primary: red
+      accent: red
       toggle:
         icon: material/brightness-4
         name: Switch to light mode
@@ -129,7 +129,6 @@ 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'
@@ -178,7 +177,6 @@ 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'

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.63.1"
+version = "0.51.1"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
@@ -8,20 +8,20 @@ packages = [{ include = "crewai", from = "src" }]
 
 [tool.poetry.urls]
 Homepage = "https://crewai.com"
-Documentation = "https://docs.crewai.com"
-Repository = "https://github.com/crewAIInc/crewAI"
+Documentation = "https://github.com/joaomdmoura/CrewAI/wiki/Index"
+Repository = "https://github.com/joaomdmoura/crewai"
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
 pydantic = "^2.4.2"
-langchain = "^0.2.16"
+langchain = ">0.2,<=0.3"
 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 = "^2024.9.11"
-crewai-tools = { version = "^0.12.1", optional = true }
+regex = "^2023.12.25"
+crewai-tools = { version = "^0.8.3", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -29,9 +29,6 @@ jsonref = "^1.1.0"
 agentops = { version = "^0.3.0", optional = true }
 embedchain = "^0.1.114"
 json-repair = "^0.25.2"
-auth0-python = "^4.7.1"
-poetry = "^1.8.3"
-litellm = "^1.44.22"
 
 [tool.poetry.extras]
 tools = ["crewai-tools"]
@@ -49,7 +46,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.12.1"
+crewai-tools = "^0.8.3"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"
@@ -65,9 +62,6 @@ 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

@@ -1,18 +1,7 @@
-import warnings
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.pipeline import Pipeline
 from crewai.process import Process
-from crewai.routers import Router
 from crewai.task import Task
-from crewai.llm import LLM
 
-
-warnings.filterwarnings(
-    "ignore",
-    message="Pydantic serializer warnings:",
-    category=UserWarning,
-    module="pydantic.main",
-)
-
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM"]
+__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline"]

src/crewai/agent.py

@@ -1,18 +1,23 @@
 import os
 from inspect import signature
-from typing import Any, List, Optional, Union
+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 pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
-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 import CacheHandler, CrewAgentExecutor, CrewAgentParser
 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.training_handler import CrewTrainingHandler
 from crewai.utilities.token_counter_callback import TokenCalcHandler
-from crewai.llm import LLM
+from crewai.utilities.training_handler import CrewTrainingHandler
 
 
 def mock_agent_ops_provider():
@@ -29,6 +34,7 @@ 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()
@@ -58,6 +64,7 @@ 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)
@@ -74,20 +81,18 @@ class Agent(BaseAgent):
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    use_stop_words: bool = Field(
-        default=True,
-        description="Use stop words for 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
+    llm: Any = Field(
+        default_factory=lambda: ChatOpenAI(
+            model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4o")
+        ),
+        description="Language model that will run the agent.",
     )
     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."
     )
@@ -103,85 +108,44 @@ 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=15,
-        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 post_init_setup(self):
-        self.agent_ops_agent_name = self.role
+    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)
 
-        # 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}
+            # Ensure self.llm.callbacks is a list
+            if not isinstance(self.llm.callbacks, list):
+                self.llm.callbacks = []
 
-            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
-                "OPENAI_BASE_URL"
-            )
-            if api_base:
-                llm_params["base_url"] = api_base
+            # 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_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 agentops and not any(
+                isinstance(handler, agentops.LangchainCallbackHandler)
+                for handler in self.llm.callbacks
+            ):
+                agentops.stop_instrumenting()
+                self.llm.callbacks.append(agentops.LangchainCallbackHandler())
 
         if not self.agent_executor:
-            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)
+        return self
 
     def execute_task(
         self,
@@ -220,7 +184,15 @@ class Agent(BaseAgent):
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
         tools = tools or self.tools or []
-        self.create_agent_executor(tools=tools, task=task)
+        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)
 
         if self.crew and self.crew._train:
             task_prompt = self._training_handler(task_prompt=task_prompt)
@@ -233,7 +205,6 @@ 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:
@@ -242,7 +213,7 @@ class Agent(BaseAgent):
                 raise e
             result = self.execute_task(task, context, tools)
 
-        if self.max_rpm and self._rpm_controller:
+        if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
 
         # If there was any tool in self.tools_results that had result_as_answer
@@ -254,25 +225,73 @@ class Agent(BaseAgent):
 
         return result
 
-    def create_agent_executor(self, tools=None, task=None) -> None:
+    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:
         """Create an agent executor for the agent.
 
         Returns:
             An instance of the CrewAgentExecutor class.
         """
         tools = tools or self.tools or []
-        parsed_tools = self._parse_tools(tools)
+
+        agent_args = {
+            "input": lambda x: x["input"],
+            "tools": lambda x: x["tools"],
+            "tool_names": lambda x: x["tool_names"],
+            "agent_scratchpad": lambda x: self.format_log_to_str(
+                x["intermediate_steps"]
+            ),
+        }
+
+        executor_args = {
+            "llm": self.llm,
+            "i18n": self.i18n,
+            "crew": self.crew,
+            "crew_agent": self,
+            "tools": self._parse_tools(tools),
+            "verbose": self.verbose,
+            "original_tools": tools,
+            "handle_parsing_errors": True,
+            "max_iterations": self.max_iter,
+            "max_execution_time": self.max_execution_time,
+            "step_callback": self.step_callback,
+            "tools_handler": self.tools_handler,
+            "function_calling_llm": self.function_calling_llm,
+            "callbacks": self.callbacks,
+            "max_tokens": self.max_tokens,
+        }
+
+        if self._rpm_controller:
+            executor_args["request_within_rpm_limit"] = (
+                self._rpm_controller.check_or_wait
+            )
 
         prompt = Prompts(
-            agent=self,
-            tools=tools,
             i18n=self.i18n,
-            use_system_prompt=self.use_system_prompt,
+            tools=tools,
             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:
@@ -280,27 +299,11 @@ 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(
-            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,
-            use_stop_words=self.use_stop_words,
-            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)],
+            agent=RunnableAgent(runnable=inner_agent), **executor_args
         )
 
     def get_delegation_tools(self, agents: List[BaseAgent]):
@@ -321,7 +324,7 @@ 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[Any]:  # type: ignore
+    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:  # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
         """Parse tools to be used for the task."""
         tools_list = []
         try:
@@ -364,7 +367,7 @@ class Agent(BaseAgent):
                 )
         return task_prompt
 
-    def _render_text_description(self, tools: List[Any]) -> str:
+    def _render_text_description(self, tools: List[BaseTool]) -> str:
         """Render the tool name and description in plain text.
 
         Output will be in the format of:
@@ -383,7 +386,7 @@ class Agent(BaseAgent):
 
         return description
 
-    def _render_text_description_and_args(self, tools: List[Any]) -> str:
+    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
         """Render the tool name, description, and args in plain text.
 
         Output will be in the format of:

src/crewai/agents/__init__.py

@@ -1,5 +1,4 @@
 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

@@ -7,6 +7,7 @@ from typing import Any, Dict, List, Optional, TypeVar
 from pydantic import (
     UUID4,
     BaseModel,
+    ConfigDict,
     Field,
     InstanceOf,
     PrivateAttr,
@@ -19,7 +20,6 @@ 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")
 
@@ -74,26 +74,21 @@ class BaseAgent(ABC, BaseModel):
     """
 
     __hash__ = object.__hash__  # type: ignore
-    _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
-    _rpm_controller: Optional[RPMController] = PrivateAttr(default=None)
+    _logger: Logger = PrivateAttr()
+    _rpm_controller: RPMController = PrivateAttr(default=None)
     _request_within_rpm_limit: Any = PrivateAttr(default=None)
-    _original_role: Optional[str] = PrivateAttr(default=None)
-    _original_goal: Optional[str] = PrivateAttr(default=None)
-    _original_backstory: Optional[str] = PrivateAttr(default=None)
-    _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
+    formatting_errors: int = 0
+    model_config = ConfigDict(arbitrary_types_allowed=True)
     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"
     )
@@ -102,8 +97,7 @@ class BaseAgent(ABC, BaseModel):
         description="Maximum number of requests per minute for the agent execution to be respected.",
     )
     allow_delegation: bool = Field(
-        default=False,
-        description="Enable agent to delegate and ask questions among each other.",
+        default=True, description="Allow delegation of tasks to agents"
     )
     tools: Optional[List[Any]] = Field(
         default_factory=list, description="Tools at agents' disposal"
@@ -129,29 +123,20 @@ class BaseAgent(ABC, BaseModel):
         default=None, description="Maximum number of tokens for the agent's execution."
     )
 
-    @model_validator(mode="before")
-    @classmethod
-    def process_model_config(cls, values):
-        return process_config(values, cls)
+    _original_role: str | None = None
+    _original_goal: str | None = None
+    _original_backstory: str | None = None
+    _token_process: TokenProcess = TokenProcess()
+
+    def __init__(__pydantic_self__, **data):
+        config = data.pop("config", {})
+        super().__init__(**config, **data)
 
     @model_validator(mode="after")
-    def 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()
-
+    def set_config_attributes(self):
+        if self.config:
+            for key, value in self.config.items():
+                setattr(self, key, value)
         return self
 
     @field_validator("id", mode="before")
@@ -162,6 +147,14 @@ 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."""
@@ -177,7 +170,7 @@ class BaseAgent(ABC, BaseModel):
     @property
     def key(self):
         source = [self.role, self.goal, self.backstory]
-        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
+        return md5("|".join(source).encode()).hexdigest()
 
     @abstractmethod
     def execute_task(
@@ -225,8 +218,10 @@ 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

@@ -19,13 +19,15 @@ 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
 
     def _should_force_answer(self) -> bool:
         """Determine if a forced answer is required based on iteration count."""
-        return (self.iterations >= self.max_iter) and not self.have_forced_answer
+        return (
+            self.iterations == self.force_answer_max_iterations
+        ) 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."""

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

@@ -39,3 +39,9 @@ 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

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

src/crewai/agents/cache/cache_handler.py

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

src/crewai/agents/crew_agent_executor.py

@@ -1,350 +0,0 @@
-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,
-        use_stop_words: bool,
-        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 = use_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.have_forced_answer = False
-        self.name_to_tool_map = {tool.name: tool for tool in self.tools}
-
-    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()
-
-        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})
-            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)
-        ):
-            self._printer.print(
-                content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.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)
-        ):
-            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{self.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{self.agent.role}\033[00m"
-                )
-                self._printer.print(
-                    content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m"
-                )
-
-    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"]
-            for i in range(0, len(content), 5000):
-                messages_groups.append(content[i : i + 5000])
-
-        summarized_contents = []
-        for group in messages_groups:
-            summary = self.llm.call(
-                [
-                    self._format_msg(
-                        self._i18n.slices("summarizer_system_message"), role="system"
-                    ),
-                    self._format_msg(
-                        self._i18n.errors("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.errors("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):
-                if self.crew is not None and hasattr(self.crew, "_train_iteration"):
-                    training_data[agent_id][self.crew._train_iteration][
-                        "improved_output"
-                    ] = result.output
-                    CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
-                else:
-                    self._logger.log(
-                        "error",
-                        "Invalid crew or missing _train_iteration attribute.",
-                        color="red",
-                    )
-
-        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

@@ -0,0 +1,405 @@
+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

@@ -1,6 +1,10 @@
 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
 
@@ -10,39 +14,7 @@ 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 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:
+class CrewAgentParser(ReActSingleInputOutputParser):
     """Parses ReAct-style LLM calls that have a single tool input.
 
     Expects output to be in one of two formats.
@@ -66,11 +38,7 @@ class CrewAgentParser:
     _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]*(.*)"
@@ -79,7 +47,7 @@ class CrewAgentParser:
         if action_match:
             if includes_answer:
                 raise OutputParserException(
-                    f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}"
+                    f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
                 )
             action = action_match.group(1)
             clean_action = self._clean_action(action)
@@ -89,23 +57,30 @@ class CrewAgentParser:
             tool_input = action_input.strip(" ").strip('"')
             safe_tool_input = self._safe_repair_json(tool_input)
 
-            return AgentAction(thought, clean_action, safe_tool_input, text)
+            return AgentAction(clean_action, safe_tool_input, text)
 
         elif includes_answer:
-            final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
-            return AgentFinish(thought, final_answer, text)
+            return AgentFinish(
+                {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
+            )
 
         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
             self.agent.increment_formatting_errors()
             raise OutputParserException(
-                f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
+                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,
             )
         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(
-                MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
+                f"Could not parse LLM output: `{text}`",
+                observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
+                llm_output=text,
+                send_to_llm=True,
             )
         else:
             format = self._i18n.slice("format_without_tools")
@@ -113,15 +88,11 @@ class CrewAgentParser:
             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

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

src/crewai/cli/authentication/constants.py

@@ -1,4 +0,0 @@
-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

@@ -1,75 +0,0 @@
-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

@@ -1,144 +0,0 @@
-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

@@ -1,5 +1,3 @@
-from typing import Optional
-
 import click
 import pkg_resources
 
@@ -9,10 +7,7 @@ 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 .replay_from_task import replay_task_command
 from .reset_memories_command import reset_memories_command
 from .run_crew import run_crew
@@ -170,84 +165,12 @@ 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
-
-
-@deploy.command(name="create")
-@click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
-def deploy_create(yes: bool):
-    """Create a Crew deployment."""
-    deploy_cmd = DeployCommand()
-    deploy_cmd.create_crew(yes)
-
-
-@deploy.command(name="list")
-def deploy_list():
-    """List all deployments."""
-    deploy_cmd = DeployCommand()
-    deploy_cmd.list_crews()
-
-
-@deploy.command(name="push")
-@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deploy_push(uuid: Optional[str]):
-    """Deploy the Crew."""
-    deploy_cmd = DeployCommand()
-    deploy_cmd.deploy(uuid=uuid)
-
-
-@deploy.command(name="status")
-@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deply_status(uuid: Optional[str]):
-    """Get the status of a deployment."""
-    deploy_cmd = DeployCommand()
-    deploy_cmd.get_crew_status(uuid=uuid)
-
-
-@deploy.command(name="logs")
-@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deploy_logs(uuid: Optional[str]):
-    """Get the logs of a deployment."""
-    deploy_cmd = DeployCommand()
-    deploy_cmd.get_crew_logs(uuid=uuid)
-
-
-@deploy.command(name="remove")
-@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deploy_remove(uuid: Optional[str]):
-    """Remove a deployment."""
-    deploy_cmd = DeployCommand()
-    deploy_cmd.remove_crew(uuid=uuid)
-
-
 if __name__ == "__main__":
     crewai()

src/crewai/cli/deploy/api.py

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

src/crewai/cli/deploy/main.py

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

src/crewai/cli/deploy/utils.py

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

src/crewai/cli/install_crew.py

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

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

@@ -14,9 +14,12 @@ pip install poetry
 
 Next, navigate to your project directory and install the dependencies:
 
-1. First lock the dependencies and install them by using the CLI command:
+1. First lock the dependencies and then install them:
 ```bash
-crewai install
+poetry lock
+```
+```bash
+poetry install
 ```
 ### Customizing
 
@@ -34,6 +37,10 @@ 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

@@ -10,6 +10,8 @@ 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

@@ -6,8 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.63.1,<1.0.0" }
-
+crewai = { extras = ["tools"], version = "^0.51.0" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"

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

@@ -15,11 +15,12 @@ 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
+poetry lock
+```
+```bash
+poetry install
 ```
-
 ### Customizing
 
 **Add your `OPENAI_API_KEY` into the `.env` file**
@@ -34,7 +35,7 @@ crewai install
 To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
 
 ```bash
-crewai run
+poetry run {{folder_name}}
 ```
 
 This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
@@ -48,7 +49,6 @@ 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 = ">=0.63.1,<1.0.0" }
+crewai = { extras = ["tools"], version = "^0.51.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -16,7 +16,10 @@ Next, navigate to your project directory and install the dependencies:
 
 1. First lock the dependencies and then install them:
 ```bash
-crewai install
+poetry lock
+```
+```bash
+poetry install
 ```
 ### Customizing
 
@@ -32,7 +35,7 @@ crewai install
 To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
 
 ```bash
-crewai run
+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/pipeline_router/pyproject.toml

@@ -6,8 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.63.1,<1.0.0" }
-
+crewai = { extras = ["tools"], version = "^0.51.0" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:main"

src/crewai/crew.py

@@ -1,14 +1,16 @@
 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,
@@ -22,7 +24,6 @@ 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
@@ -47,10 +48,11 @@ 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  # type: ignore
+        import agentops
     except ImportError:
         pass
 
@@ -68,6 +70,7 @@ 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).
@@ -103,6 +106,7 @@ 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)
@@ -111,18 +115,6 @@ 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.",
@@ -137,6 +129,10 @@ 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
     )
@@ -212,15 +208,6 @@ 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
@@ -229,19 +216,11 @@ class Crew(BaseModel):
     def create_crew_memory(self) -> "Crew":
         """Set private attributes."""
         if self.memory:
-            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._long_term_memory = LongTermMemory()
+            self._short_term_memory = ShortTermMemory(
+                crew=self, embedder_config=self.embedder
             )
+            self._entity_memory = EntityMemory(crew=self, embedder_config=self.embedder)
         return self
 
     @model_validator(mode="after")
@@ -385,7 +364,7 @@ class Crew(BaseModel):
         source = [agent.key for agent in self.agents] + [
             task.key for task in self.tasks
         ]
-        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
+        return md5("|".join(source).encode()).hexdigest()
 
     def _setup_from_config(self):
         assert self.config is not None, "Config should not be None."
@@ -538,6 +517,10 @@ 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)
 
@@ -558,7 +541,7 @@ class Crew(BaseModel):
         )._handle_crew_planning()
 
         for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
-            task.description += step_plan.plan
+            task.description += step_plan
 
     def _store_execution_log(
         self,
@@ -604,18 +587,9 @@ 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"),
@@ -625,7 +599,6 @@ class Crew(BaseModel):
                 verbose=self.verbose,
             )
             self.manager_agent = manager
-        manager.crew = self
 
     def _execute_tasks(
         self,
@@ -770,6 +743,9 @@ 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")
 
@@ -792,6 +768,7 @@ 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")
 
@@ -944,30 +921,29 @@ 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: Optional[str] = None,
+        openai_model_name: str,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
+        """Test and evaluate the Crew with the given inputs for n iterations."""
         self._test_execution_span = self._telemetry.test_execution_span(
-            self,
-            n_iterations,
-            inputs,
-            openai_model_name,  # type: ignore[arg-type]
-        )  # type: ignore[arg-type]
-        evaluator = CrewEvaluator(self, openai_model_name)  # type: ignore[arg-type]
+            self, n_iterations, inputs, openai_model_name
+        )
+        evaluator = CrewEvaluator(self, openai_model_name)
 
         for i in range(1, n_iterations + 1):
             evaluator.set_iteration(i)

src/crewai/crews/__init__.py

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

src/crewai/llm.py

@@ -1,96 +0,0 @@
-from typing import Any, Dict, List, Optional, Union
-import logging
-import litellm
-from litellm import get_supported_openai_params
-
-
-class LLM:
-    def __init__(
-        self,
-        model: str,
-        timeout: Optional[Union[float, int]] = None,
-        temperature: Optional[float] = None,
-        top_p: Optional[float] = None,
-        n: Optional[int] = None,
-        stop: Optional[Union[str, List[str]]] = None,
-        max_completion_tokens: Optional[int] = None,
-        max_tokens: Optional[int] = None,
-        presence_penalty: Optional[float] = None,
-        frequency_penalty: Optional[float] = None,
-        logit_bias: Optional[Dict[int, float]] = None,
-        response_format: Optional[Dict[str, Any]] = None,
-        seed: Optional[int] = None,
-        logprobs: Optional[bool] = None,
-        top_logprobs: Optional[int] = None,
-        base_url: Optional[str] = None,
-        api_version: Optional[str] = None,
-        api_key: Optional[str] = None,
-        callbacks: List[Any] = [],
-        **kwargs,
-    ):
-        self.model = model
-        self.timeout = timeout
-        self.temperature = temperature
-        self.top_p = top_p
-        self.n = n
-        self.stop = stop
-        self.max_completion_tokens = max_completion_tokens
-        self.max_tokens = max_tokens
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.logit_bias = logit_bias
-        self.response_format = response_format
-        self.seed = seed
-        self.logprobs = logprobs
-        self.top_logprobs = top_logprobs
-        self.base_url = base_url
-        self.api_version = api_version
-        self.api_key = api_key
-        self.callbacks = callbacks
-        self.kwargs = kwargs
-
-        litellm.drop_params = True
-        litellm.callbacks = callbacks
-
-    def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
-        if callbacks and len(callbacks) > 0:
-            litellm.callbacks = callbacks
-
-        try:
-            params = {
-                "model": self.model,
-                "messages": messages,
-                "timeout": self.timeout,
-                "temperature": self.temperature,
-                "top_p": self.top_p,
-                "n": self.n,
-                "stop": self.stop,
-                "max_tokens": self.max_tokens or self.max_completion_tokens,
-                "presence_penalty": self.presence_penalty,
-                "frequency_penalty": self.frequency_penalty,
-                "logit_bias": self.logit_bias,
-                "response_format": self.response_format,
-                "seed": self.seed,
-                "logprobs": self.logprobs,
-                "top_logprobs": self.top_logprobs,
-                "api_base": self.base_url,
-                "api_version": self.api_version,
-                "api_key": self.api_key,
-                **self.kwargs,
-            }
-            # Remove None values to avoid passing unnecessary parameters
-            params = {k: v for k, v in params.items() if v is not None}
-
-            response = litellm.completion(**params)
-            return response["choices"][0]["message"]["content"]
-        except Exception as e:
-            logging.error(f"LiteLLM call failed: {str(e)}")
-            raise  # Re-raise the exception after logging
-
-    def supports_function_calling(self) -> bool:
-        try:
-            params = get_supported_openai_params(model=self.model)
-            return "response_format" in params
-        except Exception as e:
-            logging.error(f"Failed to get supported params: {str(e)}")
-            return False

src/crewai/memory/__init__.py

@@ -1,5 +1,3 @@
 from .entity.entity_memory import EntityMemory
 from .long_term.long_term_memory import LongTermMemory
 from .short_term.short_term_memory import ShortTermMemory
-
-__all__ = ["EntityMemory", "LongTermMemory", "ShortTermMemory"]

src/crewai/memory/entity/entity_memory.py

@@ -10,13 +10,12 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    def __init__(self, crew=None, embedder_config=None, storage=None):
-        storage = (
-            storage
-            if storage
-            else RAGStorage(
-                type="entities", allow_reset=False, embedder_config=embedder_config, crew=crew
-            )
+    def __init__(self, crew=None, embedder_config=None):
+        storage = RAGStorage(
+            type="entities",
+            allow_reset=False,
+            embedder_config=embedder_config,
+            crew=crew,
         )
         super().__init__(storage)
 

src/crewai/memory/long_term/long_term_memory.py

@@ -14,8 +14,8 @@ class LongTermMemory(Memory):
     LongTermMemoryItem instances.
     """
 
-    def __init__(self, storage=None):
-        storage = storage if storage else LTMSQLiteStorage()
+    def __init__(self):
+        storage = LTMSQLiteStorage()
         super().__init__(storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"

src/crewai/memory/memory.py

@@ -21,7 +21,7 @@ class Memory:
         if agent:
             metadata["agent"] = agent
 
-        self.storage.save(value, metadata)
+        self.storage.save(value, metadata)  # type: ignore # Maybe BUG? Should be self.storage.save(key, value, metadata)
 
     def search(self, query: str) -> Dict[str, Any]:
         return self.storage.search(query)

src/crewai/memory/short_term/short_term_memory.py

@@ -13,14 +13,10 @@ class ShortTermMemory(Memory):
     MemoryItem instances.
     """
 
-    def __init__(self, crew=None, embedder_config=None, storage=None):
-        storage = (
-            storage
-            if storage
-            else RAGStorage(
+    def __init__(self, crew=None, embedder_config=None):
+        storage = RAGStorage(
             type="short_term", embedder_config=embedder_config, crew=crew
         )
-        )
         super().__init__(storage)
 
     def save(

src/crewai/memory/storage/rag_storage.py

@@ -5,14 +5,13 @@ import os
 import shutil
 from typing import Any, Dict, List, Optional
 
+from crewai.memory.storage.interface import Storage
+from crewai.utilities.paths import db_storage_path
 from embedchain import App
 from embedchain.llm.base import BaseLlm
 from embedchain.models.data_type import DataType
 from embedchain.vectordb.chroma import InvalidDimensionException
 
-from crewai.memory.storage.interface import Storage
-from crewai.utilities.paths import db_storage_path
-
 
 @contextlib.contextmanager
 def suppress_logging(
@@ -83,7 +82,7 @@ class RAGStorage(Storage):
         """
         Sanitizes agent roles to ensure valid directory names.
         """
-        return role.replace("\n", "").replace(" ", "_").replace("/", "_")
+        return role.replace('\n', '').replace(' ', '_').replace('/', '_')
 
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         self._generate_embedding(value, metadata)

src/crewai/pipeline/__init__.py

@@ -1,5 +1,3 @@
 from crewai.pipeline.pipeline import Pipeline
 from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult
 from crewai.pipeline.pipeline_output import PipelineOutput
-
-__all__ = ["Pipeline", "PipelineKickoffResult", "PipelineOutput"]

src/crewai/project/annotations.py

@@ -1,5 +1,3 @@
-from functools import wraps
-
 from crewai.project.utils import memoize
 
 
@@ -7,17 +5,13 @@ def task(func):
     if not hasattr(task, "registration_order"):
         task.registration_order = []
 
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        result = func(*args, **kwargs)
-        if not result.name:
-            result.name = func.__name__
-        return result
+    func.is_task = True
+    wrapped_func = memoize(func)
 
-    setattr(wrapper, "is_task", True)
+    # Append the function name to the registration order list
     task.registration_order.append(func.__name__)
 
-    return memoize(wrapper)
+    return wrapped_func
 
 
 def agent(func):
@@ -103,8 +97,7 @@ def crew(func):
         for task_name in sorted_task_names:
             task_instance = tasks[task_name]()
             instantiated_tasks.append(task_instance)
-            agent_instance = getattr(task_instance, "agent", None)
-            if agent_instance is not None:
+            if hasattr(task_instance, "agent"):
                 agent_instance = task_instance.agent
                 if agent_instance.role not in agent_roles:
                     instantiated_agents.append(agent_instance)

src/crewai/project/crew_base.py

@@ -1,45 +1,56 @@
 import inspect
+import os
 from pathlib import Path
 from typing import Any, Callable, Dict
 
 import yaml
 from dotenv import load_dotenv
+from pydantic import ConfigDict
 
 load_dotenv()
 
 
 def CrewBase(cls):
     class WrappedClass(cls):
+        model_config = ConfigDict(arbitrary_types_allowed=True)
         is_crew_class: bool = True  # type: ignore
 
-        # Get the directory of the class being decorated
-        base_directory = Path(inspect.getfile(cls)).parent
+        base_directory = None
+        for frame_info in inspect.stack():
+            if "site-packages" not in frame_info.filename:
+                base_directory = Path(frame_info.filename).parent.resolve()
+                break
 
         original_agents_config_path = getattr(
             cls, "agents_config", "config/agents.yaml"
         )
+
         original_tasks_config_path = getattr(cls, "tasks_config", "config/tasks.yaml")
 
         def __init__(self, *args, **kwargs):
             super().__init__(*args, **kwargs)
 
-            agents_config_path = self.base_directory / self.original_agents_config_path
-            tasks_config_path = self.base_directory / self.original_tasks_config_path
+            if self.base_directory is None:
+                raise Exception(
+                    "Unable to dynamically determine the project's base directory, you must run it from the project's root directory."
+                )
 
-            self.agents_config = self.load_yaml(agents_config_path)
-            self.tasks_config = self.load_yaml(tasks_config_path)
+            self.agents_config = self.load_yaml(
+                os.path.join(self.base_directory, self.original_agents_config_path)
+            )
+
+            self.tasks_config = self.load_yaml(
+                os.path.join(self.base_directory, self.original_tasks_config_path)
+            )
 
             self.map_all_agent_variables()
             self.map_all_task_variables()
 
         @staticmethod
-        def load_yaml(config_path: Path):
-            try:
-                with open(config_path, "r", encoding="utf-8") as file:
+        def load_yaml(config_path: str):
+            with open(config_path, "r") as file:
+                # parsedContent = YamlParser.parse(file)  # type: ignore # Argument 1 to "parse" has incompatible type "TextIOWrapper"; expected "YamlParser"
                 return yaml.safe_load(file)
-            except FileNotFoundError:
-                print(f"File not found: {config_path}")
-                raise
 
         def _get_all_functions(self):
             return {
@@ -89,10 +100,7 @@ def CrewBase(cls):
             callbacks: Dict[str, Callable],
         ) -> None:
             if llm := agent_info.get("llm"):
-                try:
                 self.agents_config[agent_name]["llm"] = llms[llm]()
-                except KeyError:
-                    self.agents_config[agent_name]["llm"] = llm
 
             if tools := agent_info.get("tools"):
                 self.agents_config[agent_name]["tools"] = [

src/crewai/project/pipeline_base.py

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

src/crewai/routers/__init__.py

@@ -1,3 +1 @@
 from crewai.routers.router import Router
-
-__all__ = ["Router"]

src/crewai/routers/router.py

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

src/crewai/task.py

@@ -6,24 +6,16 @@ import uuid
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
-from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 from opentelemetry.trace import Span
-from pydantic import (
-    UUID4,
-    BaseModel,
-    Field,
-    PrivateAttr,
-    field_validator,
-    model_validator,
-)
+from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
-from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.i18n import I18N
 
@@ -47,6 +39,9 @@ class Task(BaseModel):
         tools: List of tools/resources limited for task execution.
     """
 
+    class Config:
+        arbitrary_types_allowed = True
+
     __hash__ = object.__hash__  # type: ignore
     used_tools: int = 0
     tools_errors: int = 0
@@ -108,29 +103,17 @@ class Task(BaseModel):
         description="A converter class used to export structured output",
         default=None,
     )
-    processed_by_agents: Set[str] = Field(default_factory=set)
 
-    _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
-    _execution_span: Optional[Span] = PrivateAttr(default=None)
-    _original_description: Optional[str] = PrivateAttr(default=None)
-    _original_expected_output: Optional[str] = PrivateAttr(default=None)
-    _thread: Optional[threading.Thread] = PrivateAttr(default=None)
-    _execution_time: Optional[float] = PrivateAttr(default=None)
+    _telemetry: Telemetry
+    _execution_span: Span | None = None
+    _original_description: str | None = None
+    _original_expected_output: str | None = None
+    _thread: threading.Thread | None = None
+    _execution_time: float | None = None
 
-    @model_validator(mode="before")
-    @classmethod
-    def process_model_config(cls, values):
-        return process_config(values, cls)
-
-    @model_validator(mode="after")
-    def validate_required_fields(self):
-        required_fields = ["description", "expected_output"]
-        for field in required_fields:
-            if getattr(self, field) is None:
-                raise ValueError(
-                    f"{field} must be provided either directly or through config"
-                )
-        return self
+    def __init__(__pydantic_self__, **data):
+        config = data.pop("config", {})
+        super().__init__(**config, **data)
 
     @field_validator("id", mode="before")
     @classmethod
@@ -154,6 +137,12 @@ class Task(BaseModel):
             return value[1:]
         return value
 
+    @model_validator(mode="after")
+    def set_private_attrs(self) -> "Task":
+        """Set private attributes."""
+        self._telemetry = Telemetry()
+        return self
+
     @model_validator(mode="after")
     def set_attributes_based_on_config(self) -> "Task":
         """Set attributes based on the agent configuration."""
@@ -196,7 +185,7 @@ class Task(BaseModel):
         expected_output = self._original_expected_output or self.expected_output
         source = [description, expected_output]
 
-        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
+        return md5("|".join(source).encode()).hexdigest()
 
     def execute_async(
         self,
@@ -242,8 +231,6 @@ class Task(BaseModel):
         self.prompt_context = context
         tools = tools or self.tools or []
 
-        self.processed_by_agents.add(agent.role)
-
         result = agent.execute_task(
             task=self,
             context=context,
@@ -253,9 +240,7 @@ class Task(BaseModel):
         pydantic_output, json_output = self._export_output(result)
 
         task_output = TaskOutput(
-            name=self.name,
             description=self.description,
-            expected_output=self.expected_output,
             raw=result,
             pydantic=pydantic_output,
             json_dict=json_output,
@@ -313,10 +298,8 @@ class Task(BaseModel):
         """Increment the tools errors counter."""
         self.tools_errors += 1
 
-    def increment_delegations(self, agent_name: Optional[str]) -> None:
+    def increment_delegations(self) -> None:
         """Increment the delegations counter."""
-        if agent_name:
-            self.processed_by_agents.add(agent_name)
         self.delegations += 1
 
     def copy(self, agents: List["BaseAgent"]) -> "Task":

src/crewai/tasks/task_output.py

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

src/crewai/telemetry/__init__.py

@@ -1,3 +1 @@
 from .telemetry import Telemetry
-
-__all__ = ["Telemetry"]

src/crewai/telemetry/telemetry.py

@@ -4,28 +4,15 @@ import asyncio
 import json
 import os
 import platform
-import warnings
-from typing import TYPE_CHECKING, Any, Optional
-from contextlib import contextmanager
+from typing import TYPE_CHECKING, Any
 
-
-@contextmanager
-def suppress_warnings():
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore")
-        yield
-
-
-with suppress_warnings():
 import pkg_resources
-
-
-from opentelemetry import trace  # noqa: E402
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter  # noqa: E402
-from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
-from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
-from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402
-from opentelemetry.trace import Span, Status, StatusCode  # noqa: E402
+from opentelemetry import trace
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+from opentelemetry.sdk.resources import SERVICE_NAME, Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+from opentelemetry.trace import Span, Status, StatusCode
 
 if TYPE_CHECKING:
     from crewai.crew import Crew
@@ -41,6 +28,18 @@ class Telemetry:
     agents backstories or goals nor responses or any data that is being
     processed by the agents, nor any secrets and env vars.
 
+    Data collected includes:
+    - Version of crewAI
+    - Version of Python
+    - General OS (e.g. number of CPUs, macOS/Windows/Linux)
+    - Number of agents and tasks in a crew
+    - Crew Process being used
+    - If Agents are using memory or allowing delegation
+    - If Tasks are being executed in parallel or sequentially
+    - Language model being used
+    - Roles of agents in a crew
+    - Tools names available
+
     Users can opt-in to sharing more complete data using the `share_crew`
     attribute in the Crew class.
     """
@@ -53,7 +52,6 @@ class Telemetry:
             self.resource = Resource(
                 attributes={SERVICE_NAME: "crewAI-telemetry"},
             )
-            with suppress_warnings():
             self.provider = TracerProvider(resource=self.resource)
 
             processor = BatchSpanProcessor(
@@ -76,7 +74,6 @@ class Telemetry:
     def set_tracer(self):
         if self.ready and not self.trace_set:
             try:
-                with suppress_warnings():
                 trace.set_tracer_provider(self.provider)
                 self.trace_set = True
             except Exception:
@@ -117,13 +114,10 @@ class Telemetry:
                                     "max_iter": agent.max_iter,
                                     "max_rpm": agent.max_rpm,
                                     "i18n": agent.i18n.prompt_file,
-                                    "function_calling_llm": agent.function_calling_llm.model
-                                    if agent.function_calling_llm
-                                    else "",
-                                    "llm": agent.llm.model,
+                                    "llm": json.dumps(
+                                        self._safe_llm_attributes(agent.llm)
+                                    ),
                                     "delegation_enabled?": agent.allow_delegation,
-                                    "allow_code_execution?": agent.allow_code_execution,
-                                    "max_retry_limit": agent.max_retry_limit,
                                     "tools_names": [
                                         tool.name.casefold()
                                         for tool in agent.tools or []
@@ -171,58 +165,7 @@ class Telemetry:
                     self._add_attribute(
                         span, "crew_inputs", json.dumps(inputs) if inputs else None
                     )
-                else:
-                    self._add_attribute(
-                        span,
-                        "crew_agents",
-                        json.dumps(
-                            [
-                                {
-                                    "key": agent.key,
-                                    "id": str(agent.id),
-                                    "role": agent.role,
-                                    "verbose?": agent.verbose,
-                                    "max_iter": agent.max_iter,
-                                    "max_rpm": agent.max_rpm,
-                                    "function_calling_llm": agent.function_calling_llm.model
-                                    if agent.function_calling_llm
-                                    else "",
-                                    "llm": agent.llm.model,
-                                    "delegation_enabled?": agent.allow_delegation,
-                                    "allow_code_execution?": agent.allow_code_execution,
-                                    "max_retry_limit": agent.max_retry_limit,
-                                    "tools_names": [
-                                        tool.name.casefold()
-                                        for tool in agent.tools or []
-                                    ],
-                                }
-                                for agent in crew.agents
-                            ]
-                        ),
-                    )
-                    self._add_attribute(
-                        span,
-                        "crew_tasks",
-                        json.dumps(
-                            [
-                                {
-                                    "key": task.key,
-                                    "id": str(task.id),
-                                    "async_execution?": task.async_execution,
-                                    "human_input?": task.human_input,
-                                    "agent_role": task.agent.role
-                                    if task.agent
-                                    else "None",
-                                    "agent_key": task.agent.key if task.agent else None,
-                                    "tools_names": [
-                                        tool.name.casefold()
-                                        for tool in task.tools or []
-                                    ],
-                                }
-                                for task in crew.tasks
-                            ]
-                        ),
-                    )
+
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -301,7 +244,9 @@ class Telemetry:
                 self._add_attribute(span, "tool_name", tool_name)
                 self._add_attribute(span, "attempts", attempts)
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(
+                        span, "llm", json.dumps(self._safe_llm_attributes(llm))
+                    )
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -321,7 +266,9 @@ class Telemetry:
                 self._add_attribute(span, "tool_name", tool_name)
                 self._add_attribute(span, "attempts", attempts)
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(
+                        span, "llm", json.dumps(self._safe_llm_attributes(llm))
+                    )
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -339,14 +286,16 @@ class Telemetry:
                     pkg_resources.get_distribution("crewai").version,
                 )
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(
+                        span, "llm", json.dumps(self._safe_llm_attributes(llm))
+                    )
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
                 pass
 
     def individual_test_result_span(
-        self, crew: Crew, quality: float, exec_time: int, model_name: str
+        self, crew: Crew, quality: int, exec_time: int, model_name: str
     ):
         if self.ready:
             try:
@@ -400,63 +349,6 @@ class Telemetry:
             except Exception:
                 pass
 
-    def deploy_signup_error_span(self):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Deploy Signup Error")
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def start_deployment_span(self, uuid: Optional[str] = None):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Start Deployment")
-                if uuid:
-                    self._add_attribute(span, "uuid", uuid)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def create_crew_deployment_span(self):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Create Crew Deployment")
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def get_crew_logs_span(self, uuid: Optional[str], log_type: str = "deployment"):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Get Crew Logs")
-                self._add_attribute(span, "log_type", log_type)
-                if uuid:
-                    self._add_attribute(span, "uuid", uuid)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def remove_crew_span(self, uuid: Optional[str] = None):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Remove Crew")
-                if uuid:
-                    self._add_attribute(span, "uuid", uuid)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
     def crew_execution_span(self, crew: Crew, inputs: dict[str, Any] | None):
         """Records the complete execution of a crew.
         This is only collected if the user has opted-in to share the crew.
@@ -492,7 +384,7 @@ class Telemetry:
                                 "max_iter": agent.max_iter,
                                 "max_rpm": agent.max_rpm,
                                 "i18n": agent.i18n.prompt_file,
-                                "llm": agent.llm.model,
+                                "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
                                 "delegation_enabled?": agent.allow_delegation,
                                 "tools_names": [
                                     tool.name.casefold() for tool in agent.tools or []
@@ -568,3 +460,11 @@ class Telemetry:
             return span.set_attribute(key, value)
         except Exception:
             pass
+
+    def _safe_llm_attributes(self, llm):
+        attributes = ["name", "model_name", "base_url", "model", "top_k", "temperature"]
+        if llm:
+            safe_attributes = {k: v for k, v in vars(llm).items() if k in attributes}
+            safe_attributes["class"] = llm.__class__.__name__
+            return safe_attributes
+        return {}

src/crewai/tools/agent_tools.py

@@ -1,4 +1,5 @@
 from langchain.tools import StructuredTool
+
 from crewai.agents.agent_builder.utilities.base_agent_tool import BaseAgentTools
 
 

src/crewai/tools/cache_tools.py

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

src/crewai/tools/tool_calling.py

@@ -1,8 +1,8 @@
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Field
 from pydantic import BaseModel as PydanticBaseModel
 from pydantic import Field as PydanticField
+from pydantic.v1 import BaseModel, Field
 
 
 class ToolCalling(BaseModel):