Merge branch 'main' into feat/add-event-emitters-to-flows

* Update base_agent_tools.py

typo error

* Update main.py

typo error

* Update base_file_knowledge_source.py

typo error

* Update test_main.py

typo error

* Update en.json

* Update prompts.json

---------

Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -65,7 +65,6 @@ body:
         - '3.10'
         - '3.11'
         - '3.12'
-        - '3.13'
     validations:
       required: true
   - type: input
@@ -113,4 +112,4 @@ body:
       label: Additional context
       description: Add any other context about the problem here.
     validations:
-      required: true
+      required: true

.github/security.md

@@ -0,0 +1,19 @@
+CrewAI takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organization.
+If you believe you have found a security vulnerability in any CrewAI product or service, please report it to us as described below.
+
+ ## Reporting a Vulnerability
+ Please do not report security vulnerabilities through public GitHub issues.
+ To report a vulnerability, please email us at security@crewai.com.
+ Please include the requested information listed below so that we can triage your report more quickly
+
+ - Type of issue (e.g. SQL injection, cross-site scripting, etc.)
+ - Full paths of source file(s) related to the manifestation of the issue
+ - The location of the affected source code (tag/branch/commit or direct URL)
+ - Any special configuration required to reproduce the issue
+ - Step-by-step instructions to reproduce the issue (please include screenshots if needed)
+ - Proof-of-concept or exploit code (if possible)
+ - Impact of the issue, including how an attacker might exploit the issue
+
+ Once we have received your report, we will respond to you at the email address you provide. If the issue is confirmed, we will release a patch as soon as possible depending on the complexity of the issue.
+
+ At this time, we are not offering a bug bounty program. Any rewards will be at our discretion.

.github/workflows/linter.yml

@@ -6,7 +6,7 @@ jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Install Requirements
         run: |

.github/workflows/mkdocs.yml

@@ -13,10 +13,10 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: '3.10'
 
@@ -25,7 +25,7 @@ jobs:
         run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')"
 
       - name: Setup cache
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         with:
           key: mkdocs-material-${{ steps.req-hash.outputs.hash }}
           path: .cache
@@ -42,4 +42,4 @@ jobs:
           GH_TOKEN: ${{ secrets.GH_TOKEN }}
 
       - name: Build and deploy MkDocs
-        run: mkdocs gh-deploy --force
+        run: mkdocs gh-deploy --force

.github/workflows/security-checker.yml

@@ -11,7 +11,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.11.9"
 
@@ -19,5 +19,5 @@ jobs:
         run: pip install bandit
 
       - name: Run Bandit
-        run: bandit -c pyproject.toml -r src/ -lll
+        run: bandit -c pyproject.toml -r src/ -ll
 

.github/workflows/tests.yml

@@ -23,10 +23,10 @@ jobs:
 
 
       - name: Set up Python
-        run: uv python install 3.11.9
+        run: uv python install 3.12.8
 
       - name: Install the project
-        run: uv sync --dev
+        run: uv sync --dev --all-extras
 
       - name: Run tests
-        run: uv run pytest tests
+        run: uv run pytest tests -vv

.github/workflows/type-checker.yml

@@ -14,7 +14,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.11.9"
 

.gitignore

@@ -17,3 +17,7 @@ rc-tests/*
 temp/*
 .vscode/*
 crew_tasks_output.json
+.codesight
+.mypy_cache
+.ruff_cache
+.venv

README.md

@@ -44,7 +44,7 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-Ensure you have Python >=3.10 <=3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.12 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, install CrewAI:
 
@@ -100,7 +100,7 @@ You can now start developing your crew by editing the files in the `src/my_proje
 
 #### Example of a simple crew with a sequential process:
 
-Instatiate your crew:
+Instantiate your crew:
 
 ```shell
 crewai create crew latest-ai-development
@@ -121,7 +121,7 @@ researcher:
     You're a seasoned researcher with a knack for uncovering the latest
     developments in {topic}. Known for your ability to find the most relevant
     information and present it in a clear and concise manner.
-      
+
 reporting_analyst:
   role: >
     {topic} Reporting Analyst
@@ -205,7 +205,7 @@ class LatestAiDevelopmentCrew():
 			tasks=self.tasks, # Automatically created by the @task decorator
 			process=Process.sequential,
 			verbose=True,
-		) 
+		)
 ```
 
 **main.py**
@@ -351,13 +351,13 @@ pre-commit install
 ### Running Tests
 
 ```bash
-uvx pytest
+uv run pytest .
 ```
 
 ### Running static type checks
 
 ```bash
-uvx mypy
+uvx mypy src
 ```
 
 ### Packaging
@@ -376,7 +376,7 @@ pip install dist/*.tar.gz
 
 CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
 
-It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. We don't offer a way to disable it now, but we will in the future.
+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. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true.
 
 Data collected includes:
 
@@ -399,7 +399,7 @@ Data collected includes:
 - Roles of agents in a crew
   - Understand high level use cases so we can build better tools, integrations and examples about it
 - Tools names available
-  - Understand out of the publically available tools, which ones are being used the most so we can improve them
+  - Understand out of the publicly available tools, which ones are being used the most so we can improve them
 
 Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
 

docs/concepts/agents.mdx

@@ -1,159 +1,343 @@
 ---
 title: Agents
-description: What are CrewAI Agents and how to use them.
+description: Detailed guide on creating and managing agents within the CrewAI framework.
 icon: robot
 ---
 
-## What is an agent?
+## Overview of an Agent
 
-An agent is an **autonomous unit** programmed to:
-<ul>
-    <li class='leading-3'>Perform tasks</li>
-    <li class='leading-3'>Make decisions</li>
-    <li class='leading-3'>Communicate with other agents</li>
-</ul>
+In the CrewAI framework, an `Agent` is an autonomous unit that can:
+- Perform specific tasks
+- Make decisions based on its role and goal
+- Use tools to accomplish objectives
+- Communicate and collaborate with other agents
+- Maintain memory of interactions
+- Delegate tasks when allowed
 
 <Tip>
-Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like `Researcher`, `Writer`, or `Customer Support`, each contributing to the overall goal of the crew.
+Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
 </Tip>
 
-## Agent attributes
+## Agent Attributes
 
-| Attribute                  | Parameter  | Description                                                                                                                                                                                                                                    |
-| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Role**                   | `role`     | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
-| **Goal**                   | `goal`     | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
-| **Backstory**              | `backstory`| Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
-| **LLM** *(optional)*       | `llm`      | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
-| **Tools** *(optional)*     | `tools`    | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
-| **Function Calling LLM** *(optional)* | `function_calling_llm`  | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
-| **Max Iter** *(optional)*  | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
-| **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
-| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
-| **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
-| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.
-| **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
-| **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
-| **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
-| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
-| **Use System Prompt** *(optional)*     | `use_system_prompt`  | Adds the ability to not use system prompt (to support o1 models). Default is `True`.                                                                                                                                                                  |
-| **Respect Context Window** *(optional)*     | `respect_context_window`  | Summary strategy to avoid overflowing the context window. Default is `True`.                                                                                                                                                                  |
+| Attribute                               | Parameter                | Type                          | Description                                                                                                          |
+| :-------------------------------------- | :----------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Role**                                | `role`                   | `str`                         | Defines the agent's function and expertise within the crew.                                                           |
+| **Goal**                                | `goal`                   | `str`                         | The individual objective that guides the agent's decision-making.                                                     |
+| **Backstory**                           | `backstory`              | `str`                         | Provides context and personality to the agent, enriching interactions.                                                |
+| **LLM** _(optional)_                    | `llm`                    | `Union[str, LLM, Any]`        | Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or "gpt-4".              |
+| **Tools** _(optional)_                  | `tools`                  | `List[BaseTool]`              | Capabilities or functions available to the agent. Defaults to an empty list.                                          |
+| **Function Calling LLM** _(optional)_   | `function_calling_llm`   | `Optional[Any]`               | Language model for tool calling, overrides crew's LLM if specified.                                                   |
+| **Max Iterations** _(optional)_         | `max_iter`               | `int`                         | Maximum iterations before the agent must provide its best answer. Default is 20.                                      |
+| **Max RPM** _(optional)_                | `max_rpm`                | `Optional[int]`               | Maximum requests per minute to avoid rate limits.                                                                     |
+| **Max Execution Time** _(optional)_     | `max_execution_time`     | `Optional[int]`               | Maximum time (in seconds) for task execution.                                                                         |
+| **Memory** _(optional)_                 | `memory`                 | `bool`                        | Whether the agent should maintain memory of interactions. Default is True.                                            |
+| **Verbose** _(optional)_                | `verbose`                | `bool`                        | Enable detailed execution logs for debugging. Default is False.                                                       |
+| **Allow Delegation** _(optional)_       | `allow_delegation`       | `bool`                        | Allow the agent to delegate tasks to other agents. Default is False.                                                  |
+| **Step Callback** _(optional)_          | `step_callback`          | `Optional[Any]`               | Function called after each agent step, overrides crew callback.                                                       |
+| **Cache** _(optional)_                  | `cache`                  | `bool`                        | Enable caching for tool usage. Default is True.                                                                       |
+| **System Template** _(optional)_        | `system_template`        | `Optional[str]`               | Custom system prompt template for the agent.                                                                          |
+| **Prompt Template** _(optional)_        | `prompt_template`        | `Optional[str]`               | Custom prompt template for the agent.                                                                                 |
+| **Response Template** _(optional)_      | `response_template`      | `Optional[str]`               | Custom response template for the agent.                                                                               |
+| **Allow Code Execution** _(optional)_   | `allow_code_execution`   | `Optional[bool]`              | Enable code execution for the agent. Default is False.                                                                |
+| **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
+| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
+| **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
+| **Embedder Config** _(optional)_        | `embedder_config`        | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
+| **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
+| **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
 
-## Creating an agent
+## Creating Agents
+
+There are two ways to create agents in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
+
+### YAML Configuration (Recommended)
+
+Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
+
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
 
 <Note>
-  **Agent interaction**: Agents can interact with each other using CrewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
+Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
+```python Code
+crew.kickoff(inputs={'topic': 'AI Agents'})
+```
 </Note>
 
-To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example including all attributes:
+Here's an example of how to configure agents using YAML:
 
-```python Code example
+```yaml agents.yaml
+# src/latest_ai_development/config/agents.yaml
+researcher:
+  role: >
+    {topic} Senior Data Researcher
+  goal: >
+    Uncover cutting-edge developments in {topic}
+  backstory: >
+    You're a seasoned researcher with a knack for uncovering the latest
+    developments in {topic}. Known for your ability to find the most relevant
+    information and present it in a clear and concise manner.
+
+reporting_analyst:
+  role: >
+    {topic} Reporting Analyst
+  goal: >
+    Create detailed reports based on {topic} data analysis and research findings
+  backstory: >
+    You're a meticulous analyst with a keen eye for detail. You're known for
+    your ability to turn complex data into clear and concise reports, making
+    it easy for others to understand and act on the information you provide.
+```
+
+To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
+
+```python Code
+# src/latest_ai_development/crew.py
+from crewai import Agent, Crew, Process
+from crewai.project import CrewBase, agent, crew
+from crewai_tools import SerperDevTool
+
+@CrewBase
+class LatestAiDevelopmentCrew():
+  """LatestAiDevelopment crew"""
+
+  @agent
+  def researcher(self) -> Agent:
+    return Agent(
+      config=self.agents_config['researcher'],
+      verbose=True,
+      tools=[SerperDevTool()]
+    )
+
+  @agent
+  def reporting_analyst(self) -> Agent:
+    return Agent(
+      config=self.agents_config['reporting_analyst'],
+      verbose=True
+    )
+```
+
+<Note>
+The names you use in your YAML files (`agents.yaml`) should match the method names in your Python code.
+</Note>
+
+### Direct Code Definition
+
+You can create agents directly in code by instantiating the `Agent` class. Here's a comprehensive example showing all available parameters:
+
+```python Code
 from crewai import Agent
+from crewai_tools import SerperDevTool
 
+# Create an agent with all available parameters
 agent = Agent(
-  role='Data Analyst',
-  goal='Extract actionable insights',
-  backstory="""You're a data 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
-    performance of our marketing campaigns.""",
-  tools=[my_tool1, my_tool2],  # Optional, defaults to an empty list
-  llm=my_llm,  # Optional
-  function_calling_llm=my_llm,  # Optional
-  max_iter=15,  # Optional
-  max_rpm=None, # Optional
-  max_execution_time=None, # Optional
-  verbose=True,  # Optional
-  allow_delegation=False,  # Optional
-  step_callback=my_intermediate_step_callback,  # Optional
-  cache=True,  # Optional
-  system_template=my_system_template,  # Optional
-  prompt_template=my_prompt_template,  # Optional
-  response_template=my_response_template,  # Optional
-  config=my_config,  # Optional
-  crew=my_crew,  # Optional
-  tools_handler=my_tools_handler,  # Optional
-  cache_handler=my_cache_handler,  # Optional
-  callbacks=[callback1, callback2],  # Optional
-  allow_code_execution=True,  # Optional
-  max_retry_limit=2,  # Optional
-  use_system_prompt=True,  # Optional
-  respect_context_window=True,  # Optional
+    role="Senior Data Scientist",
+    goal="Analyze and interpret complex datasets to provide actionable insights",
+    backstory="With over 10 years of experience in data science and machine learning, "
+              "you excel at finding patterns in complex datasets.",
+    llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
+    function_calling_llm=None,  # Optional: Separate LLM for tool calling
+    memory=True,  # Default: True
+    verbose=False,  # Default: False
+    allow_delegation=False,  # Default: False
+    max_iter=20,  # Default: 20 iterations
+    max_rpm=None,  # Optional: Rate limit for API calls
+    max_execution_time=None,  # Optional: Maximum execution time in seconds
+    max_retry_limit=2,  # Default: 2 retries on error
+    allow_code_execution=False,  # Default: False
+    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
+    respect_context_window=True,  # Default: True
+    use_system_prompt=True,  # Default: True
+    tools=[SerperDevTool()],  # Optional: List of tools
+    knowledge_sources=None,  # Optional: List of knowledge sources
+    embedder_config=None,  # Optional: Custom embedder configuration
+    system_template=None,  # Optional: Custom system prompt template
+    prompt_template=None,  # Optional: Custom prompt template
+    response_template=None,  # Optional: Custom response template
+    step_callback=None,  # Optional: Callback function for monitoring
 )
 ```
 
-## Setting prompt templates
+Let's break down some key parameter combinations for common use cases:
 
-Prompt templates are used to format the prompt for the agent. You can use to update the system, regular and response templates for the agent. Here's an example of how to set prompt templates:
+#### Basic Research Agent
+```python Code
+research_agent = Agent(
+    role="Research Analyst",
+    goal="Find and summarize information about specific topics",
+    backstory="You are an experienced researcher with attention to detail",
+    tools=[SerperDevTool()],
+    verbose=True  # Enable logging for debugging
+)
+```
 
-```python Code example
-agent = Agent(
-        role="{topic} specialist",
-        goal="Figure {goal} out",
-        backstory="I am the master of {role}",
-        system_template="""<|start_header_id|>system<|end_header_id|>
+#### Code Development Agent
+```python Code
+dev_agent = Agent(
+    role="Senior Python Developer",
+    goal="Write and debug Python code",
+    backstory="Expert Python developer with 10 years of experience",
+    allow_code_execution=True,
+    code_execution_mode="safe",  # Uses Docker for safety
+    max_execution_time=300,  # 5-minute timeout
+    max_retry_limit=3  # More retries for complex code tasks
+)
+```
+
+#### Long-Running Analysis Agent
+```python Code
+analysis_agent = Agent(
+    role="Data Analyst",
+    goal="Perform deep analysis of large datasets",
+    backstory="Specialized in big data analysis and pattern recognition",
+    memory=True,
+    respect_context_window=True,
+    max_rpm=10,  # Limit API calls
+    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
+)
+```
+
+#### Custom Template Agent
+```python Code
+custom_agent = Agent(
+    role="Customer Service Representative",
+    goal="Assist customers with their inquiries",
+    backstory="Experienced in customer support with a focus on satisfaction",
+    system_template="""<|start_header_id|>system<|end_header_id|>
                         {{ .System }}<|eot_id|>""",
-        prompt_template="""<|start_header_id|>user<|end_header_id|>
+    prompt_template="""<|start_header_id|>user<|end_header_id|>
                         {{ .Prompt }}<|eot_id|>""",
-        response_template="""<|start_header_id|>assistant<|end_header_id|>
+    response_template="""<|start_header_id|>assistant<|end_header_id|>
                         {{ .Response }}<|eot_id|>""",
 )
 ```
 
-## Bring your third-party agents
+### Parameter Details
 
-Extend your third-party agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the CrewAI's `BaseAgent` class.
+#### Critical Parameters
+- `role`, `goal`, and `backstory` are required and shape the agent's behavior
+- `llm` determines the language model used (default: OpenAI's GPT-4)
 
-<Note>   
-  **BaseAgent** includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
+#### Memory and Context
+- `memory`: Enable to maintain conversation history
+- `respect_context_window`: Prevents token limit issues
+- `knowledge_sources`: Add domain-specific knowledge bases
+
+#### Execution Control
+- `max_iter`: Maximum attempts before giving best answer
+- `max_execution_time`: Timeout in seconds
+- `max_rpm`: Rate limiting for API calls
+- `max_retry_limit`: Retries on error
+
+#### Code Execution
+- `allow_code_execution`: Must be True to run code
+- `code_execution_mode`: 
+  - `"safe"`: Uses Docker (recommended for production)
+  - `"unsafe"`: Direct execution (use only in trusted environments)
+
+#### Templates
+- `system_template`: Defines agent's core behavior
+- `prompt_template`: Structures input format
+- `response_template`: Formats agent responses
+
+<Note>
+When using custom templates, you can use variables like `{role}`, `{goal}`, and `{input}` in your templates. These will be automatically populated during execution.
 </Note>
 
-CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
+## Agent Tools
 
-```python Code example
-from crewai import Agent, Task, Crew
-from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
+Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:
+- [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools)
+- [LangChain Tools](https://python.langchain.com/docs/integrations/tools)
 
-from langchain.agents import load_tools
+Here's how to add tools to an agent:
 
-langchain_tools = load_tools(["google-serper"], llm=llm)
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool, WikipediaTools
 
-agent1 = CustomAgent(
-    role="agent role",
-    goal="who is {input}?",
-    backstory="agent backstory",
-    verbose=True,
+# Create tools
+search_tool = SerperDevTool()
+wiki_tool = WikipediaTools()
+
+# Add tools to agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[search_tool, wiki_tool],
+    verbose=True
 )
-
-task1 = Task(
-    expected_output="a short biography of {input}",
-    description="a short biography of {input}",
-    agent=agent1,
-)
-
-agent2 = Agent(
-    role="agent role",
-    goal="summarize the short bio for {input} and if needed do more research",
-    backstory="agent backstory",
-    verbose=True,
-)
-
-task2 = Task(
-    description="a tldr summary of the short biography",
-    expected_output="5 bullet point summary of the biography",
-    agent=agent2,
-    context=[task1],
-)
-
-my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
-crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
 ```
 
-## Conclusion
+## Agent Memory and Context
 
-Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, 
-you can create sophisticated AI systems that leverage the power of collaborative intelligence.
+Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks.
+
+```python Code
+from crewai import Agent
+
+analyst = Agent(
+    role="Data Analyst",
+    goal="Analyze and remember complex data patterns",
+    memory=True,  # Enable memory
+    verbose=True
+)
+```
+
+<Note>
+When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
+</Note>
+
+## Important Considerations and Best Practices
+
+### Security and Code Execution
+- When using `allow_code_execution`, be cautious with user input and always validate it
+- Use `code_execution_mode: "safe"` (Docker) in production environments
+- Consider setting appropriate `max_execution_time` limits to prevent infinite loops
+
+### Performance Optimization
+- Use `respect_context_window: true` to prevent token limit issues
+- Set appropriate `max_rpm` to avoid rate limiting
+- Enable `cache: true` to improve performance for repetitive tasks
+- Adjust `max_iter` and `max_retry_limit` based on task complexity
+
+### Memory and Context Management
+- Use `memory: true` for tasks requiring historical context
+- Leverage `knowledge_sources` for domain-specific information
+- Configure `embedder_config` when using custom embedding models
+- Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
+
+### Agent Collaboration
+- Enable `allow_delegation: true` when agents need to work together
+- Use `step_callback` to monitor and log agent interactions
+- Consider using different LLMs for different purposes:
+  - Main `llm` for complex reasoning
+  - `function_calling_llm` for efficient tool usage
+
+### Model Compatibility
+- Set `use_system_prompt: false` for older models that don't support system messages
+- Ensure your chosen `llm` supports the features you need (like function calling)
+
+## Troubleshooting Common Issues
+
+1. **Rate Limiting**: If you're hitting API rate limits:
+   - Implement appropriate `max_rpm`
+   - Use caching for repetitive operations
+   - Consider batching requests
+
+2. **Context Window Errors**: If you're exceeding context limits:
+   - Enable `respect_context_window`
+   - Use more efficient prompts
+   - Clear agent memory periodically
+
+3. **Code Execution Issues**: If code execution fails:
+   - Verify Docker is installed for safe mode
+   - Check execution permissions
+   - Review code sandbox settings
+
+4. **Memory Issues**: If agent responses seem inconsistent:
+   - Verify memory is enabled
+   - Check knowledge source configuration
+   - Review conversation history management
+
+Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly.

docs/concepts/cli.mdx

@@ -6,7 +6,7 @@ icon: terminal
 
 # 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.
+The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
 
 ## Installation
 
@@ -28,20 +28,19 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 
 ### 1. Create
 
-Create a new crew or pipeline.
+Create a new crew or flow.
 
 ```shell
 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
+- `TYPE`: Choose between "crew" or "flow"
+- `NAME`: Name of the crew or flow
 
 Example:
 ```shell
 crewai create crew my_new_crew
-crewai create pipeline my_new_pipeline --router
+crewai create flow my_new_flow
 ```
 
 ### 2. Version
@@ -146,3 +145,34 @@ crewai run
 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.
 </Note>
+
+
+### 9. API Keys
+
+When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
+
+Once you've selected an LLM provider, you will be prompted for API keys.
+
+#### Initial API key providers
+
+The CLI will initially prompt for API keys for the following services:
+
+* OpenAI
+* Groq
+* Anthropic
+* Google Gemini
+
+When you select a provider, the CLI will prompt you to enter your API key.
+
+#### Other Options
+
+If you select option 6, you will be able to select from a list of LiteLLM supported providers.
+
+When you select a provider, the CLI will prompt you to enter the Key name and the API key.
+
+See the following link for each provider's key name:
+
+* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
+
+
+

docs/concepts/crews.mdx

@@ -22,7 +22,8 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
 | **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
 | **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
-| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`.                                                                                                                                                       |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
+| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                          |
 | **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`.                                                                                                                                       |
@@ -31,7 +32,6 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
 | **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
-| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
@@ -40,6 +40,155 @@ A crew in crewAI represents a collaborative group of agents working together to
 **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.
 </Tip>
 
+## Creating Crews
+
+There are two ways to create crews in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
+
+### YAML Configuration (Recommended)
+
+Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
+
+After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
+
+#### Example Crew Class with Decorators
+
+```python code
+from crewai import Agent, Crew, Task, Process
+from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
+
+
+@CrewBase
+class YourCrewName:
+    """Description of your crew"""
+
+    # Paths to your YAML configuration files
+    # To see an example agent and task defined in YAML, checkout the following:
+    # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
+    # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
+    agents_config = 'config/agents.yaml' 
+    tasks_config = 'config/tasks.yaml' 
+
+    @before_kickoff
+    def prepare_inputs(self, inputs):
+        # Modify inputs before the crew starts
+        inputs['additional_data'] = "Some extra information"
+        return inputs
+
+    @after_kickoff
+    def process_output(self, output):
+        # Modify output after the crew finishes
+        output.raw += "\nProcessed after kickoff."
+        return output
+
+    @agent
+    def agent_one(self) -> Agent:
+        return Agent(
+            config=self.agents_config['agent_one'],
+            verbose=True
+        )
+
+    @agent
+    def agent_two(self) -> Agent:
+        return Agent(
+            config=self.agents_config['agent_two'],
+            verbose=True
+        )
+
+    @task
+    def task_one(self) -> Task:
+        return Task(
+            config=self.tasks_config['task_one']
+        )
+
+    @task
+    def task_two(self) -> Task:
+        return Task(
+            config=self.tasks_config['task_two']
+        )
+
+    @crew
+    def crew(self) -> Crew:
+        return Crew(
+            agents=self.agents,  # Automatically collected by the @agent decorator
+            tasks=self.tasks,    # Automatically collected by the @task decorator. 
+            process=Process.sequential,
+            verbose=True,
+        )
+```
+
+<Note>
+Tasks will be executed in the order they are defined.
+</Note>
+
+The `CrewBase` class, along with these decorators, automates the collection of agents and tasks, reducing the need for manual management.
+
+#### Decorators overview from `annotations.py`
+
+CrewAI provides several decorators in the `annotations.py` file that are used to mark methods within your crew class for special handling:
+
+- `@CrewBase`: Marks the class as a crew base class.
+- `@agent`: Denotes a method that returns an `Agent` object.
+- `@task`: Denotes a method that returns a `Task` object.
+- `@crew`: Denotes the method that returns the `Crew` object.
+- `@before_kickoff`: (Optional) Marks a method to be executed before the crew starts.
+- `@after_kickoff`: (Optional) Marks a method to be executed after the crew finishes.
+
+These decorators help in organizing your crew's structure and automatically collecting agents and tasks without manually listing them.
+
+### Direct Code Definition (Alternative)
+
+Alternatively, you can define the crew directly in code without using YAML configuration files.
+
+```python code
+from crewai import Agent, Crew, Task, Process
+from crewai_tools import YourCustomTool
+
+class YourCrewName:
+    def agent_one(self) -> Agent:
+        return Agent(
+            role="Data Analyst",
+            goal="Analyze data trends in the market",
+            backstory="An experienced data analyst with a background in economics",
+            verbose=True,
+            tools=[YourCustomTool()]
+        )
+
+    def agent_two(self) -> Agent:
+        return Agent(
+            role="Market Researcher",
+            goal="Gather information on market dynamics",
+            backstory="A diligent researcher with a keen eye for detail",
+            verbose=True
+        )
+
+    def task_one(self) -> Task:
+        return Task(
+            description="Collect recent market data and identify trends.",
+            expected_output="A report summarizing key trends in the market.",
+            agent=self.agent_one()
+        )
+
+    def task_two(self) -> Task:
+        return Task(
+            description="Research factors affecting market dynamics.",
+            expected_output="An analysis of factors influencing the market.",
+            agent=self.agent_two()
+        )
+
+    def crew(self) -> Crew:
+        return Crew(
+            agents=[self.agent_one(), self.agent_two()],
+            tasks=[self.task_one(), self.task_two()],
+            process=Process.sequential,
+            verbose=True
+        )
+```
+
+In this example:
+
+- Agents and tasks are defined directly within the class without decorators.
+- We manually create and manage the list of agents and tasks.
+- This approach provides more control but can be less maintainable for larger projects.
 
 ## Crew Output
 
@@ -187,4 +336,4 @@ Then, to replay from a specific task, use:
 crewai replay -t <task_id>
 ```
 
-These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/concepts/flows.mdx

@@ -628,4 +628,4 @@ Also, check out our YouTube video on how to use flows in CrewAI below!
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
   allowfullscreen
-></iframe>
+></iframe>

docs/concepts/knowledge.mdx

@@ -0,0 +1,319 @@
+---
+title: Knowledge
+description: What is knowledge in CrewAI and how to use it.
+icon: book
+---
+
+# Using Knowledge in CrewAI
+
+## What is Knowledge?
+
+Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
+Think of it as giving your agents a reference library they can consult while working.
+
+<Info>
+  Key benefits of using Knowledge:
+  - Enhance agents with domain-specific information
+  - Support decisions with real-world data
+  - Maintain context across conversations
+  - Ground responses in factual information
+</Info>
+
+## Supported Knowledge Sources
+
+CrewAI supports various types of knowledge sources out of the box:
+
+<CardGroup cols={2}>
+  <Card title="Text Sources" icon="text">
+    - Raw strings
+    - Text files (.txt)
+    - PDF documents
+  </Card>
+  <Card title="Structured Data" icon="table">
+    - CSV files
+    - Excel spreadsheets
+    - JSON documents
+  </Card>
+</CardGroup>
+
+## Quick Start
+
+Here's an example using string-based knowledge:
+
+```python Code
+from crewai import Agent, Task, Crew, Process, LLM
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+
+# Create a knowledge source
+content = "Users name is John. He is 30 years old and lives in San Francisco."
+string_source = StringKnowledgeSource(
+    content=content,
+)
+
+# Create an LLM with a temperature of 0 to ensure deterministic outputs
+llm = LLM(model="gpt-4o-mini", temperature=0)
+
+# Create an agent with the knowledge store
+agent = Agent(
+    role="About User",
+    goal="You know everything about the user.",
+    backstory="""You are a master at understanding people and their preferences.""",
+    verbose=True,
+    allow_delegation=False,
+    llm=llm,
+)
+task = Task(
+    description="Answer the following questions about the user: {question}",
+    expected_output="An answer to the question.",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+)
+
+result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
+```
+
+## Knowledge Configuration
+
+### Chunking Configuration
+
+Control how content is split for processing by setting the chunk size and overlap.
+
+```python Code
+knowledge_source = StringKnowledgeSource(
+    content="Long content...",
+    chunk_size=4000,     # Characters per chunk (default)
+    chunk_overlap=200    # Overlap between chunks (default)
+)
+```
+
+## Embedder Configuration
+
+You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
+
+```python Code
+...
+string_source = StringKnowledgeSource(
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
+)
+crew = Crew(
+    ...
+    knowledge_sources=[string_source],
+    embedder={
+        "provider": "openai",
+        "config": {"model": "text-embedding-3-small"},
+    },
+)
+```
+
+## Clearing Knowledge
+
+If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.
+
+```bash Command
+crewai reset-memories --knowledge
+```
+
+This is useful when you've updated your knowledge sources and want to ensure that the agents are using the most recent information.
+
+## Custom Knowledge Sources
+
+CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles.
+
+#### Space News Knowledge Source Example
+
+<CodeGroup>
+
+```python Code
+from crewai import Agent, Task, Crew, Process, LLM
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+import requests
+from datetime import datetime
+from typing import Dict, Any
+from pydantic import BaseModel, Field
+
+class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
+    """Knowledge source that fetches data from Space News API."""
+
+    api_endpoint: str = Field(description="API endpoint URL")
+    limit: int = Field(default=10, description="Number of articles to fetch")
+
+    def load_content(self) -> Dict[Any, str]:
+        """Fetch and format space news articles."""
+        try:
+            response = requests.get(
+                f"{self.api_endpoint}?limit={self.limit}"
+            )
+            response.raise_for_status()
+
+            data = response.json()
+            articles = data.get('results', [])
+
+            formatted_data = self._format_articles(articles)
+            return {self.api_endpoint: formatted_data}
+        except Exception as e:
+            raise ValueError(f"Failed to fetch space news: {str(e)}")
+
+    def _format_articles(self, articles: list) -> str:
+        """Format articles into readable text."""
+        formatted = "Space News Articles:\n\n"
+        for article in articles:
+            formatted += f"""
+                Title: {article['title']}
+                Published: {article['published_at']}
+                Summary: {article['summary']}
+                News Site: {article['news_site']}
+                URL: {article['url']}
+                -------------------"""
+        return formatted
+
+    def add(self) -> None:
+        """Process and store the articles."""
+        content = self.load_content()
+        for _, text in content.items():
+            chunks = self._chunk_text(text)
+            self.chunks.extend(chunks)
+
+        self._save_documents()
+
+# Create knowledge source
+recent_news = SpaceNewsKnowledgeSource(
+    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
+    limit=10,
+)
+
+# Create specialized agent
+space_analyst = Agent(
+    role="Space News Analyst",
+    goal="Answer questions about space news accurately and comprehensively",
+    backstory="""You are a space industry analyst with expertise in space exploration,
+    satellite technology, and space industry trends. You excel at answering questions
+    about space news and providing detailed, accurate information.""",
+    knowledge_sources=[recent_news],
+    llm=LLM(model="gpt-4", temperature=0.0)
+)
+
+# Create task that handles user questions
+analysis_task = Task(
+    description="Answer this question about space news: {user_question}",
+    expected_output="A detailed answer based on the recent space news articles",
+    agent=space_analyst
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[space_analyst],
+    tasks=[analysis_task],
+    verbose=True,
+    process=Process.sequential
+)
+
+# Example usage
+result = crew.kickoff(
+    inputs={"user_question": "What are the latest developments in space exploration?"}
+)
+```
+
+```output Output
+# Agent: Space News Analyst
+## Task: Answer this question about space news: What are the latest developments in space exploration?
+
+
+# Agent: Space News Analyst
+## Final Answer:
+The latest developments in space exploration, based on recent space news articles, include the following:
+
+1. SpaceX has received the final regulatory approvals to proceed with the second integrated Starship/Super Heavy launch, scheduled for as soon as the morning of Nov. 17, 2023. This is a significant step in SpaceX's ambitious plans for space exploration and colonization. [Source: SpaceNews](https://spacenews.com/starship-cleared-for-nov-17-launch/)
+
+2. SpaceX has also informed the US Federal Communications Commission (FCC) that it plans to begin launching its first next-generation Starlink Gen2 satellites. This represents a major upgrade to the Starlink satellite internet service, which aims to provide high-speed internet access worldwide. [Source: Teslarati](https://www.teslarati.com/spacex-first-starlink-gen2-satellite-launch-2022/)
+
+3. AI startup Synthetaic has raised $15 million in Series B funding. The company uses artificial intelligence to analyze data from space and air sensors, which could have significant applications in space exploration and satellite technology. [Source: SpaceNews](https://spacenews.com/ai-startup-synthetaic-raises-15-million-in-series-b-funding/)
+
+4. The Space Force has formally established a unit within the U.S. Indo-Pacific Command, marking a permanent presence in the Indo-Pacific region. This could have significant implications for space security and geopolitics. [Source: SpaceNews](https://spacenews.com/space-force-establishes-permanent-presence-in-indo-pacific-region/)
+
+5. Slingshot Aerospace, a space tracking and data analytics company, is expanding its network of ground-based optical telescopes to increase coverage of low Earth orbit. This could improve our ability to track and analyze objects in low Earth orbit, including satellites and space debris. [Source: SpaceNews](https://spacenews.com/slingshots-space-tracking-network-to-extend-coverage-of-low-earth-orbit/)
+
+6. The National Natural Science Foundation of China has outlined a five-year project for researchers to study the assembly of ultra-large spacecraft. This could lead to significant advancements in spacecraft technology and space exploration capabilities. [Source: SpaceNews](https://spacenews.com/china-researching-challenges-of-kilometer-scale-ultra-large-spacecraft/)
+
+7. The Center for AEroSpace Autonomy Research (CAESAR) at Stanford University is focusing on spacecraft autonomy. The center held a kickoff event on May 22, 2024, to highlight the industry, academia, and government collaboration it seeks to foster. This could lead to significant advancements in autonomous spacecraft technology. [Source: SpaceNews](https://spacenews.com/stanford-center-focuses-on-spacecraft-autonomy/)
+```
+
+</CodeGroup>
+#### Key Components Explained
+
+1. **Custom Knowledge Source (`SpaceNewsKnowledgeSource`)**:
+
+   - Extends `BaseKnowledgeSource` for integration with CrewAI
+   - Configurable API endpoint and article limit
+   - Implements three key methods:
+     - `load_content()`: Fetches articles from the API
+     - `_format_articles()`: Structures the articles into readable text
+     - `add()`: Processes and stores the content
+
+2. **Agent Configuration**:
+
+   - Specialized role as a Space News Analyst
+   - Uses the knowledge source to access space news
+
+3. **Task Setup**:
+
+   - Takes a user question as input through `{user_question}`
+   - Designed to provide detailed answers based on the knowledge source
+
+4. **Crew Orchestration**:
+   - Manages the workflow between agent and task
+   - Handles input/output through the kickoff method
+
+This example demonstrates how to:
+
+- Create a custom knowledge source that fetches real-time data
+- Process and format external data for AI consumption
+- Use the knowledge source to answer specific user questions
+- Integrate everything seamlessly with CrewAI's agent system
+
+#### About the Spaceflight News API
+
+The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/docs/), which:
+
+- Provides free access to space-related news articles
+- Requires no authentication
+- Returns structured data about space news
+- Supports pagination and filtering
+
+You can customize the API query by modifying the endpoint URL:
+
+```python
+# Fetch more articles
+recent_news = SpaceNewsKnowledgeSource(
+    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
+    limit=20,  # Increase the number of articles
+)
+
+# Add search parameters
+recent_news = SpaceNewsKnowledgeSource(
+    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news
+    limit=10,
+)
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Content Organization">
+    - Keep chunk sizes appropriate for your content type
+    - Consider content overlap for context preservation
+    - Organize related information into separate knowledge sources
+  </Accordion>
+
+  <Accordion title="Performance Tips">
+    - Adjust chunk sizes based on content complexity 
+    - Configure appropriate embedding models
+    - Consider using local embedding providers for faster processing
+  </Accordion>
+</AccordionGroup>

docs/concepts/langchain-tools.mdx

@@ -7,32 +7,45 @@ icon: link
 ## Using LangChain Tools
 
 <Info>
-    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 [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.
 </Info>
 
 ```python Code
 import os
-from crewai import Agent
-from langchain.agents import Tool
-from langchain.utilities import GoogleSerperAPIWrapper
+from dotenv import load_dotenv
+from crewai import Agent, Task, Crew
+from crewai.tools import BaseTool
+from pydantic import Field
+from langchain_community.utilities import GoogleSerperAPIWrapper
 
-# Setup API keys
-os.environ["SERPER_API_KEY"] = "Your Key"
+# Set up your SERPER_API_KEY key in an .env file, eg:
+# SERPER_API_KEY=<your api key>
+load_dotenv()
 
 search = GoogleSerperAPIWrapper()
 
-# Create and assign the search tool to an agent
-serper_tool = Tool(
-  name="Intermediate Answer",
-  func=search.run,
-  description="Useful for search-based queries",
-)
+class SearchTool(BaseTool):
+    name: str = "Search"
+    description: str = "Useful for search-based queries. Use this to find current information about markets, companies, and trends."
+    search: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper)
 
-agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
-  tools=[serper_tool]
+    def _run(self, query: str) -> str:
+        """Execute the search query and return results"""
+        try:
+            return self.search.run(query)
+        except Exception as e:
+            return f"Error performing search: {str(e)}"
+
+# Create Agents
+researcher = Agent(
+    role='Research Analyst',
+    goal='Gather current market data and trends',
+    backstory="""You are an expert research analyst with years of experience in
+    gathering market intelligence. You're known for your ability to find
+    relevant and up-to-date market information and present it in a clear,
+    actionable format.""",
+    tools=[SearchTool()],
+    verbose=True
 )
 
 # rest of the code ...
@@ -40,6 +53,6 @@ agent = Agent(
 
 ## Conclusion
 
-Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. 
-When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, 
-and the flexibility of tool arguments to optimize your agents' performance and capabilities.
+Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
+When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms,
+and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/concepts/llms.mdx

@@ -1,180 +1,650 @@
 ---
-title: LLMs
-description: Learn how to configure and optimize LLMs for your CrewAI projects.
-icon: microchip-ai
+title: 'LLMs'
+description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
+icon: 'microchip-ai'
 ---
 
-# Large Language Models (LLMs) in CrewAI
+<Note>
+  CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
+</Note>
 
-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.
+## What are LLMs?
 
-## Key Concepts
+Large Language Models (LLMs) are the core intelligence behind CrewAI agents. They enable agents to understand context, make decisions, and generate human-like responses. Here's what you need to know:
 
-- **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))
+<CardGroup cols={2}>
+  <Card title="LLM Basics" icon="brain">
+    Large Language Models are AI systems trained on vast amounts of text data. They power the intelligence of your CrewAI agents, enabling them to understand and generate human-like text.
+  </Card>
+  <Card title="Context Window" icon="window">
+    The context window determines how much text an LLM can process at once. Larger windows (e.g., 128K tokens) allow for more context but may be more expensive and slower.
+  </Card>
+  <Card title="Temperature" icon="temperature-three-quarters">
+    Temperature (0.0 to 1.0) controls response randomness. Lower values (e.g., 0.2) produce more focused, deterministic outputs, while higher values (e.g., 0.8) increase creativity and variability.
+  </Card>
+  <Card title="Provider Selection" icon="server">
+    Each LLM provider (e.g., OpenAI, Anthropic, Google) offers different models with varying capabilities, pricing, and features. Choose based on your needs for accuracy, speed, and cost.
+  </Card>
+</CardGroup>
 
-## Configuring LLMs for Agents
+## Available Models and Their Capabilities
 
-CrewAI offers flexible options for setting up LLMs:
+Here's a detailed breakdown of supported models and their capabilities, you can compare performance at [lmarena.ai](https://lmarena.ai/):
 
-### 1. Default Configuration
+<Tabs>
+  <Tab title="OpenAI">
+    | Model | Context Window | Best For |
+    |-------|---------------|-----------|
+    | GPT-4 | 8,192 tokens | High-accuracy tasks, complex reasoning |
+    | GPT-4 Turbo | 128,000 tokens | Long-form content, document analysis |
+    | GPT-4o & GPT-4o-mini | 128,000 tokens | Cost-effective large context processing |
 
-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`
+    <Note>
+      1 token ≈ 4 characters in English. For example, 8,192 tokens ≈ 32,768 characters or about 6,000 words.
+    </Note>
+  </Tab>
+  <Tab title="Gemini">
+    | Model | Context Window | Best For |
+    |-------|---------------|-----------|
+    | Gemini 1.5 Flash | 1M tokens | Balanced multimodal model, good for most tasks |
+    | Gemini 1.5 Flash 8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks |
+    | Gemini 1.5 Pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
 
-### 2. String Identifier
+    <Tip>
+      Google's Gemini models are all multimodal, supporting audio, images, video and text, supporting context caching, json schema, function calling, etc.
+    </Tip>
+  </Tab>
+  <Tab title="Groq">
+    | Model | Context Window | Best For |
+    |-------|---------------|-----------|
+    | Llama 3.1 70B/8B | 131,072 tokens | High-performance, large context tasks |
+    | Llama 3.2 Series | 8,192 tokens | General-purpose tasks |
+    | Mixtral 8x7B | 32,768 tokens | Balanced performance and context |
+    | Gemma Series | 8,192 tokens | Efficient, smaller-scale tasks |
 
-```python Code
-agent = Agent(llm="gpt-4o", ...)
-```
+    <Tip>
+      Groq is known for its fast inference speeds, making it suitable for real-time applications.
+    </Tip>
+  </Tab>
+  <Tab title="Others">
+    | Provider | Context Window | Key Features |
+    |----------|---------------|--------------|
+    | Deepseek Chat | 128,000 tokens | Specialized in technical discussions |
+    | Claude 3 | Up to 200K tokens | Strong reasoning, code understanding |
+    | Gemini | Varies by model | Multimodal capabilities |
 
-### 3. LLM Instance
+    <Info>
+      Provider selection should consider factors like:
+      - API availability in your region
+      - Pricing structure
+      - Required features (e.g., streaming, function calling)
+      - Performance requirements
+    </Info>
+  </Tab>
+</Tabs>
 
-List of [more providers](https://docs.litellm.ai/docs/providers).
+## Setting Up Your LLM
 
-```python Code
-from crewai import LLM
+There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
 
-llm = LLM(model="gpt-4", temperature=0.7)
-agent = Agent(llm=llm, ...)
-```
+<Tabs>
+  <Tab title="1. Environment Variables">
+    The simplest way to get started. Set these variables in your environment:
 
-### 4. Custom LLM Objects
+    ```bash
+    # Required: Your API key for authentication
+    OPENAI_API_KEY=<your-api-key>
 
-Pass a custom LLM implementation or object from another library.
+    # Optional: Default model selection
+    OPENAI_MODEL_NAME=gpt-4o-mini  # Default if not set
 
-## Connecting to OpenAI-Compatible LLMs
+    # Optional: Organization ID (if applicable)
+    OPENAI_ORGANIZATION_ID=<your-org-id>
+    ```
 
-You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
+    <Warning>
+      Never commit API keys to version control. Use environment files (.env) or your system's secret management.
+    </Warning>
+  </Tab>
+  <Tab title="2. YAML Configuration">
+    Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:
 
-1. Using environment variables:
+    ```yaml
+    researcher:
+        # Agent Definition
+        role: Research Specialist
+        goal: Conduct comprehensive research and analysis
+        backstory: A dedicated research professional with years of experience
+        verbose: true
 
-```python Code
-import os
+        # Model Selection (uncomment your choice)
+        
+        # OpenAI Models - Known for reliability and performance
+        llm: openai/gpt-4o-mini
+        # llm: openai/gpt-4        # More accurate but expensive
+        # llm: openai/gpt-4-turbo  # Fast with large context
+        # llm: openai/gpt-4o       # Optimized for longer texts
+        # llm: openai/o1-preview   # Latest features
+        # llm: openai/o1-mini      # Cost-effective
 
-os.environ["OPENAI_API_KEY"] = "your-api-key"
-os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
-```
+        # Azure Models - For enterprise deployments
+        # llm: azure/gpt-4o-mini
+        # llm: azure/gpt-4
+        # llm: azure/gpt-35-turbo
 
-2. Using LLM class attributes:
+        # Anthropic Models - Strong reasoning capabilities
+        # llm: anthropic/claude-3-opus-20240229-v1:0
+        # llm: anthropic/claude-3-sonnet-20240229-v1:0
+        # llm: anthropic/claude-3-haiku-20240307-v1:0
+        # llm: anthropic/claude-2.1
+        # llm: anthropic/claude-2.0
 
-```python Code
-from crewai import LLM
+        # Google Models - Strong reasoning, large cachable context window, multimodal
+        # llm: gemini/gemini-1.5-pro-latest
+        # llm: gemini/gemini-1.5-flash-latest
+        # llm: gemini/gemini-1.5-flash-8b-latest
 
-llm = LLM(
-    model="custom-model-name",
-    api_key="your-api-key",
-    base_url="https://api.your-provider.com/v1"
-)
-agent = Agent(llm=llm, ...)
-```
+        # AWS Bedrock Models - Enterprise-grade
+        # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
+        # llm: bedrock/anthropic.claude-v2:1
+        # llm: bedrock/amazon.titan-text-express-v1
+        # llm: bedrock/meta.llama2-70b-chat-v1
 
-## LLM Configuration Options
+        # Mistral Models - Open source alternative
+        # llm: mistral/mistral-large-latest
+        # llm: mistral/mistral-medium-latest
+        # llm: mistral/mistral-small-latest
 
-When configuring an LLM for your agent, you have access to a wide range of parameters:
+        # Groq Models - Fast inference
+        # llm: groq/mixtral-8x7b-32768
+        # llm: groq/llama-3.1-70b-versatile
+        # llm: groq/llama-3.2-90b-text-preview
+        # llm: groq/gemma2-9b-it
+        # llm: groq/gemma-7b-it
 
-| Parameter         | Type            | Description                                                                                      |
-|:------------------|:---------------:|:-------------------------------------------------------------------------------------------------|
-| **model**            | `str`             | Name of the model to use (e.g., "gpt-4", "gpt-3.5-turbo", "ollama/llama3.1"). For more options, visit the providers documentation. |
-| **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) where generation should stop.                                                        |
-| **max_tokens**       | `int`             | Maximum number of tokens to generate.                                                            |
-| **presence_penalty** | `float`           | Penalizes new tokens based on their presence in prior text.                                      |
-| **frequency_penalty**| `float`           | Penalizes new tokens based on their frequency in prior text.                                     |
-| **logit_bias**       | `Dict[int, float]`| Modifies likelihood of specified tokens appearing.                                              |
-| **response_format**  | `Dict[str, Any]`  | Specifies the format of the response (e.g., JSON object).                                        |
-| **seed**             | `int`             | Sets a random seed for deterministic results.                                                    |
-| **logprobs**         | `bool`            | Returns log probabilities of output tokens if enabled.                                           |
-| **top_logprobs**     | `int`             | Number of most likely tokens for which to return log probabilities.                              |
-| **base_url**         | `str`             | The base URL for the API endpoint.                                                               |
-| **api_version**      | `str`             | Version of the API to use.                                                                       |
-| **api_key**          | `str`             | Your API key for authentication.                                                                 |
+        # IBM watsonx.ai Models - Enterprise features
+        # llm: watsonx/ibm/granite-13b-chat-v2
+        # llm: watsonx/meta-llama/llama-3-1-70b-instruct
+        # llm: watsonx/bigcode/starcoder2-15b
 
+        # Ollama Models - Local deployment
+        # llm: ollama/llama3:70b
+        # llm: ollama/codellama
+        # llm: ollama/mistral
+        # llm: ollama/mixtral
+        # llm: ollama/phi
 
-## OpenAI Example Configuration
+        # Fireworks AI Models - Specialized tasks
+        # llm: fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct
+        # llm: fireworks_ai/accounts/fireworks/models/mixtral-8x7b
+        # llm: fireworks_ai/accounts/fireworks/models/zephyr-7b-beta
 
-```python Code
-from crewai import LLM
+        # Perplexity AI Models - Research focused
+        # llm: pplx/llama-3.1-sonar-large-128k-online
+        # llm: pplx/mistral-7b-instruct
+        # llm: pplx/codellama-34b-instruct
+        # llm: pplx/mixtral-8x7b-instruct
 
-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, ...)
-```
+        # Hugging Face Models - Community models
+        # llm: huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct
+        # llm: huggingface/mistralai/Mixtral-8x7B-Instruct-v0.1
+        # llm: huggingface/tiiuae/falcon-180B-chat
+        # llm: huggingface/google/gemma-7b-it
 
-## Cerebras Example Configuration
+        # Nvidia NIM Models - GPU-optimized
+        # llm: nvidia_nim/meta/llama3-70b-instruct
+        # llm: nvidia_nim/mistral/mixtral-8x7b
+        # llm: nvidia_nim/google/gemma-7b
 
-```python Code
-from crewai import LLM
+        # SambaNova Models - Enterprise AI
+        # llm: sambanova/Meta-Llama-3.1-8B-Instruct
+        # llm: sambanova/BioMistral-7B
+        # llm: sambanova/Falcon-180B
+    ```
 
-llm = LLM(
-    model="cerebras/llama-3.1-70b",
-    base_url="https://api.cerebras.ai/v1",
-    api_key="your-api-key-here"
-)
-agent = Agent(llm=llm, ...) 
-```
+    <Info>
+      The YAML configuration allows you to:
+      - Version control your agent settings
+      - Easily switch between different models
+      - Share configurations across team members
+      - Document model choices and their purposes
+    </Info>
+  </Tab>
+  <Tab title="3. Direct Code">
+    For maximum flexibility, configure LLMs directly in your Python code:
 
-## Using Ollama (Local LLMs)
+    ```python
+    from crewai import LLM
 
-CrewAI supports using Ollama for running open-source models locally:
+    # Basic configuration
+    llm = LLM(model="gpt-4")
 
-1. Install Ollama: [ollama.ai](https://ollama.ai/)
-2. Run a model: `ollama run llama2`
-3. Configure agent:
+    # Advanced configuration with detailed parameters
+    llm = LLM(
+        model="gpt-4o-mini",
+        temperature=0.7,        # Higher for more creative outputs
+        timeout=120,           # Seconds to wait for response
+        max_tokens=4000,       # Maximum length of response
+        top_p=0.9,            # Nucleus sampling parameter
+        frequency_penalty=0.1, # Reduce repetition
+        presence_penalty=0.1,  # Encourage topic diversity
+        response_format={"type": "json"},  # For structured outputs
+        seed=42               # For reproducible results
+    )
+    ```
 
-```python Code
-from crewai import LLM
+    <Info>
+      Parameter explanations:
+      - `temperature`: Controls randomness (0.0-1.0)
+      - `timeout`: Maximum wait time for response
+      - `max_tokens`: Limits response length
+      - `top_p`: Alternative to temperature for sampling
+      - `frequency_penalty`: Reduces word repetition
+      - `presence_penalty`: Encourages new topics
+      - `response_format`: Specifies output structure
+      - `seed`: Ensures consistent outputs
+    </Info>
+  </Tab>
+</Tabs>
 
-agent = Agent(
-    llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
-    ...
-)
-```
+## Advanced Features and Optimization
 
-## Changing the Base API URL
+Learn how to get the most out of your LLM configuration:
 
-You can change the base API URL for any LLM provider by setting the `base_url` parameter:
+<AccordionGroup>
+  <Accordion title="Context Window Management">
+    CrewAI includes smart context management features:
 
-```python Code
-from crewai import LLM
+    ```python
+    from crewai import LLM
 
-llm = LLM(
-    model="custom-model-name",
-    base_url="https://api.your-provider.com/v1",
-    api_key="your-api-key"
-)
-agent = Agent(llm=llm, ...)
-```
+    # CrewAI automatically handles:
+    # 1. Token counting and tracking
+    # 2. Content summarization when needed
+    # 3. Task splitting for large contexts
 
-This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
+    llm = LLM(
+        model="gpt-4",
+        max_tokens=4000,  # Limit response length
+    )
+    ```
 
-## Best Practices
+    <Info>
+      Best practices for context management:
+      1. Choose models with appropriate context windows
+      2. Pre-process long inputs when possible
+      3. Use chunking for large documents
+      4. Monitor token usage to optimize costs
+    </Info>
+  </Accordion>
 
-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.
+  <Accordion title="Performance Optimization">
+    <Steps>
+      <Step title="Token Usage Optimization">
+        Choose the right context window for your task:
+        - Small tasks (up to 4K tokens): Standard models
+        - Medium tasks (between 4K-32K): Enhanced models
+        - Large tasks (over 32K): Large context models
+        
+        ```python
+        # Configure model with appropriate settings
+        llm = LLM(
+            model="openai/gpt-4-turbo-preview",
+            temperature=0.7,    # Adjust based on task
+            max_tokens=4096,    # Set based on output needs
+            timeout=300        # Longer timeout for complex tasks
+        )
+        ```
+        <Tip>
+          - Lower temperature (0.1 to 0.3) for factual responses
+          - Higher temperature (0.7 to 0.9) for creative tasks
+        </Tip>
+      </Step>
 
-## Troubleshooting
+      <Step title="Best Practices">
+        1. Monitor token usage
+        2. Implement rate limiting
+        3. Use caching when possible
+        4. Set appropriate max_tokens limits
+      </Step>
+    </Steps>
 
-- **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.
+    <Info>
+      Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
+    </Info>
+  </Accordion>
+</AccordionGroup>
+
+## Provider Configuration Examples
+
+<AccordionGroup>
+  <Accordion title="OpenAI">
+    ```python Code
+    # Required
+    OPENAI_API_KEY=sk-...
+    
+    # Optional
+    OPENAI_API_BASE=<custom-base-url>
+    OPENAI_ORGANIZATION=<your-org-id>
+    ```
+
+    Example usage:
+    ```python Code
+    from crewai import LLM
+
+    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
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Anthropic">
+    ```python Code
+    ANTHROPIC_API_KEY=sk-ant-...
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="anthropic/claude-3-sonnet-20240229-v1:0",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Google">
+    ```python Code
+    # Option 1. Gemini accessed with an API key.
+    # https://ai.google.dev/gemini-api/docs/api-key
+    GEMINI_API_KEY=<your-api-key>
+
+    # Option 2. Vertex AI IAM credentials for Gemini, Anthropic, and anything in the Model Garden.
+    # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="gemini/gemini-1.5-pro-latest",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Azure">
+    ```python Code
+    # Required
+    AZURE_API_KEY=<your-api-key>
+    AZURE_API_BASE=<your-resource-url>
+    AZURE_API_VERSION=<api-version>
+    
+    # Optional
+    AZURE_AD_TOKEN=<your-azure-ad-token>
+    AZURE_API_TYPE=<your-azure-api-type>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="azure/gpt-4",
+        api_version="2023-05-15"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="AWS Bedrock">
+    ```python Code
+    AWS_ACCESS_KEY_ID=<your-access-key>
+    AWS_SECRET_ACCESS_KEY=<your-secret-key>
+    AWS_DEFAULT_REGION=<your-region>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Mistral">
+    ```python Code
+    MISTRAL_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="mistral/mistral-large-latest",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Groq">
+    ```python Code
+    GROQ_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="groq/llama-3.2-90b-text-preview",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="IBM watsonx.ai">
+    ```python Code
+    # Required
+    WATSONX_URL=<your-url>
+    WATSONX_APIKEY=<your-apikey>
+    WATSONX_PROJECT_ID=<your-project-id>
+    
+    # Optional
+    WATSONX_TOKEN=<your-token>
+    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="watsonx/meta-llama/llama-3-1-70b-instruct",
+        base_url="https://api.watsonx.ai/v1"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Ollama (Local LLMs)">
+    1. Install Ollama: [ollama.ai](https://ollama.ai/)
+    2. Run a model: `ollama run llama2`
+    3. Configure:
+
+    ```python Code
+    llm = LLM(
+        model="ollama/llama3:70b",
+        base_url="http://localhost:11434"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Fireworks AI">
+    ```python Code
+    FIREWORKS_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Perplexity AI">
+    ```python Code
+    PERPLEXITY_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="llama-3.1-sonar-large-128k-online",
+        base_url="https://api.perplexity.ai/"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Hugging Face">
+    ```python Code
+    HUGGINGFACE_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct",
+        base_url="your_api_endpoint"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Nvidia NIM">
+    ```python Code
+    NVIDIA_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="nvidia_nim/meta/llama3-70b-instruct",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="SambaNova">
+    ```python Code
+    SAMBANOVA_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="sambanova/Meta-Llama-3.1-8B-Instruct",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Cerebras">
+    ```python Code
+    # Required
+    CEREBRAS_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="cerebras/llama3.1-70b",
+        temperature=0.7,
+        max_tokens=8192
+    )
+    ```
+
+    <Info>
+      Cerebras features:
+      - Fast inference speeds
+      - Competitive pricing
+      - Good balance of speed and quality
+      - Support for long context windows
+    </Info>
+  </Accordion>
+</AccordionGroup>
+
+## Common Issues and Solutions
+
+<Tabs>
+  <Tab title="Authentication">
+    <Warning>
+      Most authentication issues can be resolved by checking API key format and environment variable names.
+    </Warning>
+    
+    ```bash
+    # OpenAI
+    OPENAI_API_KEY=sk-...
+    
+    # Anthropic
+    ANTHROPIC_API_KEY=sk-ant-...
+    ```
+  </Tab>
+  <Tab title="Model Names">
+    <Check>
+      Always include the provider prefix in model names
+    </Check>
+    
+    ```python
+    # Correct
+    llm = LLM(model="openai/gpt-4")
+    
+    # Incorrect
+    llm = LLM(model="gpt-4")
+    ```
+  </Tab>
+  <Tab title="Context Length">
+    <Tip>
+      Use larger context models for extensive tasks
+    </Tip>
+    
+    ```python
+    # Large context model
+    llm = LLM(model="openai/gpt-4o")  # 128K tokens
+    ```
+  </Tab>
+</Tabs>
+
+## Getting Help
+
+If you need assistance, these resources are available:
+
+<CardGroup cols={3}>
+  <Card
+    title="LiteLLM Documentation"
+    href="https://docs.litellm.ai/docs/"
+    icon="book"
+  >
+    Comprehensive documentation for LiteLLM integration and troubleshooting common issues.
+  </Card>
+  <Card
+    title="GitHub Issues"
+    href="https://github.com/joaomdmoura/crewAI/issues"
+    icon="bug"
+  >
+    Report bugs, request features, or browse existing issues for solutions.
+  </Card>
+  <Card
+    title="Community Forum"
+    href="https://community.crewai.com"
+    icon="comment-question"
+  >
+    Connect with other CrewAI users, share experiences, and get help from the community.
+  </Card>
+</CardGroup>
+
+<Note>
+  Best Practices for API Key Security:
+  - Use environment variables or secure vaults
+  - Never commit keys to version control
+  - Rotate keys regularly
+  - Use separate keys for development and production
+  - Monitor key usage for unusual patterns
+</Note>

docs/concepts/memory.mdx

@@ -18,6 +18,7 @@ reason, and learn from past interactions.
 | **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
 | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
+| **User Memory**      | Stores user-specific information and preferences, enhancing personalization and user experience. |
 
 ## How Memory Systems Empower Agents
 
@@ -92,6 +93,47 @@ my_crew = Crew(
 )
 ```
 
+## Integrating Mem0 for Enhanced User Memory
+
+[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
+
+To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
+
+
+```python Code
+import os
+from crewai import Crew, Process
+from mem0 import MemoryClient
+
+# Set environment variables for Mem0
+os.environ["MEM0_API_KEY"] = "m0-xx"
+
+# Step 1: Record preferences based on past conversation or user input
+client = MemoryClient()
+messages = [
+    {"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
+    {"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
+    {"role": "user", "content": "I am more of a beach person than a mountain person."},
+    {"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
+    {"role": "user", "content": "I like Airbnb more."},
+]
+client.add(messages, user_id="john")
+
+# Step 2: Create a Crew with User Memory
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    verbose=True,
+    process=Process.sequential,
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "john"},
+    },
+)
+```
+
 
 ## Additional Embedding Providers
 
@@ -105,9 +147,48 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=embedding_functions.OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-        )
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": 'text-embedding-3-small'
+        }
+    }
+)
+```
+Alternatively, you can directly pass the OpenAIEmbeddingFunction to the embedder parameter.
+
+Example:
+```python Code
+from crewai import Crew, Agent, Task, Process
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
+)
+```
+
+### Using Ollama embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large"
+        }
+    }
 )
 ```
 
@@ -122,16 +203,20 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=embedding_functions.OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model_name="text-embedding-ada-002"
-    )
+    embedder={
+        "provider": "google",
+        "config": {
+            "api_key": "<YOUR_API_KEY>",
+            "model_name": "<model_name>"
+        }
+    }
 )
 ```
 
 ### Using Azure OpenAI embeddings
 
 ```python Code
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
@@ -140,7 +225,7 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=embedding_functions.OpenAIEmbeddingFunction(
+    embedder=OpenAIEmbeddingFunction(
         api_key="YOUR_API_KEY",
         api_base="YOUR_API_BASE_PATH",
         api_type="azure",
@@ -153,6 +238,7 @@ my_crew = Crew(
 ### Using Vertex AI embeddings
 
 ```python Code
+from chromadb.utils.embedding_functions import GoogleVertexEmbeddingFunction
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
@@ -161,7 +247,7 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=embedding_functions.GoogleVertexEmbeddingFunction(
+    embedder=GoogleVertexEmbeddingFunction(
         project_id="YOUR_PROJECT_ID",
         region="YOUR_REGION",
         api_key="YOUR_API_KEY",
@@ -181,10 +267,57 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=embedding_functions.CohereEmbeddingFunction(
-        api_key=YOUR_API_KEY,
-        model_name="<model_name>"
-    )
+    embedder={
+        "provider": "cohere",
+        "config": {
+            "api_key": "YOUR_API_KEY",
+            "model_name": "<model_name>"
+        }
+    }
+)
+```
+### Using HuggingFace embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "huggingface",
+        "config": {
+            "api_url": "<api_url>",
+        }
+    }
+)
+```
+
+### Using Watson embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "watson",
+        "config": {
+            "model": "<model_name>",
+            "api_url": "<api_url>",
+            "api_key": "<YOUR_API_KEY>",
+            "project_id": "<YOUR_PROJECT_ID>",
+        }
+    }
 )
 ```
 

docs/concepts/tasks.mdx

@@ -1,6 +1,6 @@
 ---
 title: Tasks
-description: Detailed guide on managing and creating tasks within the CrewAI framework, reflecting the latest codebase updates.
+description: Detailed guide on managing and creating tasks within the CrewAI framework.
 icon: list-check
 ---
 
@@ -8,41 +8,171 @@ icon: list-check
 
 In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`. 
 
-They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
-
+Tasks provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
 
 Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
+### Task Execution Flow
+
+Tasks can be executed in two ways:
+- **Sequential**: Tasks are executed in the order they are defined
+- **Hierarchical**: Tasks are assigned to agents based on their roles and expertise
+
+The execution flow is defined when creating the crew:
+```python Code
+crew = Crew(
+    agents=[agent1, agent2],
+    tasks=[task1, task2],
+    process=Process.sequential  # or Process.hierarchical
+)
+```
+
 ## 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.                                                |
+| **Name** _(optional)_           | `name`           | `Optional[str]`               | A name identifier for the task.                                                                                      |
+| **Agent** _(optional)_          | `agent`           | `Optional[BaseAgent]`         | The agent responsible for executing the task.                                                                        |
+| **Tools** _(optional)_          | `tools`           | `List[BaseTool]`             | The tools/resources the agent is limited to use for this task.                                                       |
+| **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Other tasks whose outputs will be used as context for this task.                                                     |
+| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | Whether the task should be executed asynchronously. Defaults to False.                                               |
+| **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
+| **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
+| **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
+| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
+| **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
 
-## Creating a Task
+## Creating Tasks
 
-Creating a task involves defining its scope, responsible agent, and any additional attributes for flexibility:
+There are two ways to create tasks in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
 
+### YAML Configuration (Recommended)
+
+Using YAML configuration provides a cleaner, more maintainable way to define tasks. We strongly recommend using this approach to define tasks in your CrewAI projects.
+
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
+
+<Note>
+Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
 ```python Code
+crew.kickoff(inputs={'topic': 'AI Agents'})
+```
+</Note>
+
+Here's an example of how to configure tasks using YAML:
+
+```yaml tasks.yaml
+research_task:
+  description: >
+    Conduct a thorough research about {topic}
+    Make sure you find any interesting and relevant information given
+    the current year is 2024.
+  expected_output: >
+    A list with 10 bullet points of the most relevant information about {topic}
+  agent: researcher
+
+reporting_task:
+  description: >
+    Review the context you got and expand each topic into a full section for a report.
+    Make sure the report is detailed and contains any and all relevant information.
+  expected_output: >
+    A fully fledge reports with the mains topics, each with a full section of information.
+    Formatted as markdown without '```'
+  agent: reporting_analyst
+  output_file: report.md
+```
+
+To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
+
+```python crew.py
+# src/latest_ai_development/crew.py
+
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+from crewai_tools import SerperDevTool
+
+@CrewBase
+class LatestAiDevelopmentCrew():
+  """LatestAiDevelopment crew"""
+
+  @agent
+  def researcher(self) -> Agent:
+    return Agent(
+      config=self.agents_config['researcher'],
+      verbose=True,
+      tools=[SerperDevTool()]
+    )
+
+  @agent
+  def reporting_analyst(self) -> Agent:
+    return Agent(
+      config=self.agents_config['reporting_analyst'],
+      verbose=True
+    )
+
+  @task
+  def research_task(self) -> Task:
+    return Task(
+      config=self.tasks_config['research_task']
+    )
+
+  @task
+  def reporting_task(self) -> Task:
+    return Task(
+      config=self.tasks_config['reporting_task']
+    )
+
+  @crew
+  def crew(self) -> Crew:
+    return Crew(
+      agents=[
+        self.researcher(),
+        self.reporting_analyst()
+      ],
+      tasks=[
+        self.research_task(),
+        self.reporting_task()
+      ],
+      process=Process.sequential
+    )
+```
+
+<Note>
+The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
+</Note>
+
+### Direct Code Definition (Alternative)
+
+Alternatively, you can define tasks directly in your code without using YAML configuration:
+
+```python task.py
 from crewai import Task
 
-task = Task(
-    description='Find and summarize the latest and most relevant news on AI',
-    agent=sales_agent,
-    expected_output='A bullet list summary of the top 5 most important AI news',
+research_task = Task(
+    description="""
+        Conduct a thorough research about AI Agents.
+        Make sure you find any interesting and relevant information given
+        the current year is 2024.
+    """,
+    expected_output="""
+        A list with 10 bullet points of the most relevant information about AI Agents
+    """,
+    agent=researcher
+)
+
+reporting_task = Task(
+    description="""
+        Review the context you got and expand each topic into a full section for a report.
+        Make sure the report is detailed and contains any and all relevant information.
+    """,
+    expected_output="""
+        A fully fledge reports with the mains topics, each with a full section of information.
+        Formatted as markdown without '```'
+    """,
+    agent=reporting_analyst,
+    output_file="report.md"
 )
 ```
 
@@ -52,6 +182,8 @@ task = Task(
 
 ## Task Output
 
+Understanding task outputs is crucial for building effective AI workflows. CrewAI provides a structured way to handle task results through the `TaskOutput` class, which supports multiple output formats and can be easily passed between tasks.
+
 The output of a task in CrewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw output, JSON, and Pydantic models.
 
 By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
@@ -112,6 +244,186 @@ if task_output.pydantic:
     print(f"Pydantic Output: {task_output.pydantic}")
 ```
 
+## Task Dependencies and Context
+
+Tasks can depend on the output of other tasks using the `context` attribute. For example:
+
+```python Code
+research_task = Task(
+    description="Research the latest developments in AI",
+    expected_output="A list of recent AI developments",
+    agent=researcher
+)
+
+analysis_task = Task(
+    description="Analyze the research findings and identify key trends",
+    expected_output="Analysis report of AI trends",
+    agent=analyst,
+    context=[research_task]  # This task will wait for research_task to complete
+)
+```
+
+## Getting Structured Consistent Outputs from Tasks
+When you need to ensure that a task outputs a structured and consistent format, you can use the `output_pydantic` or `output_json` properties on a task. These properties allow you to define the expected output structure, making it easier to parse and utilize the results in your application. 
+
+<Note>
+It's also important to note that the output of the final task of a crew becomes the final output of the actual crew itself.
+</Note>
+
+### Using `output_pydantic`
+The `output_pydantic` property allows you to define a Pydantic model that the task output should conform to. This ensures that the output is not only structured but also validated according to the Pydantic model.
+
+Here’s an example demonstrating how to use output_pydantic:
+
+```python Code
+import json
+
+from crewai import Agent, Crew, Process, Task
+from pydantic import BaseModel
+
+
+class Blog(BaseModel):
+    title: str
+    content: str
+
+
+blog_agent = Agent(
+    role="Blog Content Generator Agent",
+    goal="Generate a blog title and content",
+    backstory="""You are an expert content creator, skilled in crafting engaging and informative blog posts.""",
+    verbose=False,
+    allow_delegation=False,
+    llm="gpt-4o",
+)
+
+task1 = Task(
+    description="""Create a blog title and content on a given topic. Make sure the content is under 200 words.""",
+    expected_output="A compelling blog title and well-written content.",
+    agent=blog_agent,
+    output_pydantic=Blog,
+)
+
+# Instantiate your crew with a sequential process
+crew = Crew(
+    agents=[blog_agent],
+    tasks=[task1],
+    verbose=True,
+    process=Process.sequential,
+)
+
+result = crew.kickoff()
+
+# Option 1: Accessing Properties Using Dictionary-Style Indexing
+print("Accessing Properties - Option 1")
+title = result["title"]
+content = result["content"]
+print("Title:", title)
+print("Content:", content)
+
+# Option 2: Accessing Properties Directly from the Pydantic Model
+print("Accessing Properties - Option 2")
+title = result.pydantic.title
+content = result.pydantic.content
+print("Title:", title)
+print("Content:", content)
+
+# Option 3: Accessing Properties Using the to_dict() Method
+print("Accessing Properties - Option 3")
+output_dict = result.to_dict()
+title = output_dict["title"]
+content = output_dict["content"]
+print("Title:", title)
+print("Content:", content)
+
+# Option 4: Printing the Entire Blog Object
+print("Accessing Properties - Option 5")
+print("Blog:", result)
+
+```
+In this example:
+* A Pydantic model Blog is defined with title and content fields.
+* The task task1 uses the output_pydantic property to specify that its output should conform to the Blog model.
+* After executing the crew, you can access the structured output in multiple ways as shown.
+
+#### Explanation of Accessing the Output
+1. Dictionary-Style Indexing: You can directly access the fields using result["field_name"]. This works because the CrewOutput class implements the __getitem__ method.
+2. Directly from Pydantic Model: Access the attributes directly from the result.pydantic object.
+3. Using to_dict() Method: Convert the output to a dictionary and access the fields.
+4. Printing the Entire Object: Simply print the result object to see the structured output.
+
+### Using `output_json`
+The `output_json` property allows you to define the expected output in JSON format. This ensures that the task's output is a valid JSON structure that can be easily parsed and used in your application.
+
+Here’s an example demonstrating how to use `output_json`:
+
+```python Code
+import json
+
+from crewai import Agent, Crew, Process, Task
+from pydantic import BaseModel
+
+
+# Define the Pydantic model for the blog
+class Blog(BaseModel):
+    title: str
+    content: str
+
+
+# Define the agent
+blog_agent = Agent(
+    role="Blog Content Generator Agent",
+    goal="Generate a blog title and content",
+    backstory="""You are an expert content creator, skilled in crafting engaging and informative blog posts.""",
+    verbose=False,
+    allow_delegation=False,
+    llm="gpt-4o",
+)
+
+# Define the task with output_json set to the Blog model
+task1 = Task(
+    description="""Create a blog title and content on a given topic. Make sure the content is under 200 words.""",
+    expected_output="A JSON object with 'title' and 'content' fields.",
+    agent=blog_agent,
+    output_json=Blog,
+)
+
+# Instantiate the crew with a sequential process
+crew = Crew(
+    agents=[blog_agent],
+    tasks=[task1],
+    verbose=True,
+    process=Process.sequential,
+)
+
+# Kickoff the crew to execute the task
+result = crew.kickoff()
+
+# Option 1: Accessing Properties Using Dictionary-Style Indexing
+print("Accessing Properties - Option 1")
+title = result["title"]
+content = result["content"]
+print("Title:", title)
+print("Content:", content)
+
+# Option 2: Printing the Entire Blog Object
+print("Accessing Properties - Option 2")
+print("Blog:", result)
+```
+
+In this example:
+* A Pydantic model Blog is defined with title and content fields, which is used to specify the structure of the JSON output.
+* The task task1 uses the output_json property to indicate that it expects a JSON output conforming to the Blog model.
+* After executing the crew, you can access the structured JSON output in two ways as shown.
+
+#### Explanation of Accessing the Output
+
+1. Accessing Properties Using Dictionary-Style Indexing: You can access the fields directly using result["field_name"]. This is possible because the CrewOutput class implements the __getitem__ method, allowing you to treat the output like a dictionary. In this option, we're retrieving the title and content from the result.
+2. Printing the Entire Blog Object: By printing result, you get the string representation of the CrewOutput object. Since the __str__ method is implemented to return the JSON output, this will display the entire output as a formatted string representing the Blog object.
+
+---
+
+By using output_pydantic or output_json, you ensure that your tasks produce outputs in a consistent and structured format, making it easier to process and utilize the data within your application or across multiple tasks.
+
 ## Integrating Tools with Tasks
 
 Leverage tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) for enhanced task performance and agent interaction.
@@ -167,16 +479,16 @@ This is useful when you have a task that depends on the output of another task t
 # ...
 
 research_ai_task = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
+    description="Research the latest developments in AI",
+    expected_output="A list of recent AI developments",
     async_execution=True,
     agent=research_agent,
     tools=[search_tool]
 )
 
 research_ops_task = Task(
-    description='Find and summarize the latest AI Ops news',
-    expected_output='A bullet list summary of the top 5 most important AI Ops news',
+    description="Research the latest developments in AI Ops",
+    expected_output="A list of recent AI Ops developments",
     async_execution=True,
     agent=research_agent,
     tools=[search_tool]
@@ -184,7 +496,7 @@ research_ops_task = Task(
 
 write_blog_task = Task(
     description="Write a full blog post about the importance of AI and its latest news",
-    expected_output='Full blog post that is 4 paragraphs long',
+    expected_output="Full blog post that is 4 paragraphs long",
     agent=writer_agent,
     context=[research_ai_task, research_ops_task]
 )
@@ -320,4 +632,4 @@ save_output_task = Task(
 Tasks are the driving force behind the actions of agents in CrewAI. 
 By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. 
 Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, 
-ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
+ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/concepts/tools.mdx

@@ -5,13 +5,14 @@ icon: screwdriver-wrench
 ---
 
 ## Introduction
-CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. 
+
+CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
 This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
 
 ## What is a Tool?
 
-A tool in CrewAI is a skill or function that agents can utilize to perform various actions. 
-This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), 
+A tool in CrewAI is a skill or function that agents can utilize to perform various actions.
+This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools),
 enabling everything from simple searches to complex interactions and effective teamwork among agents.
 
 ## Key Characteristics of Tools
@@ -103,57 +104,53 @@ crew.kickoff()
 
 Here is a list of the available tools and their descriptions:
 
-| Tool                        | Description                                                                                   |
-| :-------------------------- | :-------------------------------------------------------------------------------------------- |
-| **BrowserbaseLoadTool**     | A tool for interacting with and extracting data from web browsers.                            |
-| **CodeDocsSearchTool**      | A RAG tool optimized for searching through code documentation and related technical documents. |
-| **CodeInterpreterTool**     | A tool for interpreting python code.                                                          |
-| **ComposioTool**            | Enables use of Composio tools.                                                                |
-| **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
-| **DALL-E Tool**             | A tool for generating images using the DALL-E API.                                            |
-| **DirectorySearchTool**     | A RAG tool for searching within directories, useful for navigating through file systems.      |
-| **DOCXSearchTool**          | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.         |
-| **DirectoryReadTool**       | Facilitates reading and processing of directory structures and their contents.                |
-| **EXASearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
-| **FileReadTool**            | Enables reading and extracting data from files, supporting various file formats.              |
-| **FirecrawlSearchTool**     | A tool to search webpages using Firecrawl and return the results.                             |
-| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl.                                               |
-| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages URL using Firecrawl and returning its contents.              |
-| **GithubSearchTool**        | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
-| **SerperDevTool**           | A specialized tool for development purposes, with specific functionalities under development. |
-| **TXTSearchTool**           | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.     |
-| **JSONSearchTool**          | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
-| **LlamaIndexTool**          | Enables the use of LlamaIndex tools.                                                          |
-| **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
-| **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
-| **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
-| **Vision Tool**             | A tool for generating images using the DALL-E API.                                            |
-| **RagTool**                 | A general-purpose RAG tool capable of handling various data sources and types.                |
-| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.   |
-| **ScrapeWebsiteTool**       | Facilitates scraping entire websites, ideal for comprehensive data collection.                |
-| **WebsiteSearchTool**       | A RAG tool for searching website content, optimized for web data extraction.                  |
-| **XMLSearchTool**           | A RAG tool designed for searching within XML files, suitable for structured data formats.     |
-| **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis.          |
-| **YoutubeVideoSearchTool**  | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.         |
+| Tool                             | Description                                                                                    |
+| :------------------------------- | :--------------------------------------------------------------------------------------------- |
+| **BrowserbaseLoadTool**          | A tool for interacting with and extracting data from web browsers.                             |
+| **CodeDocsSearchTool**           | A RAG tool optimized for searching through code documentation and related technical documents. |
+| **CodeInterpreterTool**          | A tool for interpreting python code.                                                           |
+| **ComposioTool**                 | Enables use of Composio tools.                                                                 |
+| **CSVSearchTool**                | A RAG tool designed for searching within CSV files, tailored to handle structured data.        |
+| **DALL-E Tool**                  | A tool for generating images using the DALL-E API.                                             |
+| **DirectorySearchTool**          | A RAG tool for searching within directories, useful for navigating through file systems.       |
+| **DOCXSearchTool**               | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.          |
+| **DirectoryReadTool**            | Facilitates reading and processing of directory structures and their contents.                 |
+| **EXASearchTool**                | A tool designed for performing exhaustive searches across various data sources.                |
+| **FileReadTool**                 | Enables reading and extracting data from files, supporting various file formats.               |
+| **FirecrawlSearchTool**          | A tool to search webpages using Firecrawl and return the results.                              |
+| **FirecrawlCrawlWebsiteTool**    | A tool for crawling webpages using Firecrawl.                                                  |
+| **FirecrawlScrapeWebsiteTool**   | A tool for scraping webpages URL using Firecrawl and returning its contents.                   |
+| **GithubSearchTool**             | A RAG tool for searching within GitHub repositories, useful for code and documentation search. |
+| **SerperDevTool**                | A specialized tool for development purposes, with specific functionalities under development.  |
+| **TXTSearchTool**                | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.      |
+| **JSONSearchTool**               | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
+| **LlamaIndexTool**               | Enables the use of LlamaIndex tools.                                                           |
+| **MDXSearchTool**                | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.       |
+| **PDFSearchTool**                | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
+| **PGSearchTool**                 | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
+| **Vision Tool**                  | A tool for generating images using the DALL-E API.                                             |
+| **RagTool**                      | A general-purpose RAG tool capable of handling various data sources and types.                 |
+| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.         |
+| **ScrapeWebsiteTool**            | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
+| **WebsiteSearchTool**            | A RAG tool for searching website content, optimized for web data extraction.                   |
+| **XMLSearchTool**                | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
+| **YoutubeChannelSearchTool**     | A RAG tool for searching within YouTube channels, useful for video content analysis.           |
+| **YoutubeVideoSearchTool**       | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
 
 ## Creating your own Tools
 
 <Tip>
-  Developers can craft `custom tools` tailored for their agent’s needs or utilize pre-built options.
+  Developers can craft `custom tools` tailored for their agent’s needs or
+  utilize pre-built options.
 </Tip>
 
-To create your own CrewAI tools you will need to install our extra tools package:
-
-```bash
-pip install 'crewai[tools]'
-```
-
-Once you do that there are two main ways for one to create a CrewAI tool:
+There are two main ways for one to create a CrewAI tool:
 
 ### Subclassing `BaseTool`
 
 ```python Code
-from crewai_tools import BaseTool
+from crewai.tools import BaseTool
+
 
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
@@ -167,7 +164,7 @@ class MyCustomTool(BaseTool):
 ### Utilizing the `tool` Decorator
 
 ```python Code
-from crewai_tools import tool
+from crewai.tools import tool
 @tool("Name of my tool")
 def my_tool(question: str) -> str:
     """Clear description for what this tool is useful for, your agent will need this information to use it."""
@@ -175,14 +172,58 @@ def my_tool(question: str) -> str:
     return "Result from your custom tool"
 ```
 
+### Structured Tools
+
+The `StructuredTool` class wraps functions as tools, providing flexibility and validation while reducing boilerplate. It supports custom schemas and dynamic logic for seamless integration of complex functionalities.
+
+#### Example:
+Using `StructuredTool.from_function`, you can wrap a function that interacts with an external API or system, providing a structured interface. This enables robust validation and consistent execution, making it easier to integrate complex functionalities into your applications as demonstrated in the following example:
+
+```python
+from crewai.tools.structured_tool import CrewStructuredTool
+from pydantic import BaseModel
+
+# Define the schema for the tool's input using Pydantic
+class APICallInput(BaseModel):
+    endpoint: str
+    parameters: dict
+
+# Wrapper function to execute the API call
+def tool_wrapper(*args, **kwargs):
+    # Here, you would typically call the API using the parameters
+    # For demonstration, we'll return a placeholder string
+    return f"Call the API at {kwargs['endpoint']} with parameters {kwargs['parameters']}"
+
+# Create and return the structured tool
+def create_structured_tool():
+    return CrewStructuredTool.from_function(
+        name='Wrapper API',
+        description="A tool to wrap API calls with structured input.",
+        args_schema=APICallInput,
+        func=tool_wrapper,
+    )
+
+# Example usage
+structured_tool = create_structured_tool()
+
+# Execute the tool with structured input
+result = structured_tool._run(**{
+    "endpoint": "https://example.com/api",
+    "parameters": {"key1": "value1", "key2": "value2"}
+})
+print(result)  # Output: Call the API at https://example.com/api with parameters {'key1': 'value1', 'key2': 'value2'}
+```
+
 ### Custom Caching Mechanism
 
 <Tip>
-  Tools can optionally implement a `cache_function` to fine-tune caching behavior. This function determines when to cache results based on specific conditions, offering granular control over caching logic.
+  Tools can optionally implement a `cache_function` to fine-tune caching
+  behavior. This function determines when to cache results based on specific
+  conditions, offering granular control over caching logic.
 </Tip>
 
 ```python Code
-from crewai_tools import tool
+from crewai.tools import tool
 
 @tool
 def multiplication_tool(first_number: int, second_number: int) -> str:
@@ -208,6 +249,6 @@ writer1 = Agent(
 
 ## Conclusion
 
-Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. 
-When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, 
-caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.
+Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
+When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling,
+caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/how-to/agentops-observability.mdx

@@ -57,7 +57,7 @@ This feature is useful for debugging and understanding how agents interact with
    <Step title="Install AgentOps">
       Install AgentOps with:
       ```bash
-      pip install crewai[agentops]
+      pip install 'crewai[agentops]'
       ```
       or
       ```bash

docs/how-to/before-and-after-kickoff-hooks.mdx

@@ -0,0 +1,59 @@
+---
+title: Before and After Kickoff Hooks
+description: Learn how to use before and after kickoff hooks in CrewAI
+---
+
+CrewAI provides hooks that allow you to execute code before and after a crew's kickoff. These hooks are useful for preprocessing inputs or post-processing results.
+
+## Before Kickoff Hook
+
+The before kickoff hook is executed before the crew starts its tasks. It receives the input dictionary and can modify it before passing it to the crew. You can use this hook to set up your environment, load necessary data, or preprocess your inputs. This is useful in scenarios where the input data might need enrichment or validation before being processed by the crew.
+
+Here's an example of defining a before kickoff function in your `crew.py`:
+
+```python
+from crewai import CrewBase, before_kickoff
+
+@CrewBase
+class MyCrew:
+    @before_kickoff
+    def prepare_data(self, inputs):
+        # Preprocess or modify inputs
+        inputs['processed'] = True
+        return inputs
+
+#...
+```
+
+In this example, the prepare_data function modifies the inputs by adding a new key-value pair indicating that the inputs have been processed.
+
+## After Kickoff Hook
+
+The after kickoff hook is executed after the crew has completed its tasks. It receives the result object, which contains the outputs of the crew's execution. This hook is ideal for post-processing results, such as logging, data transformation, or further analysis.
+
+Here's how you can define an after kickoff function in your `crew.py`:
+
+```python
+from crewai import CrewBase, after_kickoff
+
+@CrewBase
+class MyCrew:
+    @after_kickoff
+    def log_results(self, result):
+        # Log or modify the results
+        print("Crew execution completed with result:", result)
+        return result
+
+# ...
+```
+
+
+In the `log_results` function, the results of the crew execution are simply printed out. You can extend this to perform more complex operations such as sending notifications or integrating with other services.
+
+## Utilizing Both Hooks
+
+Both hooks can be used together to provide a comprehensive setup and teardown process for your crew's execution. They are particularly useful in maintaining clean code architecture by separating concerns and enhancing the modularity of your CrewAI implementations.
+
+## Conclusion
+
+Before and after kickoff hooks in CrewAI offer powerful ways to interact with the lifecycle of a crew's execution. By understanding and utilizing these hooks, you can greatly enhance the robustness and flexibility of your AI agents.

docs/how-to/create-custom-tools.mdx

@@ -6,28 +6,27 @@ icon: hammer
 
 ## Creating and Utilizing Tools in CrewAI
 
-This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools, 
-incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, 
+This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools,
+incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools,
 enabling agents to perform a wide range of actions.
 
-### Prerequisites
-
-Before creating your own tools, ensure you have the crewAI extra tools package installed:
-
-```bash
-pip install 'crewai[tools]'
-```
-
 ### Subclassing `BaseTool`
 
-To create a personalized tool, inherit from `BaseTool` and define the necessary attributes and the `_run` method.
+To create a personalized tool, inherit from `BaseTool` and define the necessary attributes, including the `args_schema` for input validation, and the `_run` method.
 
 ```python Code
-from crewai_tools import BaseTool
+from typing import Type
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+class MyToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+    argument: str = Field(..., description="Description of the argument.")
 
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = "What this tool does. It's vital for effective utilization."
+    args_schema: Type[BaseModel] = MyToolInput
 
     def _run(self, argument: str) -> str:
         # Your tool's logic here
@@ -40,7 +39,7 @@ Alternatively, you can use the tool decorator `@tool`. This approach allows you
 offering a concise and efficient way to create specialized tools tailored to your needs.
 
 ```python Code
-from crewai_tools import tool
+from crewai.tools import tool
 
 @tool("Tool Name")
 def my_simple_tool(question: str) -> str:
@@ -66,5 +65,5 @@ def my_cache_strategy(arguments: dict, result: str) -> bool:
 cached_tool.cache_function = my_cache_strategy
 ```
 
-By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes, 
+By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes,
 you can leverage the full capabilities of the CrewAI framework, enhancing both the development experience and the efficiency of your AI agents.

docs/how-to/llm-connections.mdx

@@ -32,6 +32,7 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - Cloudflare Workers AI
 - DeepInfra
 - Groq
+- [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1)
 - And many more!
 
 For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers).
@@ -125,10 +126,10 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
     </Tab>
     <Tab title="Using LLM Class Attributes">
         <CodeGroup>
-        ```python Code
-        llm = LLM(
-            model="custom-model-name",
-            api_key="your-api-key",
+            ```python Code
+            llm = LLM(
+                model="custom-model-name",
+                api_key="your-api-key",
                 base_url="https://api.your-provider.com/v1"
             )
             agent = Agent(llm=llm, ...)
@@ -179,4 +180,4 @@ This is particularly useful when working with OpenAI-compatible APIs or when you
 
 ## 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.
+By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.

docs/how-to/openlit-observability.mdx

@@ -0,0 +1,181 @@
+---
+title: Agent Monitoring with OpenLIT
+description: Quickly start monitoring your Agents in just a single line of code with OpenTelemetry.
+icon: magnifying-glass-chart
+---
+
+# OpenLIT Overview
+
+[OpenLIT](https://github.com/openlit/openlit?src=crewai-docs) is an open-source tool that makes it simple to monitor the performance of AI agents, LLMs, VectorDBs, and GPUs with just **one** line of code. 
+
+It provides OpenTelemetry-native tracing and metrics to track important parameters like cost, latency, interactions and task sequences. 
+This setup enables you to track hyperparameters and monitor for performance issues, helping you find ways to enhance and fine-tune your agents over time.
+
+<Frame caption="OpenLIT Dashboard">
+  <img src="/images/openlit1.png" alt="Overview Agent usage including cost and tokens" />
+  <img src="/images/openlit2.png" alt="Overview of agent otel traces and metrics" />
+  <img src="/images/openlit3.png" alt="Overview of agent traces in details" />
+</Frame>
+
+### Features
+
+- **Analytics Dashboard**: Monitor your Agents health and performance with detailed dashboards that track metrics, costs, and user interactions.
+- **OpenTelemetry-native Observability SDK**: Vendor-neutral SDKs to send traces and metrics to your existing observability tools like Grafana, DataDog and more.
+- **Cost Tracking for Custom and Fine-Tuned Models**: Tailor cost estimations for specific models using custom pricing files for precise budgeting.
+- **Exceptions Monitoring Dashboard**: Quickly spot and resolve issues by tracking common exceptions and errors with a monitoring dashboard.
+- **Compliance and Security**: Detect potential threats such as profanity and PII leaks.
+- **Prompt Injection Detection**: Identify potential code injection and secret leaks.
+- **API Keys and Secrets Management**: Securely handle your LLM API keys and secrets centrally, avoiding insecure practices.
+- **Prompt Management**: Manage and version Agent prompts using PromptHub for consistent and easy access across Agents.
+- **Model Playground** Test and compare different models for your CrewAI agents before deployment.
+
+## Setup Instructions
+
+<Steps>
+    <Step title="Deploy OpenLIT">
+      <Steps>
+        <Step title="Git Clone OpenLIT Repository">
+          ```shell
+          git clone git@github.com:openlit/openlit.git
+          ```
+        </Step>
+        <Step title="Start Docker Compose">
+          From the root directory of the [OpenLIT Repo](https://github.com/openlit/openlit), Run the below command:
+          ```shell
+          docker compose up -d
+          ```
+        </Step>
+      </Steps>
+    </Step>
+    <Step title="Install OpenLIT SDK">
+      ```shell
+      pip install openlit
+      ```
+    </Step>
+    <Step title="Initialize OpenLIT in Your Application">
+      Add the following two lines to your application code:
+      <Tabs>
+        <Tab title="Setup using function arguments">
+          ```python
+          import openlit
+          openlit.init(otlp_endpoint="http://127.0.0.1:4318")
+          ```
+          
+          Example Usage for monitoring a CrewAI Agent:
+    
+          ```python
+          from crewai import Agent, Task, Crew, Process
+          import openlit
+
+          openlit.init(disable_metrics=True)
+          # Define your agents
+          researcher = Agent(
+              role="Researcher",
+              goal="Conduct thorough research and analysis on AI and AI agents",
+              backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
+              allow_delegation=False,
+              llm='command-r'
+          )
+
+
+          # Define your task
+          task = Task(
+              description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.",
+              expected_output="5 bullet points, each with a paragraph and accompanying notes.",
+          )
+
+          # Define the manager agent
+          manager = Agent(
+              role="Project Manager",
+              goal="Efficiently manage the crew and ensure high-quality task completion",
+              backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
+              allow_delegation=True,
+              llm='command-r'
+          )
+
+          # Instantiate your crew with a custom manager
+          crew = Crew(
+              agents=[researcher],
+              tasks=[task],
+              manager_agent=manager,
+              process=Process.hierarchical,
+          )
+
+          # Start the crew's work
+          result = crew.kickoff()
+
+          print(result)
+          ```
+        </Tab>
+        <Tab title="Setup using Environment Variables">
+
+          Add the following two lines to your application code:
+          ```python
+          import openlit
+
+          openlit.init()
+          ```
+
+          Run the following command to configure the OTEL export endpoint:
+          ```shell
+          export OTEL_EXPORTER_OTLP_ENDPOINT = "http://127.0.0.1:4318"
+          ```
+
+          Example Usage for monitoring a CrewAI Async Agent:
+
+          ```python
+          import asyncio
+          from crewai import Crew, Agent, Task
+          import openlit
+
+          openlit.init(otlp_endpoint="http://127.0.0.1:4318")
+
+          # 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,
+            llm="command-r"
+          )
+
+          # 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="5 bullet points, each with a paragraph and accompanying notes.",
+          )
+
+          # Create a crew and add the task
+          analysis_crew = Crew(
+            agents=[coding_agent],
+            tasks=[data_analysis_task]
+          )
+
+          # Async function to kickoff the crew asynchronously
+          async def async_crew_execution():
+              result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
+              print("Crew Result:", result)
+
+          # Run the async function
+          asyncio.run(async_crew_execution())
+          ```
+        </Tab>
+      </Tabs>
+      Refer to OpenLIT [Python SDK repository](https://github.com/openlit/openlit/tree/main/sdk/python) for more advanced configurations and use cases.
+    </Step>
+    <Step title="Visualize and Analyze">
+      With the Agent Observability data now being collected and sent to OpenLIT, the next step is to visualize and analyze this data to get insights into your Agent's performance, behavior, and identify areas of improvement.
+
+      Just head over to OpenLIT at `127.0.0.1:3000` on your browser to start exploring. You can login using the default credentials
+      - **Email**: `user@openlit.io`
+      - **Password**: `openlituser`
+      
+      <Frame caption="OpenLIT Dashboard">
+        <img src="/images/openlit1.png" alt="Overview Agent usage including cost and tokens" />
+        <img src="/images/openlit2.png" alt="Overview of agent otel traces and metrics" />
+      </Frame>
+
+    </Step>
+</Steps> 
+

docs/installation.mdx

@@ -1,128 +1,145 @@
 ---
 title: Installation
-description: 
+description: Get started with CrewAI - Install, configure, and build your first AI crew
 icon: wrench
 ---
 
-This guide will walk you through the installation process for CrewAI and its dependencies. 
-CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. 
-Let's get started! 🚀
+<Note>
+  **Python Version Requirements**
+  
+  CrewAI requires `Python >=3.10 and <=3.12`. Here's how to check your version:
+  ```bash
+  python3 --version
+  ```
+  
+  If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
+</Note>
 
-<Tip>
-    Make sure you have `Python >=3.10 <=3.13` installed on your system before you proceed.
-</Tip>
+# Installing CrewAI
+
+CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. 
+Let's get you set up! 🚀
 
 <Steps>
     <Step title="Install CrewAI">
-    Install the main CrewAI package with the following command:
-    <CodeGroup>
-    ```shell Terminal
-    pip install crewai
-    ```
-    </CodeGroup>
-    You can also install the main CrewAI package and the tools package that include a series of helpful tools for your agents:
-    <CodeGroup>
-    ```shell Terminal
-    pip install 'crewai[tools]'
-    ```
-    </CodeGroup>
-    Alternatively, you can also use:
-    <CodeGroup>
-    ```shell Terminal
-    pip install crewai crewai-tools
-    ```
-    </CodeGroup>
-    </Step>
-    <Step title="Upgrade CrewAI">
-    To upgrade CrewAI and CrewAI Tools to the latest version, run the following command
-    <CodeGroup>
-    ```shell Terminal
-    pip install --upgrade crewai crewai-tools
-    ```
-    </CodeGroup>
-    <Note>
-    1. If you're using an older version of CrewAI, you may receive a warning about using `Poetry` for dependency management. 
-      ![Error from older versions](./images/crewai-run-poetry-error.png)
-
-    2. In this case, you'll need to run the command below to update your project. 
-      This command will migrate your project to use [UV](https://github.com/astral-sh/uv) and update the necessary files.
+        Install CrewAI with all recommended tools using either method:
         ```shell Terminal
-        crewai update
+        pip install 'crewai[tools]'
+        ```
+        or
+        ```shell Terminal
+        pip install crewai crewai-tools
         ```
-    3. After running the command above, you should see the following output:
-      ![Successfully migrated to UV](./images/crewai-update.png)
 
-    4. You're all set! You can now proceed to the next step! 🎉
-    </Note>
+        <Note>
+          Both methods install the core package and additional tools needed for most use cases.
+        </Note>
     </Step>
-    <Step title="Verify the installation">
-    To verify that `crewai` and `crewai-tools` are installed correctly, run the following command
-    <CodeGroup>
-    ```shell Terminal
-    pip freeze | grep crewai
-    ```
-    </CodeGroup>
-    You should see the version number of `crewai` and `crewai-tools`.
-    <CodeGroup>
-    ```markdown Version
-    crewai==X.X.X
-    crewai-tools==X.X.X
-    ```
-    </CodeGroup>
-    If you see the version number, then the installation was successful! 🎉
+
+    <Step title="Upgrade CrewAI (Existing Installations Only)">
+        If you have an older version of CrewAI installed, you can upgrade it:
+        ```shell Terminal
+        pip install --upgrade crewai crewai-tools
+        ```
+
+        <Warning>
+            If you see a Poetry-related warning, you'll need to migrate to our new dependency manager:
+            ```shell Terminal
+            crewai update
+            ```
+            This will update your project to use [UV](https://github.com/astral-sh/uv), our new faster dependency manager.
+        </Warning>
+
+        <Note>
+            Skip this step if you're doing a fresh installation.
+        </Note>
+    </Step>
+
+    <Step title="Verify Installation">
+        Check your installed versions:
+        ```shell Terminal
+        pip freeze | grep crewai
+        ```
+
+        You should see something like:
+        ```markdown Output
+        crewai==X.X.X
+        crewai-tools==X.X.X
+        ```
+        <Check>Installation successful! You're ready to create your first crew.</Check>
     </Step>
 </Steps>
 
-## Create a new CrewAI project
+# Creating a New Project
 
-The next step is to create a new CrewAI project. 
-We recommend using the YAML Template scaffolding to get started as it provides a structured approach to defining agents and tasks.
+<Info>
+  We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks.
+</Info>
 
 <Steps>
-  <Step title="Create a new CrewAI project using the YAML Template Configuration">
-  To create a new CrewAI project, run the following CLI (Command Line Interface) command:
-  <CodeGroup>
-      ```shell Terminal
-      crewai create crew <project_name>
-      ```
-    </CodeGroup>
-  This command creates a new project folder with the following structure:
-    | File/Directory          | Description                                      |
-    |:------------------------|:-------------------------------------------------|
-    | `my_project/`           | Root directory of the project                    |
-    | ├── `.gitignore`        | Specifies files and directories to ignore in Git |
-    | ├── `pyproject.toml`    | Project configuration and dependencies           |
-    | ├── `README.md`         | Project documentation                            |
-    | ├── `.env`              | Environment variables                            |
-    | └── `src/`              | Source code directory                            |
-    | &nbsp;&nbsp;&nbsp;&nbsp;└── `my_project/` | Main application package                      |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├── `__init__.py` | Marks the directory as a Python package     |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├── `main.py`     | Main application script                    |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├── `crew.py`     | Crew-related functionalities                |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├── `tools/`      | Custom tools directory                      |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│   ├── `custom_tool.py` | Custom tool implementation              |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│   └── `__init__.py`    | Marks tools directory as a package       |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└── `config/`     | Configuration files directory               |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├── `agents.yaml`  | Agent configurations                    |
-    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└── `tasks.yaml`   | Task configurations                      |
+  <Step title="Generate Project Structure">
+    Run the CrewAI CLI command:
+    ```shell Terminal
+    crewai create crew <project_name>
+    ```
 
-  You can now start developing your crew by editing the files in the `src/my_project` folder. 
-  The `main.py` file is the entry point of the project, the `crew.py` file is where you define your crew, the `agents.yaml` file is where you define your agents, 
-  and the `tasks.yaml` file is where you define your tasks.
+    This creates a new project with the following structure:
+    <Frame>
+    ```
+    my_project/
+    ├── .gitignore
+    ├── pyproject.toml
+    ├── README.md
+    ├── .env
+    └── src/
+        └── my_project/
+            ├── __init__.py
+            ├── main.py
+            ├── crew.py
+            ├── tools/
+            │   ├── custom_tool.py
+            │   └── __init__.py
+            └── config/
+                ├── agents.yaml
+                └── tasks.yaml
+    ```
+    </Frame>
   </Step>   
-  <Step title="Customize your project">
-  To customize your project, you can:
-    - Modify `src/my_project/config/agents.yaml` to define your agents.
-    - Modify `src/my_project/config/tasks.yaml` to define your tasks.
-    - Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
-    - Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
-    - Add your environment variables into the `.env` file.
+
+  <Step title="Customize Your Project">
+    Your project will contain these essential files:
+
+    | File | Purpose |
+    | --- | --- |
+    | `agents.yaml` | Define your AI agents and their roles |
+    | `tasks.yaml` | Set up agent tasks and workflows |
+    | `.env` | Store API keys and environment variables |
+    | `main.py` | Project entry point and execution flow |
+    | `crew.py` | Crew orchestration and coordination |
+    | `tools/` | Directory for custom agent tools |
+
+    <Tip>
+      Start by editing `agents.yaml` and `tasks.yaml` to define your crew's behavior.
+      Keep sensitive information like API keys in `.env`.
+    </Tip>
   </Step>
 </Steps>
 
-## Next steps
+## Next Steps
 
-Now that you have installed `crewai` and `crewai-tools`, you're ready to spin up your first crew!
-
-- 👨‍💻 Build your first agent with CrewAI by following the [Quickstart](/quickstart) guide.
-- 💬 Join the [Community](https://community.crewai.com) to get help and share your feedback.
+<CardGroup cols={2}>
+  <Card
+    title="Build Your First Agent"
+    icon="code"
+    href="/quickstart"
+  >
+    Follow our quickstart guide to create your first CrewAI agent and get hands-on experience.
+  </Card>
+  <Card
+    title="Join the Community"
+    icon="comments"
+    href="https://community.crewai.com"
+  >
+    Connect with other developers, get help, and share your CrewAI experiences.
+  </Card>
+</CardGroup>

docs/introduction.mdx

@@ -1,49 +1,85 @@
 ---
 title: Introduction
-description: Welcome to CrewAI docs!
+description: Build AI agent teams that work together to tackle complex tasks
 icon: handshake
 ---
 
 # What is CrewAI?
 
-**CrewAI is a cutting-edge Python framework for orchestrating role-playing, autonomous AI agents.**
+**CrewAI is a cutting-edge framework for orchestrating autonomous AI agents.** 
 
-By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
+CrewAI enables you to create AI teams where each agent has specific roles, tools, and goals, working together to accomplish complex tasks.
 
-<Frame caption="CrewAI Mindmap">
-  <img src="crewAI-mindmap.png" alt="CrewAI Mindmap" />
-</Frame>  
+Think of it as assembling your dream team - each member (agent) brings unique skills and expertise, collaborating seamlessly to achieve your objectives.
 
-## Why CrewAI?
-- 🤼‍♀️ **Role-Playing Agents**: Agents can take on different roles and personas to better understand and interact with complex systems.
-- 🤖 **Autonomous Decision Making**: Agents can make decisions autonomously based on the given context and available tools.
-- 🤝 **Seamless Collaboration**: Agents can work together seamlessly, sharing information and resources to achieve common goals.
-- 🧠 **Complex Task Tackling**: CrewAI is designed to tackle complex tasks, such as multi-step workflows, decision making, and problem solving.
+## How CrewAI Works
 
-# Get Started with CrewAI
+<Note>
+  Just like a company has departments (Sales, Engineering, Marketing) working together under leadership to achieve business goals, CrewAI helps you create an organization of AI agents with specialized roles collaborating to accomplish complex tasks.
+</Note>
+
+<Frame caption="CrewAI Framework Overview">
+  <img src="crewAI-mindmap.png" alt="CrewAI Framework Overview" />
+</Frame>
+
+| Component | Description | Key Features |
+|:----------|:-----------:|:------------|
+| **Crew** | The top-level organization | • Manages AI agent teams<br/>• Oversees workflows<br/>• Ensures collaboration<br/>• Delivers outcomes |
+| **AI Agents** | Specialized team members | • Have specific roles (researcher, writer)<br/>• Use designated tools<br/>• Can delegate tasks<br/>• Make autonomous decisions |
+| **Process** | Workflow management system | • Defines collaboration patterns<br/>• Controls task assignments<br/>• Manages interactions<br/>• Ensures efficient execution |
+| **Tasks** | Individual assignments | • Have clear objectives<br/>• Use specific tools<br/>• Feed into larger process<br/>• Produce actionable results |
+
+### How It All Works Together
+
+1. The **Crew** organizes the overall operation
+2. **AI Agents** work on their specialized tasks
+3. The **Process** ensures smooth collaboration
+4. **Tasks** get completed to achieve the goal
+
+## Key Features
 
 <CardGroup cols={2}>
+  <Card title="Role-Based Agents" icon="users">
+    Create specialized agents with defined roles, expertise, and goals - from researchers to analysts to writers
+  </Card>
+  <Card title="Flexible Tools" icon="screwdriver-wrench">
+    Equip agents with custom tools and APIs to interact with external services and data sources
+  </Card>
+  <Card title="Intelligent Collaboration" icon="people-arrows">
+    Agents work together, sharing insights and coordinating tasks to achieve complex objectives
+  </Card>
+  <Card title="Task Management" icon="list-check">
+    Define sequential or parallel workflows, with agents automatically handling task dependencies
+  </Card>
+</CardGroup>
+
+## Why Choose CrewAI?
+
+- 🧠 **Autonomous Operation**: Agents make intelligent decisions based on their roles and available tools
+- 📝 **Natural Interaction**: Agents communicate and collaborate like human team members
+- 🛠️ **Extensible Design**: Easy to add new tools, roles, and capabilities
+- 🚀 **Production Ready**: Built for reliability and scalability in real-world applications
+
+<CardGroup cols={3}>
   <Card
-  title="Quickstart"
-  color="#F3A78B"
-  href="quickstart"
-  icon="terminal"
-    iconType="solid"
+    title="Install CrewAI"
+    icon="wrench"
+    href="/installation"
   >
-    Getting started with CrewAI 
+    Get started with CrewAI in your development environment.
+  </Card>
+  <Card
+    title="Quick Start"
+    icon="bolt"
+    href="/quickstart"
+  >
+    Follow our quickstart guide to create your first CrewAI agent and get hands-on experience.
   </Card>
   <Card
     title="Join the Community"
-    color="#F3A78B"
+    icon="comments"
     href="https://community.crewai.com"
-    icon="comment-question"
-      iconType="duotone"
-    >
-      Join the CrewAI community and get help with your project!
-    </Card>
-</CardGroup>
-
-## Next Step
-
-- [Install CrewAI](/installation) to get started with your first agent.
-
+  >
+    Connect with other developers, get help, and share your CrewAI experiences.
+  </Card>
+</CardGroup>

docs/mint.json

@@ -68,6 +68,7 @@
         "concepts/tasks",
         "concepts/crews",
         "concepts/flows",
+        "concepts/knowledge",
         "concepts/llms",
         "concepts/processes",
         "concepts/collaboration",
@@ -98,7 +99,8 @@
         "how-to/replay-tasks-from-latest-crew-kickoff",
         "how-to/conditional-tasks",
         "how-to/agentops-observability",
-        "how-to/langtrace-observability"
+        "how-to/langtrace-observability",
+        "how-to/openlit-observability"
       ]
     },
     {

docs/quickstart.mdx

@@ -8,7 +8,7 @@ icon: rocket
 
 Let's create a simple crew that will help us `research` and `report` on the `latest AI developments` for a given topic or subject.
 
-Before we proceed, make sure you have `crewai` and `crewai-tools` installed. 
+Before we proceed, make sure you have `crewai` and `crewai-tools` installed.
 If you haven't installed them yet, you can do so by following the [installation guide](/installation).
 
 Follow the steps below to get crewing! 🚣‍♂️
@@ -23,7 +23,7 @@ Follow the steps below to get crewing! 🚣‍♂️
       ```
     </CodeGroup>
   </Step>
-  <Step title="Modify your `agents.yaml` file">  
+  <Step title="Modify your `agents.yaml` file">
   <Tip>
   You can also modify the agents as needed to fit your use case or copy and paste as is to your project.
   Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{topic}` will be replaced by the value of the variable in the `main.py` file.
@@ -39,7 +39,7 @@ Follow the steps below to get crewing! 🚣‍♂️
         You're a seasoned researcher with a knack for uncovering the latest
         developments in {topic}. Known for your ability to find the most relevant
         information and present it in a clear and concise manner.
-          
+
     reporting_analyst:
       role: >
         {topic} Reporting Analyst
@@ -51,7 +51,7 @@ Follow the steps below to get crewing! 🚣‍♂️
         it easy for others to understand and act on the information you provide.
     ```
   </Step>
-  <Step title="Modify your `tasks.yaml` file">  
+  <Step title="Modify your `tasks.yaml` file">
     ```yaml tasks.yaml
     # src/latest_ai_development/config/tasks.yaml
     research_task:
@@ -73,8 +73,8 @@ Follow the steps below to get crewing! 🚣‍♂️
       agent: reporting_analyst
       output_file: report.md
     ```
-  </Step>   
-  <Step title="Modify your `crew.py` file">  
+  </Step>
+  <Step title="Modify your `crew.py` file">
     ```python crew.py
     # src/latest_ai_development/crew.py
     from crewai import Agent, Crew, Process, Task
@@ -121,10 +121,34 @@ Follow the steps below to get crewing! 🚣‍♂️
           tasks=self.tasks, # Automatically created by the @task decorator
           process=Process.sequential,
           verbose=True,
-        ) 
+        )
     ```
   </Step>
-  <Step title="Feel free to pass custom inputs to your crew"> 
+  <Step title="[Optional] Add before and after crew functions">
+    ```python crew.py
+    # src/latest_ai_development/crew.py
+    from crewai import Agent, Crew, Process, Task
+    from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
+    from crewai_tools import SerperDevTool
+
+    @CrewBase
+    class LatestAiDevelopmentCrew():
+      """LatestAiDevelopment crew"""
+
+      @before_kickoff
+      def before_kickoff_function(self, inputs):
+        print(f"Before kickoff function with inputs: {inputs}")
+        return inputs # You can return the inputs or modify them as needed
+
+      @after_kickoff
+      def after_kickoff_function(self, result):
+        print(f"After kickoff function with result: {result}")
+        return result # You can return the result or modify it as needed
+
+      # ... remaining code
+    ```
+  </Step>
+  <Step title="Feel free to pass custom inputs to your crew">
   For example, you can pass the `topic` input to your crew to customize the research and reporting.
     ```python main.py
     #!/usr/bin/env python
@@ -237,14 +261,14 @@ Follow the steps below to get crewing! 🚣‍♂️
 ### Note on Consistency in Naming
 
 The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
-For example, you can reference the agent for specific tasks from `tasks.yaml` file. 
+For example, you can reference the agent for specific tasks from `tasks.yaml` file.
 This naming consistency allows CrewAI to automatically link your configurations with your code; otherwise, your task won't recognize the reference properly.
 
 #### Example References
 
 <Tip>
   Note how we use the same name for the agent in the `agents.yaml` (`email_summarizer`) file as the method name in the `crew.py` (`email_summarizer`) file.
-</Tip>  
+</Tip>
 
 ```yaml agents.yaml
 email_summarizer:
@@ -281,6 +305,8 @@ Use the annotations to properly reference the agent and task in the `crew.py` fi
 * `@task`
 * `@crew`
 * `@tool`
+* `@before_kickoff`
+* `@after_kickoff`
 * `@callback`
 * `@output_json`
 * `@output_pydantic`
@@ -304,7 +330,7 @@ def email_summarizer_task(self) -> Task:
 
 <Tip>
 In addition to the [sequential process](../how-to/sequential-process), you can use the [hierarchical process](../how-to/hierarchical-process),
-which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. 
+which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results.
 You can learn more about the core concepts [here](/concepts).
 </Tip>
 
@@ -323,11 +349,28 @@ Replace `<task_id>` with the ID of the task you want to replay.
 If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature:
 
 ```shell
-crewai reset-memory
+crewai reset-memories --all
 ```
 
 This will clear the crew's memory, allowing for a fresh start.
 
 ## Deploying Your Project
 
-The easiest way to deploy your crew is through [CrewAI Enterprise](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.
+The easiest way to deploy your crew is through CrewAI Enterprise, where you can deploy your crew in a few clicks.
+
+<CardGroup cols={2}>
+  <Card
+    title="Deploy on Enterprise"
+    icon="rocket"
+    href="http://app.crewai.com"
+  >
+    Get started with CrewAI Enterprise and deploy your crew in a production environment with just a few clicks.
+  </Card>
+  <Card
+    title="Join the Community"
+    icon="comments"
+    href="https://community.crewai.com"
+  >
+    Join our open source community to discuss ideas, share your projects, and connect with other CrewAI developers.
+  </Card>
+</CardGroup>

docs/tools/githubsearchtool.mdx

@@ -34,6 +34,7 @@ from crewai_tools import GithubSearchTool
 # Initialize the tool for semantic searches within a specific GitHub repository
 tool = GithubSearchTool(
 	github_repo='https://github.com/example/repo',
+	gh_token='your_github_personal_access_token',
 	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 
@@ -41,6 +42,7 @@ tool = GithubSearchTool(
 
 # Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
 tool = GithubSearchTool(
+	gh_token='your_github_personal_access_token',
 	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 ```
@@ -48,6 +50,7 @@ tool = GithubSearchTool(
 ## Arguments
 
 - `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search.
+- `gh_token` : Your GitHub Personal Access Token (PAT) required for authentication. You can create one in your GitHub account settings under Developer Settings > Personal Access Tokens.
 - `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code, 
 `repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues. 
 This field is mandatory and allows tailoring the search to specific content types within the GitHub repository.
@@ -77,5 +80,4 @@ tool = GithubSearchTool(
             ),
         ),
     )
-)
-```
+)

mkdocs.yml

@@ -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'
@@ -152,6 +151,7 @@ nav:
     - Conditional Tasks: 'how-to/Conditional-Tasks.md'
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
+    - Agent Monitoring with OpenLIT: 'how-to/openlit-Observability.md'
   - Tools Docs:
     - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
     - Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'

pyproject.toml

@@ -1,22 +1,20 @@
 [project]
 name = "crewai"
-version = "0.74.2"
+version = "0.86.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
-requires-python = ">=3.10,<=3.13"
+requires-python = ">=3.10,<=3.12"
 authors = [
     { name = "Joao Moura", email = "joao@crewai.com" }
 ]
 dependencies = [
     "pydantic>=2.4.2",
-    "langchain>=0.2.16",
     "openai>=1.13.3",
     "opentelemetry-api>=1.22.0",
     "opentelemetry-sdk>=1.22.0",
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
-    "crewai-tools>=0.13.2",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
@@ -27,7 +25,11 @@ dependencies = [
     "pyvis>=0.3.2",
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
-    "chromadb>=0.4.24",
+    "tomli>=2.0.2",
+    "chromadb>=0.5.18",
+    "pdfplumber>=0.11.4",
+    "openpyxl>=3.1.5",
+    "blinker>=1.9.0",
 ]
 
 [project.urls]
@@ -36,8 +38,19 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.13.2"]
+tools = ["crewai-tools>=0.14.0"]
 agentops = ["agentops>=0.3.0"]
+fastembed = ["fastembed>=0.4.1"]
+pdfplumber = [
+    "pdfplumber>=0.11.4",
+]
+pandas = [
+    "pandas>=2.2.3",
+]
+openpyxl = [
+    "openpyxl>=3.1.5",
+]
+mem0 = ["mem0ai>=0.1.29"]
 
 [tool.uv]
 dev-dependencies = [
@@ -51,7 +64,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
-    "crewai-tools>=0.13.2",
+    "crewai-tools>=0.14.0",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/__init__.py

@@ -1,11 +1,11 @@
 import warnings
+
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.flow.flow import Flow
+from crewai.knowledge.knowledge import Knowledge
 from crewai.llm import LLM
-from crewai.pipeline import Pipeline
 from crewai.process import Process
-from crewai.routers import Router
 from crewai.task import Task
 
 warnings.filterwarnings(
@@ -14,5 +14,13 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.74.2"
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]
+__version__ = "0.86.0"
+__all__ = [
+    "Agent",
+    "Crew",
+    "Process",
+    "Task",
+    "LLM",
+    "Flow",
+    "Knowledge",
+]

src/crewai/agent.py

@@ -1,17 +1,25 @@
 import os
-from inspect import signature
-from typing import Any, List, Optional, Union
+import shutil
+import subprocess
+from typing import Any, Dict, List, Literal, Optional, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
+from crewai.knowledge.knowledge import Knowledge
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
-from crewai.tools.agent_tools import AgentTools
+from crewai.task import Task
+from crewai.tools import BaseTool
+from crewai.tools.agent_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.converter import generate_model_description
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -49,6 +57,7 @@ class Agent(BaseAgent):
             role: The role of the agent.
             goal: The objective of the agent.
             backstory: The backstory of the agent.
+            knowledge: The knowledge base of the agent.
             config: Dict representation of agent configuration.
             llm: The language model that will run the agent.
             function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
@@ -59,6 +68,7 @@ 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.
+            knowledge_sources: Knowledge sources for the agent.
     """
 
     _times_executed: int = PrivateAttr(default=0)
@@ -112,10 +122,31 @@ class Agent(BaseAgent):
         default=2,
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
     )
+    code_execution_mode: Literal["safe", "unsafe"] = Field(
+        default="safe",
+        description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
+    )
+    embedder_config: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Embedder configuration for the agent.",
+    )
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+        default=None,
+        description="Knowledge sources for the agent.",
+    )
+    _knowledge: Optional[Knowledge] = PrivateAttr(
+        default=None,
+    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
+        self._set_knowledge()
         self.agent_ops_agent_name = self.role
+        unaccepted_attributes = [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+        ]
 
         # Handle different cases for self.llm
         if isinstance(self.llm, str):
@@ -125,8 +156,12 @@ class Agent(BaseAgent):
             # 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")
+            # Determine the model name from environment variables or use default
+            model_name = (
+                os.environ.get("OPENAI_MODEL_NAME")
+                or os.environ.get("MODEL")
+                or "gpt-4o-mini"
+            )
             llm_params = {"model": model_name}
 
             api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
@@ -135,9 +170,30 @@ class Agent(BaseAgent):
             if api_base:
                 llm_params["base_url"] = api_base
 
-            api_key = os.environ.get("OPENAI_API_KEY")
-            if api_key:
-                llm_params["api_key"] = api_key
+            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
+
+            # Iterate over all environment variables to find matching API keys or use defaults
+            for provider, env_vars in ENV_VARS.items():
+                if provider == set_provider:
+                    for env_var in env_vars:
+                        # Check if the environment variable is set
+                        key_name = env_var.get("key_name")
+                        if key_name and key_name not in unaccepted_attributes:
+                            env_value = os.environ.get(key_name)
+                            if env_value:
+                                key_name = key_name.lower()
+                                for pattern in LITELLM_PARAMS:
+                                    if pattern in key_name:
+                                        key_name = pattern
+                                        break
+                                llm_params[key_name] = env_value
+                        # Check for default values if the environment variable is not set
+                        elif env_var.get("default", False):
+                            for key, value in env_var.items():
+                                if key not in ["prompt", "key_name", "default"]:
+                                    # Only add default if the key is already set in os.environ
+                                    if key in os.environ:
+                                        llm_params[key] = value
 
             self.llm = LLM(**llm_params)
         else:
@@ -173,6 +229,9 @@ class Agent(BaseAgent):
         if not self.agent_executor:
             self._setup_agent_executor()
 
+        if self.allow_code_execution:
+            self._validate_docker_installation()
+
         return self
 
     def _setup_agent_executor(self):
@@ -180,11 +239,26 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
+    def _set_knowledge(self):
+        try:
+            if self.knowledge_sources:
+                knowledge_agent_name = f"{self.role.replace(' ', '_')}"
+                if isinstance(self.knowledge_sources, list) and all(
+                    isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
+                ):
+                    self._knowledge = Knowledge(
+                        sources=self.knowledge_sources,
+                        embedder_config=self.embedder_config,
+                        collection_name=knowledge_agent_name,
+                    )
+        except (TypeError, ValueError) as e:
+            raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
+
     def execute_task(
         self,
-        task: Any,
+        task: Task,
         context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -201,6 +275,22 @@ class Agent(BaseAgent):
 
         task_prompt = task.prompt()
 
+        # If the task requires output in JSON or Pydantic format,
+        # append specific instructions to the task prompt to ensure
+        # that the final answer does not include any code block markers
+        if task.output_json or task.output_pydantic:
+            # Generate the schema based on the output format
+            if task.output_json:
+                # schema = json.dumps(task.output_json, indent=2)
+                schema = generate_model_description(task.output_json)
+
+            elif task.output_pydantic:
+                schema = generate_model_description(task.output_pydantic)
+
+            task_prompt += "\n" + self.i18n.slice("formatted_task_instructions").format(
+                output_format=schema
+            )
+
         if context:
             task_prompt = self.i18n.slice("task_with_context").format(
                 task=task_prompt, context=context
@@ -208,14 +298,32 @@ class Agent(BaseAgent):
 
         if self.crew and self.crew.memory:
             contextual_memory = ContextualMemory(
+                self.crew.memory_config,
                 self.crew._short_term_memory,
                 self.crew._long_term_memory,
                 self.crew._entity_memory,
+                self.crew._user_memory,
             )
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
+        if self._knowledge:
+            agent_knowledge_snippets = self._knowledge.query([task.prompt()])
+            if agent_knowledge_snippets:
+                agent_knowledge_context = extract_knowledge_context(
+                    agent_knowledge_snippets
+                )
+                if agent_knowledge_context:
+                    task_prompt += agent_knowledge_context
+
+        if self.crew:
+            knowledge_snippets = self.crew.query_knowledge([task.prompt()])
+            if knowledge_snippets:
+                crew_knowledge_context = extract_knowledge_context(knowledge_snippets)
+                if crew_knowledge_context:
+                    task_prompt += crew_knowledge_context
+
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)
 
@@ -251,7 +359,9 @@ class Agent(BaseAgent):
 
         return result
 
-    def create_agent_executor(self, tools=None, task=None) -> None:
+    def create_agent_executor(
+        self, tools: Optional[List[BaseTool]] = None, task=None
+    ) -> None:
         """Create an agent executor for the agent.
 
         Returns:
@@ -308,7 +418,9 @@ class Agent(BaseAgent):
         try:
             from crewai_tools import CodeInterpreterTool
 
-            return [CodeInterpreterTool()]
+            # Set the unsafe_mode based on the code_execution_mode attribute
+            unsafe_mode = self.code_execution_mode == "unsafe"
+            return [CodeInterpreterTool(unsafe_mode=unsafe_mode)]
         except ModuleNotFoundError:
             self._logger.log(
                 "info", "Coding tools not available. Install crewai_tools. "
@@ -322,11 +434,11 @@ class Agent(BaseAgent):
         tools_list = []
         try:
             # tentatively try to import from crewai_tools import BaseTool as CrewAITool
-            from crewai_tools import BaseTool as CrewAITool
+            from crewai.tools import BaseTool as CrewAITool
 
             for tool in tools:
                 if isinstance(tool, CrewAITool):
-                    tools_list.append(tool.to_langchain())
+                    tools_list.append(tool.to_structured_tool())
                 else:
                     tools_list.append(tool)
         except ModuleNotFoundError:
@@ -381,33 +493,42 @@ 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:
+            Output will be in the format of:
 
-        .. code-block:: markdown
+            .. code-block:: markdown
 
             search: This tool is used for search, args: {"query": {"type": "string"}}
             calculator: This tool is used for math, \
-    args: {"expression": {"type": "string"}}
+            args: {"expression": {"type": "string"}}
         """
         tool_strings = []
         for tool in tools:
-            args_schema = str(tool.model_fields)
-            if hasattr(tool, "func") and tool.func:
-                sig = signature(tool.func)
-                description = (
-                    f"Tool Name: {tool.name}{sig}\nTool Description: {tool.description}"
-                )
-            else:
-                description = (
-                    f"Tool Name: {tool.name}\nTool Description: {tool.description}"
-                )
-            tool_strings.append(f"{description}\nTool Arguments: {args_schema}")
+            tool_strings.append(tool.description)
 
         return "\n".join(tool_strings)
 
+    def _validate_docker_installation(self) -> None:
+        """Check if Docker is installed and running."""
+        if not shutil.which("docker"):
+            raise RuntimeError(
+                f"Docker is not installed. Please install Docker to use code execution with agent: {self.role}"
+            )
+
+        try:
+            subprocess.run(
+                ["docker", "info"],
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+            )
+        except subprocess.CalledProcessError:
+            raise RuntimeError(
+                f"Docker is not running. Please start Docker to use code execution with agent: {self.role}"
+            )
+
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])

src/crewai/agents/agent_builder/base_agent.py

@@ -18,6 +18,8 @@ from pydantic_core import PydanticCustomError
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.tools import BaseTool
+from crewai.tools.base_tool import Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 
@@ -49,11 +51,11 @@ class BaseAgent(ABC, BaseModel):
 
 
     Methods:
-        execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[Any]] = None) -> str:
+        execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[BaseTool]] = None) -> str:
             Abstract method to execute a task.
         create_agent_executor(tools=None) -> None:
             Abstract method to create an agent executor.
-        _parse_tools(tools: List[Any]) -> List[Any]:
+        _parse_tools(tools: List[BaseTool]) -> List[Any]:
             Abstract method to parse tools.
         get_delegation_tools(agents: List["BaseAgent"]):
             Abstract method to set the agents task tools for handling delegation and question asking to other agents in crew.
@@ -134,6 +136,35 @@ class BaseAgent(ABC, BaseModel):
     def process_model_config(cls, values):
         return process_config(values, cls)
 
+    @field_validator("tools")
+    @classmethod
+    def validate_tools(cls, tools: List[Any]) -> List[BaseTool]:
+        """Validate and process the tools provided to the agent.
+
+        This method ensures that each tool is either an instance of BaseTool
+        or an object with 'name', 'func', and 'description' attributes. If the
+        tool meets these criteria, it is processed and added to the list of
+        tools. Otherwise, a ValueError is raised.
+        """
+        processed_tools = []
+        for tool in tools:
+            if isinstance(tool, BaseTool):
+                processed_tools.append(tool)
+            elif (
+                hasattr(tool, "name")
+                and hasattr(tool, "func")
+                and hasattr(tool, "description")
+            ):
+                # Tool has the required attributes, create a Tool instance
+                processed_tools.append(Tool.from_langchain(tool))
+            else:
+                raise ValueError(
+                    f"Invalid tool type: {type(tool)}. "
+                    "Tool must be an instance of BaseTool or "
+                    "an object with 'name', 'func', and 'description' attributes."
+                )
+        return processed_tools
+
     @model_validator(mode="after")
     def validate_and_set_attributes(self):
         # Validate required fields
@@ -188,7 +219,7 @@ class BaseAgent(ABC, BaseModel):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         pass
 
@@ -197,11 +228,11 @@ class BaseAgent(ABC, BaseModel):
         pass
 
     @abstractmethod
-    def _parse_tools(self, tools: List[Any]) -> List[Any]:
+    def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]:
         pass
 
     @abstractmethod
-    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[Any]:
+    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[BaseTool]:
         """Set the task tools that init BaseAgenTools class."""
         pass
 

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -3,16 +3,15 @@ from typing import TYPE_CHECKING, Optional
 
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
+from crewai.utilities import I18N
 from crewai.utilities.converter import ConverterError
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-from crewai.utilities import I18N
 from crewai.utilities.printer import Printer
 
-
 if TYPE_CHECKING:
+    from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.crew import Crew
     from crewai.task import Task
-    from crewai.agents.agent_builder.base_agent import BaseAgent
 
 
 class CrewAgentExecutorMixin:
@@ -100,14 +99,19 @@ class CrewAgentExecutorMixin:
                 print(f"Failed to add to long term memory: {e}")
                 pass
 
-    def _ask_human_input(self, final_answer: dict) -> str:
+    def _ask_human_input(self, final_answer: str) -> str:
         """Prompt human input for final decision making."""
         self._printer.print(
             content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
         )
 
         self._printer.print(
-            content="\n\n=====\n## Please provide feedback on the Final Result and the Agent's actions:",
+            content=(
+                "\n\n=====\n"
+                "## Please provide feedback on the Final Result and the Agent's actions. "
+                "Respond with 'looks good' or a similar phrase when you're satisfied.\n"
+                "=====\n"
+            ),
             color="bold_yellow",
         )
         return input()

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

@@ -4,6 +4,7 @@ from crewai.types.usage_metrics import UsageMetrics
 class TokenProcess:
     total_tokens: int = 0
     prompt_tokens: int = 0
+    cached_prompt_tokens: int = 0
     completion_tokens: int = 0
     successful_requests: int = 0
 
@@ -15,6 +16,9 @@ class TokenProcess:
         self.completion_tokens = self.completion_tokens + tokens
         self.total_tokens = self.total_tokens + tokens
 
+    def sum_cached_prompt_tokens(self, tokens: int):
+        self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
+
     def sum_successful_requests(self, requests: int):
         self.successful_requests = self.successful_requests + requests
 
@@ -22,6 +26,7 @@ class TokenProcess:
         return UsageMetrics(
             total_tokens=self.total_tokens,
             prompt_tokens=self.prompt_tokens,
+            cached_prompt_tokens=self.cached_prompt_tokens,
             completion_tokens=self.completion_tokens,
             successful_requests=self.successful_requests,
         )

src/crewai/agents/crew_agent_executor.py

@@ -1,5 +1,6 @@
 import json
 import re
+from dataclasses import dataclass
 from typing import Any, Dict, List, Union
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -12,9 +13,10 @@ from crewai.agents.parser import (
     OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.tools.base_tool import BaseTool
 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.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
@@ -22,6 +24,12 @@ from crewai.utilities.logger import Logger
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
+@dataclass
+class ToolResult:
+    result: Any
+    result_as_answer: bool
+
+
 class CrewAgentExecutor(CrewAgentExecutorMixin):
     _logger: Logger = Logger()
 
@@ -33,7 +41,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         agent: BaseAgent,
         prompt: dict[str, str],
         max_iter: int,
-        tools: List[Any],
+        tools: List[BaseTool],
         tools_names: str,
         stop_words: List[str],
         tools_description: str,
@@ -70,7 +78,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.iterations = 0
         self.log_error_after = 3
         self.have_forced_answer = False
-        self.name_to_tool_map = {tool.name: tool for tool in self.tools}
+        self.tool_name_to_tool_map: Dict[str, BaseTool] = {
+            tool.name: tool for tool in self.tools
+        }
         if self.llm.stop:
             self.llm.stop = list(set(self.llm.stop + self.stop))
         else:
@@ -80,7 +90,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         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:
@@ -93,17 +102,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         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)
+            formatted_answer = self._handle_human_feedback(formatted_answer)
 
-            # Making sure we only ask for it once, so disabling for the next thought loop
-            self.ask_for_human_input = False
-            self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
-            formatted_answer = self._invoke_loop()
-
-            if self.crew and self.crew._train:
-                self._handle_crew_training_output(formatted_answer)
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
@@ -117,6 +117,15 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         callbacks=self.callbacks,
                     )
 
+                    if answer is None or answer == "":
+                        self._printer.print(
+                            content="Received None or empty response from LLM call.",
+                            color="red",
+                        )
+                        raise ValueError(
+                            "Invalid response from LLM call - None or empty."
+                        )
+
                     if not self.use_stop_words:
                         try:
                             self._format_answer(answer)
@@ -131,30 +140,42 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     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
+                        tool_result = self._execute_tool_and_check_finality(
+                            formatted_answer
+                        )
+                        if self.step_callback:
+                            self.step_callback(tool_result)
+
+                        formatted_answer.text += f"\nObservation: {tool_result.result}"
+                        formatted_answer.result = tool_result.result
+                        if tool_result.result_as_answer:
+                            return AgentFinish(
+                                thought="",
+                                output=tool_result.result,
+                                text=formatted_answer.text,
+                            )
                         self._show_logs(formatted_answer)
 
-                        if self.step_callback:
-                            self.step_callback(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="assistant")
-                        )
+                    if self._should_force_answer():
+                        if self.have_forced_answer:
+                            return AgentFinish(
+                                thought="",
+                                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="assistant")
+                    )
 
         except OutputParserException as e:
             self.messages.append({"role": "user", "content": e.error})
@@ -229,7 +250,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
                 )
 
-    def _use_tool(self, agent_action: AgentAction) -> Any:
+    def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult:
         tool_usage = ToolUsage(
             tools_handler=self.tools_handler,
             tools=self.tools,
@@ -245,19 +266,25 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         if isinstance(tool_calling, ToolUsageErrorException):
             tool_result = tool_calling.message
+            return ToolResult(result=tool_result, result_as_answer=False)
         else:
             if tool_calling.tool_name.casefold().strip() in [
-                name.casefold().strip() for name in self.name_to_tool_map
+                name.casefold().strip() for name in self.tool_name_to_tool_map
             ] or tool_calling.tool_name.casefold().replace("_", " ") in [
-                name.casefold().strip() for name in self.name_to_tool_map
+                name.casefold().strip() for name in self.tool_name_to_tool_map
             ]:
                 tool_result = tool_usage.use(tool_calling, agent_action.text)
+                tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
+                if tool:
+                    return ToolResult(
+                        result=tool_result, result_as_answer=tool.result_as_answer
+                    )
             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
+        return ToolResult(result=tool_result, result_as_answer=False)
 
     def _summarize_messages(self) -> None:
         messages_groups = []
@@ -275,7 +302,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         self._i18n.slice("summarizer_system_message"), role="system"
                     ),
                     self._format_msg(
-                        self._i18n.slice("sumamrize_instruction").format(group=group),
+                        self._i18n.slice("summarize_instruction").format(group=group),
                     ),
                 ],
                 callbacks=self.callbacks,
@@ -292,16 +319,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     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.",
+            self._printer.print(
+                content="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.",
+            self._printer.print(
+                content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
                 color="red",
             )
             raise SystemExit(
@@ -328,15 +353,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     ] = result.output
                     training_handler.save(training_data)
                 else:
-                    self._logger.log(
-                        "error",
-                        "Invalid train iteration type or agent_id not in training data.",
+                    self._printer.print(
+                        content="Invalid train iteration type or agent_id not in training data.",
                         color="red",
                     )
             else:
-                self._logger.log(
-                    "error",
-                    "Crew is None or does not have _train_iteration attribute.",
+                self._printer.print(
+                    content="Crew is None or does not have _train_iteration attribute.",
                     color="red",
                 )
 
@@ -354,15 +377,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         train_iteration, agent_id, training_data
                     )
                 else:
-                    self._logger.log(
-                        "error",
-                        "Invalid train iteration type. Expected int.",
+                    self._printer.print(
+                        content="Invalid train iteration type. Expected int.",
                         color="red",
                     )
             else:
-                self._logger.log(
-                    "error",
-                    "Crew is None or does not have _train_iteration attribute.",
+                self._printer.print(
+                    content="Crew is None or does not have _train_iteration attribute.",
                     color="red",
                 )
 
@@ -376,4 +397,83 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return CrewAgentParser(agent=self.agent).parse(answer)
 
     def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
+        prompt = prompt.rstrip()
         return {"role": role, "content": prompt}
+
+    def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
+        """
+        Handles the human feedback loop, allowing the user to provide feedback
+        on the agent's output and determining if additional iterations are needed.
+
+        Parameters:
+            formatted_answer (AgentFinish): The initial output from the agent.
+
+        Returns:
+            AgentFinish: The final output after incorporating human feedback.
+        """
+        while 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)
+
+            # Make an LLM call to verify if additional changes are requested based on human feedback
+            additional_changes_prompt = self._i18n.slice(
+                "human_feedback_classification"
+            ).format(feedback=human_feedback)
+
+            retry_count = 0
+            llm_call_successful = False
+            additional_changes_response = None
+
+            while retry_count < MAX_LLM_RETRY and not llm_call_successful:
+                try:
+                    additional_changes_response = (
+                        self.llm.call(
+                            [
+                                self._format_msg(
+                                    additional_changes_prompt, role="system"
+                                )
+                            ],
+                            callbacks=self.callbacks,
+                        )
+                        .strip()
+                        .lower()
+                    )
+                    llm_call_successful = True
+                except Exception as e:
+                    retry_count += 1
+
+                    self._printer.print(
+                        content=f"Error during LLM call to classify human feedback: {e}. Retrying... ({retry_count}/{MAX_LLM_RETRY})",
+                        color="red",
+                    )
+
+            if not llm_call_successful:
+                self._printer.print(
+                    content="Error processing feedback after multiple attempts.",
+                    color="red",
+                )
+                self.ask_for_human_input = False
+                break
+
+            if additional_changes_response == "false":
+                self.ask_for_human_input = False
+            elif additional_changes_response == "true":
+                self.ask_for_human_input = True
+                # Add human feedback to messages
+                self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
+                # Invoke the loop again with updated messages
+                formatted_answer = self._invoke_loop()
+
+                if self.crew and self.crew._train:
+                    self._handle_crew_training_output(formatted_answer)
+            else:
+                # Unexpected response
+                self._printer.print(
+                    content=f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
+                    color="red",
+                )
+                self.ask_for_human_input = False
+
+        return formatted_answer

src/crewai/agents/tools_handler.py

@@ -1,6 +1,6 @@
 from typing import Any, Optional, Union
 
-from ..tools.cache_tools import CacheTools
+from ..tools.cache_tools.cache_tools import CacheTools
 from ..tools.tool_calling import InstructorToolCalling, ToolCalling
 from .cache.cache_handler import CacheHandler
 

src/crewai/cli/add_crew_to_flow.py

@@ -0,0 +1,70 @@
+from pathlib import Path
+
+import click
+
+from crewai.cli.utils import copy_template
+
+
+def add_crew_to_flow(crew_name: str) -> None:
+    """Add a new crew to the current flow."""
+    # Check if pyproject.toml exists in the current directory
+    if not Path("pyproject.toml").exists():
+        print("This command must be run from the root of a flow project.")
+        raise click.ClickException(
+            "This command must be run from the root of a flow project."
+        )
+
+    # Determine the flow folder based on the current directory
+    flow_folder = Path.cwd()
+    crews_folder = flow_folder / "src" / flow_folder.name / "crews"
+
+    if not crews_folder.exists():
+        print("Crews folder does not exist in the current flow.")
+        raise click.ClickException("Crews folder does not exist in the current flow.")
+
+    # Create the crew within the flow's crews directory
+    create_embedded_crew(crew_name, parent_folder=crews_folder)
+
+    click.echo(
+        f"Crew {crew_name} added to the current flow successfully!",
+    )
+
+
+def create_embedded_crew(crew_name: str, parent_folder: Path) -> None:
+    """Create a new crew within an existing flow project."""
+    folder_name = crew_name.replace(" ", "_").replace("-", "_").lower()
+    class_name = crew_name.replace("_", " ").replace("-", " ").title().replace(" ", "")
+
+    crew_folder = parent_folder / folder_name
+
+    if crew_folder.exists():
+        if not click.confirm(
+            f"Crew {folder_name} already exists. Do you want to override it?"
+        ):
+            click.secho("Operation cancelled.", fg="yellow")
+            return
+        click.secho(f"Overriding crew {folder_name}...", fg="green", bold=True)
+    else:
+        click.secho(f"Creating crew {folder_name}...", fg="green", bold=True)
+        crew_folder.mkdir(parents=True)
+
+    # Create config and crew.py files
+    config_folder = crew_folder / "config"
+    config_folder.mkdir(exist_ok=True)
+
+    templates_dir = Path(__file__).parent / "templates" / "crew"
+    config_template_files = ["agents.yaml", "tasks.yaml"]
+    crew_template_file = f"{folder_name}.py"  # Updated file name
+
+    for file_name in config_template_files:
+        src_file = templates_dir / "config" / file_name
+        dst_file = config_folder / file_name
+        copy_template(src_file, dst_file, crew_name, class_name, folder_name)
+
+    src_file = templates_dir / "crew.py"
+    dst_file = crew_folder / crew_template_file
+    copy_template(src_file, dst_file, crew_name, class_name, folder_name)
+
+    click.secho(
+        f"Crew {crew_name} added to the flow successfully!", fg="green", bold=True
+    )

src/crewai/cli/authentication/main.py

@@ -7,6 +7,7 @@ from rich.console import Console
 
 from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN
 from .utils import TokenManager, validate_token
+from crewai.cli.tools.main import ToolCommand
 
 console = Console()
 
@@ -34,7 +35,9 @@ class AuthenticationCommand:
             "scope": "openid",
             "audience": AUTH0_AUDIENCE,
         }
-        response = requests.post(url=self.DEVICE_CODE_URL, data=device_code_payload)
+        response = requests.post(
+            url=self.DEVICE_CODE_URL, data=device_code_payload, timeout=20
+        )
         response.raise_for_status()
         return response.json()
 
@@ -54,14 +57,29 @@ class AuthenticationCommand:
 
         attempts = 0
         while True and attempts < 5:
-            response = requests.post(self.TOKEN_URL, data=token_payload)
+            response = requests.post(self.TOKEN_URL, data=token_payload, timeout=30)
             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")
+
+                try:
+                    ToolCommand().login()
+                except Exception:
+                    console.print(
+                        "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.",
+                        style="yellow",
+                    )
+                    console.print(
+                        "Other features will work normally, but you may experience limitations "
+                        "with downloading and publishing tools."
+                        "\nRun [bold]crewai login[/bold] to try logging in again.\n",
+                        style="yellow",
+                    )
+
+                console.print("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n")
                 return
 
             if token_data["error"] not in ("authorization_pending", "slow_down"):

src/crewai/cli/authentication/token.py

@@ -0,0 +1,10 @@
+from .utils import TokenManager
+
+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/cli.py

@@ -3,9 +3,9 @@ from typing import Optional
 import click
 import pkg_resources
 
+from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
-from crewai.cli.create_pipeline import create_pipeline
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
@@ -25,25 +25,24 @@ from .update_crew import update_crew
 
 
 @click.group()
+@click.version_option(pkg_resources.get_distribution("crewai").version)
 def crewai():
     """Top-level command group for crewai."""
 
 
 @crewai.command()
-@click.argument("type", type=click.Choice(["crew", "pipeline", "flow"]))
+@click.argument("type", type=click.Choice(["crew", "flow"]))
 @click.argument("name")
-def create(type, name):
-    """Create a new crew, pipeline, or flow."""
+@click.option("--provider", type=str, help="The provider to use for the crew")
+@click.option("--skip_provider", is_flag=True, help="Skip provider validation")
+def create(type, name, provider, skip_provider=False):
+    """Create a new crew, or flow."""
     if type == "crew":
-        create_crew(name)
-    elif type == "pipeline":
-        create_pipeline(name)
+        create_crew(name, provider, skip_provider)
     elif type == "flow":
         create_flow(name)
     else:
-        click.secho(
-            "Error: Invalid type. Must be 'crew', 'pipeline', or 'flow'.", fg="red"
-        )
+        click.secho("Error: Invalid type. Must be 'crew' or 'flow'.", fg="red")
 
 
 @crewai.command()
@@ -52,7 +51,10 @@ def create(type, name):
 )
 def version(tools):
     """Show the installed version of crewai."""
-    crewai_version = pkg_resources.get_distribution("crewai").version
+    try:
+        crewai_version = pkg_resources.get_distribution("crewai").version
+    except Exception:
+        crewai_version = "unknown version"
     click.echo(f"crewai version: {crewai_version}")
 
     if tools:
@@ -133,6 +135,7 @@ def log_tasks_outputs() -> None:
 @click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory")
 @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
 @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
+@click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage")
 @click.option(
     "-k",
     "--kickoff-outputs",
@@ -140,17 +143,24 @@ def log_tasks_outputs() -> None:
     help="Reset LATEST KICKOFF TASK OUTPUTS",
 )
 @click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
-def reset_memories(long, short, entities, kickoff_outputs, all):
+def reset_memories(
+    long: bool,
+    short: bool,
+    entities: bool,
+    knowledge: bool,
+    kickoff_outputs: bool,
+    all: bool,
+) -> None:
     """
     Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
     """
     try:
-        if not all and not (long or short or entities or kickoff_outputs):
+        if not all and not (long or short or entities or knowledge or kickoff_outputs):
             click.echo(
                 "Please specify at least one memory type to reset using the appropriate flags."
             )
             return
-        reset_memories_command(long, short, entities, kickoff_outputs, all)
+        reset_memories_command(long, short, entities, knowledge, kickoff_outputs, all)
     except Exception as e:
         click.echo(f"An error occurred while resetting memories: {e}", err=True)
 
@@ -176,10 +186,16 @@ def test(n_iterations: int, model: str):
     evaluate_crew(n_iterations, model)
 
 
-@crewai.command()
-def install():
+@crewai.command(
+    context_settings=dict(
+        ignore_unknown_options=True,
+        allow_extra_args=True,
+    )
+)
+@click.pass_context
+def install(context):
     """Install the Crew."""
-    install_crew()
+    install_crew(context.args)
 
 
 @crewai.command()
@@ -318,5 +334,13 @@ def flow_plot():
     plot_flow()
 
 
+@flow.command(name="add-crew")
+@click.argument("crew_name")
+def flow_add_crew(crew_name):
+    """Add a crew to an existing flow."""
+    click.echo(f"Adding crew {crew_name} to the flow")
+    add_crew_to_flow(crew_name)
+
+
 if __name__ == "__main__":
     crewai()

src/crewai/cli/command.py

@@ -2,7 +2,7 @@ import requests
 from requests.exceptions import JSONDecodeError
 from rich.console import Console
 from crewai.cli.plus_api import PlusAPI
-from crewai.cli.utils import get_auth_token
+from crewai.cli.authentication.token import get_auth_token
 from crewai.telemetry.telemetry import Telemetry
 
 console = Console()

src/crewai/cli/config.py

@@ -0,0 +1,38 @@
+import json
+from pathlib import Path
+from pydantic import BaseModel, Field
+from typing import Optional
+
+DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
+
+class Settings(BaseModel):
+    tool_repository_username: Optional[str] = Field(None, description="Username for interacting with the Tool Repository")
+    tool_repository_password: Optional[str] = Field(None, description="Password for interacting with the Tool Repository")
+    config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True)
+
+    def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):
+        """Load Settings from config path"""
+        config_path.parent.mkdir(parents=True, exist_ok=True)
+
+        file_data = {}
+        if config_path.is_file():
+            try:
+                with config_path.open("r") as f:
+                    file_data = json.load(f)
+            except json.JSONDecodeError:
+                file_data = {}
+
+        merged_data = {**file_data, **data}
+        super().__init__(config_path=config_path, **merged_data)
+
+    def dump(self) -> None:
+        """Save current settings to settings.json"""
+        if self.config_path.is_file():
+            with self.config_path.open("r") as f:
+                existing_data = json.load(f)
+        else:
+            existing_data = {}
+
+        updated_data = {**existing_data, **self.model_dump(exclude_unset=True)}
+        with self.config_path.open("w") as f:
+            json.dump(updated_data, f, indent=4)

src/crewai/cli/constants.py

@@ -1,19 +1,164 @@
 ENV_VARS = {
-    'openai': ['OPENAI_API_KEY'],
-    'anthropic': ['ANTHROPIC_API_KEY'],
-    'gemini': ['GEMINI_API_KEY'],
-    'groq': ['GROQ_API_KEY'],
-    'ollama': ['FAKE_KEY'],
+    "openai": [
+        {
+            "prompt": "Enter your OPENAI API key (press Enter to skip)",
+            "key_name": "OPENAI_API_KEY",
+        }
+    ],
+    "anthropic": [
+        {
+            "prompt": "Enter your ANTHROPIC API key (press Enter to skip)",
+            "key_name": "ANTHROPIC_API_KEY",
+        }
+    ],
+    "gemini": [
+        {
+            "prompt": "Enter your GEMINI API key (press Enter to skip)",
+            "key_name": "GEMINI_API_KEY",
+        }
+    ],
+    "groq": [
+        {
+            "prompt": "Enter your GROQ API key (press Enter to skip)",
+            "key_name": "GROQ_API_KEY",
+        }
+    ],
+    "watson": [
+        {
+            "prompt": "Enter your WATSONX URL (press Enter to skip)",
+            "key_name": "WATSONX_URL",
+        },
+        {
+            "prompt": "Enter your WATSONX API Key (press Enter to skip)",
+            "key_name": "WATSONX_APIKEY",
+        },
+        {
+            "prompt": "Enter your WATSONX Project Id (press Enter to skip)",
+            "key_name": "WATSONX_PROJECT_ID",
+        },
+    ],
+    "ollama": [
+        {
+            "default": True,
+            "API_BASE": "http://localhost:11434",
+        }
+    ],
+    "bedrock": [
+        {
+            "prompt": "Enter your AWS Access Key ID (press Enter to skip)",
+            "key_name": "AWS_ACCESS_KEY_ID",
+        },
+        {
+            "prompt": "Enter your AWS Secret Access Key (press Enter to skip)",
+            "key_name": "AWS_SECRET_ACCESS_KEY",
+        },
+        {
+            "prompt": "Enter your AWS Region Name (press Enter to skip)",
+            "key_name": "AWS_REGION_NAME",
+        },
+    ],
+    "azure": [
+        {
+            "prompt": "Enter your Azure deployment name (must start with 'azure/')",
+            "key_name": "model",
+        },
+        {
+            "prompt": "Enter your AZURE API key (press Enter to skip)",
+            "key_name": "AZURE_API_KEY",
+        },
+        {
+            "prompt": "Enter your AZURE API base URL (press Enter to skip)",
+            "key_name": "AZURE_API_BASE",
+        },
+        {
+            "prompt": "Enter your AZURE API version (press Enter to skip)",
+            "key_name": "AZURE_API_VERSION",
+        },
+    ],
+    "cerebras": [
+        {
+            "prompt": "Enter your Cerebras model name (must start with 'cerebras/')",
+            "key_name": "model",
+        },
+        {
+            "prompt": "Enter your Cerebras API version (press Enter to skip)",
+            "key_name": "CEREBRAS_API_KEY",
+        },
+    ],
 }
 
-PROVIDERS = ['openai', 'anthropic', 'gemini', 'groq', 'ollama']
+
+PROVIDERS = [
+    "openai",
+    "anthropic",
+    "gemini",
+    "groq",
+    "ollama",
+    "watson",
+    "bedrock",
+    "azure",
+    "cerebras",
+]
 
 MODELS = {
-    'openai': ['gpt-4', 'gpt-4o', 'gpt-4o-mini', 'o1-mini', 'o1-preview'],
-    'anthropic': ['claude-3-5-sonnet-20240620', 'claude-3-sonnet-20240229', 'claude-3-opus-20240229', 'claude-3-haiku-20240307'],
-    'gemini': ['gemini-1.5-flash', 'gemini-1.5-pro', 'gemini-gemma-2-9b-it', 'gemini-gemma-2-27b-it'],
-    'groq': ['llama-3.1-8b-instant', 'llama-3.1-70b-versatile', 'llama-3.1-405b-reasoning', 'gemma2-9b-it', 'gemma-7b-it'],
-    'ollama': ['llama3.1', 'mixtral'],
+    "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
+    "anthropic": [
+        "claude-3-5-sonnet-20240620",
+        "claude-3-sonnet-20240229",
+        "claude-3-opus-20240229",
+        "claude-3-haiku-20240307",
+    ],
+    "gemini": [
+        "gemini/gemini-1.5-flash",
+        "gemini/gemini-1.5-pro",
+        "gemini/gemini-gemma-2-9b-it",
+        "gemini/gemini-gemma-2-27b-it",
+    ],
+    "groq": [
+        "groq/llama-3.1-8b-instant",
+        "groq/llama-3.1-70b-versatile",
+        "groq/llama-3.1-405b-reasoning",
+        "groq/gemma2-9b-it",
+        "groq/gemma-7b-it",
+    ],
+    "ollama": ["ollama/llama3.1", "ollama/mixtral"],
+    "watson": [
+        "watsonx/meta-llama/llama-3-1-70b-instruct",
+        "watsonx/meta-llama/llama-3-1-8b-instruct",
+        "watsonx/meta-llama/llama-3-2-11b-vision-instruct",
+        "watsonx/meta-llama/llama-3-2-1b-instruct",
+        "watsonx/meta-llama/llama-3-2-90b-vision-instruct",
+        "watsonx/meta-llama/llama-3-405b-instruct",
+        "watsonx/mistral/mistral-large",
+        "watsonx/ibm/granite-3-8b-instruct",
+    ],
+    "bedrock": [
+        "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
+        "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+        "bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
+        "bedrock/anthropic.claude-v2:1",
+        "bedrock/anthropic.claude-v2",
+        "bedrock/anthropic.claude-instant-v1",
+        "bedrock/meta.llama3-1-405b-instruct-v1:0",
+        "bedrock/meta.llama3-1-70b-instruct-v1:0",
+        "bedrock/meta.llama3-1-8b-instruct-v1:0",
+        "bedrock/meta.llama3-70b-instruct-v1:0",
+        "bedrock/meta.llama3-8b-instruct-v1:0",
+        "bedrock/amazon.titan-text-lite-v1",
+        "bedrock/amazon.titan-text-express-v1",
+        "bedrock/cohere.command-text-v14",
+        "bedrock/ai21.j2-mid-v1",
+        "bedrock/ai21.j2-ultra-v1",
+        "bedrock/ai21.jamba-instruct-v1:0",
+        "bedrock/meta.llama2-13b-chat-v1",
+        "bedrock/meta.llama2-70b-chat-v1",
+        "bedrock/mistral.mistral-7b-instruct-v0:2",
+        "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
+    ],
 }
 
-JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+
+
+LITELLM_PARAMS = ["api_key", "api_base", "api_version"]

src/crewai/cli/create_crew.py

@@ -1,8 +1,16 @@
+import shutil
+import sys
 from pathlib import Path
+
 import click
+
+from crewai.cli.constants import ENV_VARS, MODELS
+from crewai.cli.provider import (
+    get_provider_data,
+    select_model,
+    select_provider,
+)
 from crewai.cli.utils import copy_template, load_env_vars, write_env_file
-from crewai.cli.provider import get_provider_data, select_provider, PROVIDERS
-from crewai.cli.constants import ENV_VARS
 
 
 def create_folder_structure(name, parent_folder=None):
@@ -14,24 +22,28 @@ def create_folder_structure(name, parent_folder=None):
     else:
         folder_path = Path(folder_name)
 
+    if folder_path.exists():
+        if not click.confirm(
+            f"Folder {folder_name} already exists. Do you want to override it?"
+        ):
+            click.secho("Operation cancelled.", fg="yellow")
+            sys.exit(0)
+        click.secho(f"Overriding folder {folder_name}...", fg="green", bold=True)
+        shutil.rmtree(folder_path)  # Delete the existing folder and its contents
+
     click.secho(
         f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
         fg="green",
         bold=True,
     )
 
-    if not folder_path.exists():
-        folder_path.mkdir(parents=True)
-        (folder_path / "tests").mkdir(exist_ok=True)
-        if not parent_folder:
-            (folder_path / "src" / folder_name).mkdir(parents=True)
-            (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
-            (folder_path / "src" / folder_name / "config").mkdir(parents=True)
-    else:
-        click.secho(
-            f"\tFolder {folder_name} already exists.",
-            fg="yellow",
-        )
+    folder_path.mkdir(parents=True)
+    (folder_path / "tests").mkdir(exist_ok=True)
+    (folder_path / "knowledge").mkdir(exist_ok=True)
+    if not parent_folder:
+        (folder_path / "src" / folder_name).mkdir(parents=True)
+        (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
+        (folder_path / "src" / folder_name / "config").mkdir(parents=True)
 
     return folder_path, folder_name, class_name
 
@@ -41,7 +53,14 @@ def copy_template_files(folder_path, name, class_name, parent_folder):
     templates_dir = package_dir / "templates" / "crew"
 
     root_template_files = (
-        [".gitignore", "pyproject.toml", "README.md"] if not parent_folder else []
+        [
+            ".gitignore",
+            "pyproject.toml",
+            "README.md",
+            "knowledge/user_preference.txt",
+        ]
+        if not parent_folder
+        else []
     )
     tools_template_files = ["tools/custom_tool.py", "tools/__init__.py"]
     config_template_files = ["config/agents.yaml", "config/tasks.yaml"]
@@ -70,43 +89,96 @@ def copy_template_files(folder_path, name, class_name, parent_folder):
             copy_template(src_file, dst_file, name, class_name, folder_path.name)
 
 
-def create_crew(name, parent_folder=None):
+def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
     folder_path, folder_name, class_name = create_folder_structure(name, parent_folder)
     env_vars = load_env_vars(folder_path)
+    if not skip_provider:
+        if not provider:
+            provider_models = get_provider_data()
+            if not provider_models:
+                return
 
-    provider_models = get_provider_data()
-    if not provider_models:
-        return
+        existing_provider = None
+        for provider, env_keys in ENV_VARS.items():
+            if any(
+                "key_name" in details and details["key_name"] in env_vars
+                for details in env_keys
+            ):
+                existing_provider = provider
+                break
 
-    selected_provider = select_provider(provider_models)
-    if not selected_provider:
-        return
-    provider = selected_provider
+        if existing_provider:
+            if not click.confirm(
+                f"Found existing environment variable configuration for {existing_provider.capitalize()}. Do you want to override it?"
+            ):
+                click.secho("Keeping existing provider configuration.", fg="yellow")
+                return
 
-    # selected_model = select_model(provider, provider_models)
-    # if not selected_model:
-    #     return
-    # model = selected_model
+        provider_models = get_provider_data()
+        if not provider_models:
+            return
 
-    if provider in PROVIDERS:
-        api_key_var = ENV_VARS[provider][0]
-    else:
-        api_key_var = click.prompt(
-            f"Enter the environment variable name for your {provider.capitalize()} API key",
-            type=str,
-        )
+        while True:
+            selected_provider = select_provider(provider_models)
+            if selected_provider is None:  # User typed 'q'
+                click.secho("Exiting...", fg="yellow")
+                sys.exit(0)
+            if selected_provider:  # Valid selection
+                break
+            click.secho(
+                "No provider selected. Please try again or press 'q' to exit.", fg="red"
+            )
 
-    env_vars = {api_key_var: "YOUR_API_KEY_HERE"}
-    write_env_file(folder_path, env_vars)
+        # Check if the selected provider has predefined models
+        if selected_provider in MODELS and MODELS[selected_provider]:
+            while True:
+                selected_model = select_model(selected_provider, provider_models)
+                if selected_model is None:  # User typed 'q'
+                    click.secho("Exiting...", fg="yellow")
+                    sys.exit(0)
+                if selected_model:  # Valid selection
+                    break
+                click.secho(
+                    "No model selected. Please try again or press 'q' to exit.",
+                    fg="red",
+                )
+            env_vars["MODEL"] = selected_model
 
-    # env_vars['MODEL'] = model
-    # click.secho(f"Selected model: {model}", fg="green")
+        # Check if the selected provider requires API keys
+        if selected_provider in ENV_VARS:
+            provider_env_vars = ENV_VARS[selected_provider]
+            for details in provider_env_vars:
+                if details.get("default", False):
+                    # Automatically add default key-value pairs
+                    for key, value in details.items():
+                        if key not in ["prompt", "key_name", "default"]:
+                            env_vars[key] = value
+                elif "key_name" in details:
+                    # Prompt for non-default key-value pairs
+                    prompt = details["prompt"]
+                    key_name = details["key_name"]
+                    api_key_value = click.prompt(prompt, default="", show_default=False)
+
+                    if api_key_value.strip():
+                        env_vars[key_name] = api_key_value
+
+        if env_vars:
+            write_env_file(folder_path, env_vars)
+            click.secho("API keys and model saved to .env file", fg="green")
+        else:
+            click.secho(
+                "No API keys provided. Skipping .env file creation.", fg="yellow"
+            )
+
+        click.secho(f"Selected model: {env_vars.get('MODEL', 'N/A')}", fg="green")
 
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "crew"
 
     root_template_files = (
-        [".gitignore", "pyproject.toml", "README.md"] if not parent_folder else []
+        [".gitignore", "pyproject.toml", "README.md", "knowledge/user_preference.txt"]
+        if not parent_folder
+        else []
     )
     tools_template_files = ["tools/custom_tool.py", "tools/__init__.py"]
     config_template_files = ["config/agents.yaml", "config/tasks.yaml"]

src/crewai/cli/create_pipeline.py

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

src/crewai/cli/install_crew.py

@@ -3,12 +3,13 @@ import subprocess
 import click
 
 
-def install_crew() -> None:
+def install_crew(proxy_options: list[str]) -> None:
     """
     Install the crew by running the UV command to lock and install.
     """
     try:
-        subprocess.run(["uv", "sync"], check=True, capture_output=False, text=True)
+        command = ["uv", "sync"] + proxy_options
+        subprocess.run(command, 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)

src/crewai/cli/plot_flow.py

@@ -7,7 +7,7 @@ def plot_flow() -> None:
     """
     Plot the flow by running a command in the UV environment.
     """
-    command = ["uv", "run", "plot_flow"]
+    command = ["uv", "run", "plot"]
 
     try:
         result = subprocess.run(command, capture_output=False, text=True, check=True)

src/crewai/cli/plus_api.py

@@ -1,7 +1,7 @@
 from typing import Optional
 import requests
 from os import getenv
-from crewai.cli.utils import get_crewai_version
+from crewai.cli.version import get_crewai_version
 from urllib.parse import urljoin
 
 

src/crewai/cli/provider.py

@@ -1,67 +1,91 @@
 import json
 import time
-import requests
 from collections import defaultdict
+from pathlib import Path
+
 import click
-from pathlib import Path 
-from crewai.cli.constants import PROVIDERS, MODELS, JSON_URL
+import requests
+
+from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS
+
 
 def select_choice(prompt_message, choices):
     """
     Presents a list of choices to the user and prompts them to select one.
-    
+
     Args:
     - prompt_message (str): The message to display to the user before presenting the choices.
     - choices (list): A list of options to present to the user.
-    
+
     Returns:
-    - str: The selected choice from the list, or None if the operation is aborted or an invalid selection is made.
+    - str: The selected choice from the list, or None if the user chooses to quit.
     """
+
+    provider_models = get_provider_data()
+    if not provider_models:
+        return
     click.secho(prompt_message, fg="cyan")
     for idx, choice in enumerate(choices, start=1):
         click.secho(f"{idx}. {choice}", fg="cyan")
-    try:
-        selected_index = click.prompt("Enter the number of your choice", type=int) - 1
-    except click.exceptions.Abort:
-        click.secho("Operation aborted by the user.", fg="red")
-        return None
-    if not (0 <= selected_index < len(choices)):
-        click.secho("Invalid selection.", fg="red")
-        return None
-    return choices[selected_index]
+    click.secho("q. Quit", fg="cyan")
+
+    while True:
+        choice = click.prompt(
+            "Enter the number of your choice or 'q' to quit", type=str
+        )
+
+        if choice.lower() == "q":
+            return None
+
+        try:
+            selected_index = int(choice) - 1
+            if 0 <= selected_index < len(choices):
+                return choices[selected_index]
+        except ValueError:
+            pass
+
+        click.secho(
+            "Invalid selection. Please select a number between 1 and 6 or 'q' to quit.",
+            fg="red",
+        )
+
 
 def select_provider(provider_models):
     """
     Presents a list of providers to the user and prompts them to select one.
-    
+
     Args:
     - provider_models (dict): A dictionary of provider models.
-    
+
     Returns:
-    - str: The selected provider, or None if the operation is aborted or an invalid selection is made.
+    - str: The selected provider
+    - None: If user explicitly quits
     """
     predefined_providers = [p.lower() for p in PROVIDERS]
     all_providers = sorted(set(predefined_providers + list(provider_models.keys())))
 
-    provider = select_choice("Select a provider to set up:", predefined_providers + ['other'])
-    if not provider:
+    provider = select_choice(
+        "Select a provider to set up:", predefined_providers + ["other"]
+    )
+    if provider is None:  # User typed 'q'
         return None
-    provider = provider.lower()
 
-    if provider == 'other':
+    if provider == "other":
         provider = select_choice("Select a provider from the full list:", all_providers)
-        if not provider:
+        if provider is None:  # User typed 'q'
             return None
-    return provider
+
+    return provider.lower() if provider else False
+
 
 def select_model(provider, provider_models):
     """
     Presents a list of models for a given provider to the user and prompts them to select one.
-    
+
     Args:
     - provider (str): The provider for which to select a model.
     - provider_models (dict): A dictionary of provider models.
-    
+
     Returns:
     - str: The selected model, or None if the operation is aborted or an invalid selection is made.
     """
@@ -76,37 +100,49 @@ def select_model(provider, provider_models):
         click.secho(f"No models available for provider '{provider}'.", fg="red")
         return None
 
-    selected_model = select_choice(f"Select a model to use for {provider.capitalize()}:", available_models)
+    selected_model = select_choice(
+        f"Select a model to use for {provider.capitalize()}:", available_models
+    )
     return selected_model
 
+
 def load_provider_data(cache_file, cache_expiry):
     """
     Loads provider data from a cache file if it exists and is not expired. If the cache is expired or corrupted, it fetches the data from the web.
-    
+
     Args:
     - cache_file (Path): The path to the cache file.
     - cache_expiry (int): The cache expiry time in seconds.
-    
+
     Returns:
     - dict or None: The loaded provider data or None if the operation fails.
     """
     current_time = time.time()
-    if cache_file.exists() and (current_time - cache_file.stat().st_mtime) < cache_expiry:
+    if (
+        cache_file.exists()
+        and (current_time - cache_file.stat().st_mtime) < cache_expiry
+    ):
         data = read_cache_file(cache_file)
         if data:
             return data
-        click.secho("Cache is corrupted. Fetching provider data from the web...", fg="yellow")
+        click.secho(
+            "Cache is corrupted. Fetching provider data from the web...", fg="yellow"
+        )
     else:
-        click.secho("Cache expired or not found. Fetching provider data from the web...", fg="cyan")
+        click.secho(
+            "Cache expired or not found. Fetching provider data from the web...",
+            fg="cyan",
+        )
     return fetch_provider_data(cache_file)
 
+
 def read_cache_file(cache_file):
     """
     Reads and returns the JSON content from a cache file. Returns None if the file contains invalid JSON.
-    
+
     Args:
     - cache_file (Path): The path to the cache file.
-    
+
     Returns:
     - dict or None: The JSON content of the cache file or None if the JSON is invalid.
     """
@@ -116,18 +152,19 @@ def read_cache_file(cache_file):
     except json.JSONDecodeError:
         return None
 
+
 def fetch_provider_data(cache_file):
     """
     Fetches provider data from a specified URL and caches it to a file.
-    
+
     Args:
     - cache_file (Path): The path to the cache file.
-    
+
     Returns:
     - dict or None: The fetched provider data or None if the operation fails.
     """
     try:
-        response = requests.get(JSON_URL, stream=True, timeout=10)
+        response = requests.get(JSON_URL, stream=True, timeout=60)
         response.raise_for_status()
         data = download_data(response)
         with open(cache_file, "w") as f:
@@ -139,38 +176,42 @@ def fetch_provider_data(cache_file):
         click.secho("Error parsing provider data. Invalid JSON format.", fg="red")
     return None
 
+
 def download_data(response):
     """
     Downloads data from a given HTTP response and returns the JSON content.
-    
+
     Args:
     - response (requests.Response): The HTTP response object.
-    
+
     Returns:
     - dict: The JSON content of the response.
     """
-    total_size = int(response.headers.get('content-length', 0))
+    total_size = int(response.headers.get("content-length", 0))
     block_size = 8192
     data_chunks = []
-    with click.progressbar(length=total_size, label='Downloading', show_pos=True) as progress_bar:
+    with click.progressbar(
+        length=total_size, label="Downloading", show_pos=True
+    ) as progress_bar:
         for chunk in response.iter_content(block_size):
             if chunk:
                 data_chunks.append(chunk)
                 progress_bar.update(len(chunk))
-    data_content = b''.join(data_chunks)
-    return json.loads(data_content.decode('utf-8'))
+    data_content = b"".join(data_chunks)
+    return json.loads(data_content.decode("utf-8"))
+
 
 def get_provider_data():
     """
     Retrieves provider data from a cache file, filters out models based on provider criteria, and returns a dictionary of providers mapped to their models.
-    
+
     Returns:
     - dict or None: A dictionary of providers mapped to their models or None if the operation fails.
     """
-    cache_dir = Path.home() / '.crewai'
+    cache_dir = Path.home() / ".crewai"
     cache_dir.mkdir(exist_ok=True)
-    cache_file = cache_dir / 'provider_cache.json'
-    cache_expiry = 24 * 3600 
+    cache_file = cache_dir / "provider_cache.json"
+    cache_expiry = 24 * 3600
 
     data = load_provider_data(cache_file, cache_expiry)
     if not data:
@@ -179,8 +220,8 @@ def get_provider_data():
     provider_models = defaultdict(list)
     for model_name, properties in data.items():
         provider = properties.get("litellm_provider", "").strip().lower()
-        if 'http' in provider or provider == 'other':
+        if "http" in provider or provider == "other":
             continue
         if provider:
             provider_models[provider].append(model_name)
-    return provider_models
+    return provider_models

src/crewai/cli/reset_memories_command.py

@@ -5,9 +5,17 @@ from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 
 
-def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
+def reset_memories_command(
+    long,
+    short,
+    entity,
+    knowledge,
+    kickoff_outputs,
+    all,
+) -> None:
     """
     Reset the crew memories.
 
@@ -17,6 +25,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
       entity (bool): Whether to reset the entity memory.
       kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
       all (bool): Whether to reset all memories.
+      knowledge (bool): Whether to reset the knowledge.
     """
 
     try:
@@ -25,6 +34,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
             EntityMemory().reset()
             LongTermMemory().reset()
             TaskOutputStorageHandler().reset()
+            KnowledgeStorage().reset()
             click.echo("All memories have been reset.")
         else:
             if long:
@@ -40,6 +50,9 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
             if kickoff_outputs:
                 TaskOutputStorageHandler().reset()
                 click.echo("Latest Kickoff outputs stored has been reset.")
+            if knowledge:
+                KnowledgeStorage().reset()
+                click.echo("Knowledge has been reset.")
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while resetting the memories: {e}", err=True)

src/crewai/cli/run_crew.py

@@ -1,10 +1,10 @@
 import subprocess
 
 import click
-import tomllib
 from packaging import version
 
-from crewai.cli.utils import get_crewai_version
+from crewai.cli.utils import read_toml
+from crewai.cli.version import get_crewai_version
 
 
 def run_crew() -> None:
@@ -15,10 +15,9 @@ def run_crew() -> None:
     crewai_version = get_crewai_version()
     min_required_version = "0.71.0"
 
-    with open("pyproject.toml", "rb") as f:
-        data = tomllib.load(f)
+    pyproject_data = read_toml()
 
-    if data.get("tool", {}).get("poetry") and (
+    if pyproject_data.get("tool", {}).get("poetry") and (
         version.parse(crewai_version) < version.parse(min_required_version)
     ):
         click.secho(
@@ -26,7 +25,6 @@ def run_crew() -> None:
             f"Please run `crewai update` to update your pyproject.toml to use uv.",
             fg="red",
         )
-        print()
 
     try:
         subprocess.run(command, capture_output=False, text=True, check=True)
@@ -35,10 +33,7 @@ def run_crew() -> None:
         click.echo(f"An error occurred while running the crew: {e}", err=True)
         click.echo(e.output, err=True, nl=True)
 
-        with open("pyproject.toml", "rb") as f:
-            data = tomllib.load(f)
-
-        if data.get("tool", {}).get("poetry"):
+        if pyproject_data.get("tool", {}).get("poetry"):
             click.secho(
                 "It's possible that you are using an old version of crewAI that uses poetry, please run `crewai update` to update your pyproject.toml to use uv.",
                 fg="yellow",

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

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

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

@@ -12,6 +12,6 @@ reporting_task:
     Review the context you got and expand each topic into a full section for a report.
     Make sure the report is detailed and contains any and all relevant information.
   expected_output: >
-    A fully fledge reports with the mains topics, each with a full section of information.
+    A fully fledged report with the main topics, each with a full section of information.
     Formatted as markdown without '```'
   agent: reporting_analyst

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

@@ -1,21 +1,26 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 
-# Uncomment the following line to use an example of a custom tool
-# from {{folder_name}}.tools.custom_tool import MyCustomTool
-
-# Check our tools documentations for more information on how to use them
-# from crewai_tools import SerperDevTool
+# If you want to run a snippet of code before or after the crew starts, 
+# you can use the @before_kickoff and @after_kickoff decorators
+# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
 
 @CrewBase
-class {{crew_name}}Crew():
+class {{crew_name}}():
 	"""{{crew_name}} crew"""
 
+	# Learn more about YAML configuration files here:
+	# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
+	# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
+
+	# If you would like to add tools to your agents, you can learn more about it here:
+	# https://docs.crewai.com/concepts/agents#agent-tools
 	@agent
 	def researcher(self) -> Agent:
 		return Agent(
 			config=self.agents_config['researcher'],
-			# tools=[MyCustomTool()], # Example of custom tool, loaded on the beginning of file
 			verbose=True
 		)
 
@@ -26,6 +31,9 @@ class {{crew_name}}Crew():
 			verbose=True
 		)
 
+	# To learn more about structured task outputs, 
+	# task dependencies, and task callbacks, check out the documentation:
+	# https://docs.crewai.com/concepts/tasks#overview-of-a-task
 	@task
 	def research_task(self) -> Task:
 		return Task(
@@ -42,10 +50,13 @@ class {{crew_name}}Crew():
 	@crew
 	def crew(self) -> Crew:
 		"""Creates the {{crew_name}} crew"""
+		# To learn how to add knowledge sources to your crew, check out the documentation:
+		# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
+
 		return Crew(
 			agents=self.agents, # Automatically created by the @agent decorator
 			tasks=self.tasks, # Automatically created by the @task decorator
 			process=Process.sequential,
 			verbose=True,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
-		)
+		)

src/crewai/cli/templates/crew/knowledge/user_preference.txt

@@ -0,0 +1,4 @@
+User name is John Doe.
+User is an AI Engineer.
+User is interested in AI Agents.
+User is based in San Francisco, California.

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

@@ -1,6 +1,10 @@
 #!/usr/bin/env python
 import sys
-from {{folder_name}}.crew import {{crew_name}}Crew
+import warnings
+
+from {{folder_name}}.crew import {{crew_name}}
+
+warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 # This main file is intended to be a way for you to run your
 # crew locally, so refrain from adding unnecessary logic into this file.
@@ -14,7 +18,7 @@ def run():
     inputs = {
         'topic': 'AI LLMs'
     }
-    {{crew_name}}Crew().crew().kickoff(inputs=inputs)
+    {{crew_name}}().crew().kickoff(inputs=inputs)
 
 
 def train():
@@ -25,7 +29,7 @@ def train():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
+        {{crew_name}}().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
@@ -35,7 +39,7 @@ def replay():
     Replay the crew execution from a specific task.
     """
     try:
-        {{crew_name}}Crew().crew().replay(task_id=sys.argv[1])
+        {{crew_name}}().crew().replay(task_id=sys.argv[1])
 
     except Exception as e:
         raise Exception(f"An error occurred while replaying the crew: {e}")
@@ -48,7 +52,7 @@ def test():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
+        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while replaying the crew: {e}")

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

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<=3.13"
+requires-python = ">=3.10,<=3.12"
 dependencies = [
-    "crewai[tools]>=0.74.2,<1.0.0"
+    "crewai[tools]>=0.86.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -1,11 +1,18 @@
-from crewai_tools import BaseTool
+from crewai.tools import BaseTool
+from typing import Type
+from pydantic import BaseModel, Field
 
 
+class MyCustomToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+    argument: str = Field(..., description="Description of the argument.")
+
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = (
         "Clear description for what this tool is useful for, you agent will need this information to use it."
     )
+    args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
         # Implementation goes here

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

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

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

@@ -1,31 +1,47 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 
+# If you want to run a snippet of code before or after the crew starts,
+# you can use the @before_kickoff and @after_kickoff decorators
+# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
+
+
 @CrewBase
-class PoemCrew():
-	"""Poem Crew"""
+class PoemCrew:
+    """Poem Crew"""
 
-	agents_config = 'config/agents.yaml'
-	tasks_config = 'config/tasks.yaml'
+    # Learn more about YAML configuration files here:
+    # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
+    # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
+    agents_config = "config/agents.yaml"
+    tasks_config = "config/tasks.yaml"
 
-	@agent
-	def poem_writer(self) -> Agent:
-		return Agent(
-			config=self.agents_config['poem_writer'],
-		)
+    # If you would lik to add tools to your crew, you can learn more about it here:
+    # https://docs.crewai.com/concepts/agents#agent-tools
+    @agent
+    def poem_writer(self) -> Agent:
+        return Agent(
+            config=self.agents_config["poem_writer"],
+        )
 
-	@task
-	def write_poem(self) -> Task:
-		return Task(
-			config=self.tasks_config['write_poem'],
-		)
+    # To learn more about structured task outputs,
+    # task dependencies, and task callbacks, check out the documentation:
+    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
+    @task
+    def write_poem(self) -> Task:
+        return Task(
+            config=self.tasks_config["write_poem"],
+        )
 
-	@crew
-	def crew(self) -> Crew:
-		"""Creates the Research Crew"""
-		return Crew(
-			agents=self.agents, # Automatically created by the @agent decorator
-			tasks=self.tasks, # Automatically created by the @task decorator
-			process=Process.sequential,
-			verbose=True,
-		)
+    @crew
+    def crew(self) -> Crew:
+        """Creates the Research Crew"""
+        # To learn how to add knowledge sources to your crew, check out the documentation:
+        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
+
+        return Crew(
+            agents=self.agents,  # Automatically created by the @agent decorator
+            tasks=self.tasks,  # Automatically created by the @task decorator
+            process=Process.sequential,
+            verbose=True,
+        )

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

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<=3.13"
+requires-python = ">=3.10,<=3.12"
 dependencies = [
-    "crewai[tools]>=0.74.2,<1.0.0",
+    "crewai[tools]>=0.86.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -1,4 +1,13 @@
-from crewai_tools import BaseTool
+from typing import Type
+
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class MyCustomToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+
+    argument: str = Field(..., description="Description of the argument.")
 
 
 class MyCustomTool(BaseTool):
@@ -6,6 +15,7 @@ class MyCustomTool(BaseTool):
     description: str = (
         "Clear description for what this tool is useful for, you agent will need this information to use it."
     )
+    args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
         # Implementation goes here

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,17 +0,0 @@
-[tool.poetry]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-
-[tool.poetry.dependencies]
-python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.74.2,<1.0.0" }
-asyncio = "*"
-
-[tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

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

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

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

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

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

@@ -1,54 +0,0 @@
-# {{crew_name}} Crew
-
-Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
-
-## Installation
-
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
-
-First, if you haven't already, install Poetry:
-
-```bash
-pip install poetry
-```
-
-Next, navigate to your project directory and install the dependencies:
-
-1. First lock the dependencies and then install them:
-```bash
-crewai install
-```
-### Customizing
-
-**Add your `OPENAI_API_KEY` into the `.env` file**
-
-- Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
-- Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks
-- Modify `src/{{folder_name}}/crew.py` to add your own logic, tools and specific args
-- Modify `src/{{folder_name}}/main.py` to add custom inputs for your agents and tasks
-
-## Running the Project
-
-To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
-
-```bash
-crewai run
-```
-
-This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
-
-This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
-
-## Understanding Your Crew
-
-The {{name}} Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
-
-## Support
-
-For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
-- Visit our [documentation](https://docs.crewai.com)
-- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
-- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
-- [Chat with our docs](https://chatg.pt/DWjSBZn)
-
-Let's create wonders together with the power and simplicity of crewAI.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,21 +0,0 @@
-[project]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-requires-python = ">=3.10,<=3.13"
-dependencies = [
-    "crewai[tools]>=0.74.2,<1.0.0"
-]
-
-[project.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-run_crew = "{{folder_name}}.main:main"
-train = "{{folder_name}}.main:train"
-replay = "{{folder_name}}.main:replay"
-test = "{{folder_name}}.main:test"
-
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-

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

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

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

@@ -5,7 +5,7 @@ custom tools to power up your crews.
 
 ## Installing
 
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project
+Ensure you have Python >=3.10 <=3.12 installed on your system. This project
 uses [UV](https://docs.astral.sh/uv/) for dependency management and package
 handling, offering a seamless setup and execution experience.