add support for langfuse with litellm

* drop metadata requirement

* fix linting

* Update docs for new knowledge

* more linting

* more linting

* make save_documents private

* update docs to the new way we use knowledge and include clearing memory

.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"
 

.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"
 

README.md

@@ -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**
@@ -357,7 +357,7 @@ 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:
 

docs/concepts/agents.mdx

@@ -1,161 +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:
+In the CrewAI framework, an `Agent` is an autonomous unit that can:
-<ul>
+- Perform specific tasks
-    <li class='leading-3'>Perform tasks</li>
+- Make decisions based on its role and goal
-    <li class='leading-3'>Make decisions</li>
+- Use tools to accomplish objectives
-    <li class='leading-3'>Communicate with other agents</li>
+- Communicate and collaborate with other agents
-</ul>
+- 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                                                                                                                                                                                                                                    |
+| Attribute                               | Parameter                | Type                          | Description                                                                                                          |
-| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| :-------------------------------------- | :----------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
-| **Role**                   | `role`     | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
+| **Role**                                | `role`                   | `str`                         | Defines the agent's function and expertise within the crew.                                                           |
-| **Goal**                   | `goal`     | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
+| **Goal**                                | `goal`                   | `str`                         | The individual objective that guides the agent's decision-making.                                                     |
-| **Backstory**              | `backstory`| Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
+| **Backstory**                           | `backstory`              | `str`                         | Provides context and personality to the agent, enriching interactions.                                                |
-| **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.                                                         |
+| **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`    | 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.             |
+| **Tools** _(optional)_                  | `tools`                  | `List[BaseTool]`              | Capabilities or functions available to the agent. Defaults to 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`.                                                                                          |
+| **Function Calling LLM** _(optional)_   | `function_calling_llm`   | `Optional[Any]`               | Language model for tool calling, overrides crew's LLM if specified.                                                   |
-| **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 Iterations** _(optional)_         | `max_iter`               | `int`                         | Maximum iterations before the agent must provide its best answer. Default is 20.                                      |
-| **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 RPM** _(optional)_                | `max_rpm`                | `Optional[int]`               | Maximum requests per minute to avoid rate limits.                                                                     |
-| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
+| **Max Execution Time** _(optional)_     | `max_execution_time`     | `Optional[int]`               | Maximum time (in seconds) for task execution.                                                                         |
-| **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
+| **Memory** _(optional)_                 | `memory`                 | `bool`                        | Whether the agent should maintain memory of interactions. Default is True.                                            |
-| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.                                                                                                      |
+| **Verbose** _(optional)_                | `verbose`                | `bool`                        | Enable detailed execution logs for debugging. 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`.                                                               |
+| **Allow Delegation** _(optional)_       | `allow_delegation`       | `bool`                        | Allow the agent to delegate tasks to other agents. Default is False.                                                  |
-| **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
+| **Step Callback** _(optional)_          | `step_callback`          | `Optional[Any]`               | Function called after each agent step, overrides crew callback.                                                       |
-| **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Cache** _(optional)_                  | `cache`                  | `bool`                        | Enable caching for tool usage. Default is True.                                                                       |
-| **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
+| **System Template** _(optional)_        | `system_template`        | `Optional[str]`               | Custom system prompt template for the agent.                                                                          |
-| **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Prompt Template** _(optional)_        | `prompt_template`        | `Optional[str]`               | Custom prompt template for the agent.                                                                                 |
-| **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
+| **Response Template** _(optional)_      | `response_template`      | `Optional[str]`               | Custom response template for the agent.                                                                               |
-| **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`.                                                                                                                                                                  |
+| **Allow Code Execution** _(optional)_   | `allow_code_execution`   | `Optional[bool]`              | Enable code execution for the agent. Default is False.                                                                |
-| **Use System Prompt** *(optional)*     | `use_system_prompt`  | Adds the ability to not use system prompt (to support o1 models). Default is `True`.                                                                                                                                                                  |
+| **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`  | Summary strategy to avoid overflowing the context window. Default is `True`.                                                                                                                                                                  |
+| **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` | Determines the mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution on the host machine). Default is `safe`.                                                                                                          |
+| **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',
+    role="Senior Data Scientist",
-  goal='Extract actionable insights',
+    goal="Analyze and interpret complex datasets to provide actionable insights",
-  backstory="""You're a data analyst at a large company.
+    backstory="With over 10 years of experience in data science and machine learning, "
-    You're responsible for analyzing data and providing insights
+              "you excel at finding patterns in complex datasets.",
-    to the business.
+    llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
-    You're currently working on a project to analyze the
+    function_calling_llm=None,  # Optional: Separate LLM for tool calling
-    performance of our marketing campaigns.""",
+    memory=True,  # Default: True
-  tools=[my_tool1, my_tool2],  # Optional, defaults to an empty list
+    verbose=False,  # Default: False
-  llm=my_llm,  # Optional
+    allow_delegation=False,  # Default: False
-  function_calling_llm=my_llm,  # Optional
+    max_iter=20,  # Default: 20 iterations
-  max_iter=15,  # Optional
+    max_rpm=None,  # Optional: Rate limit for API calls
-  max_rpm=None, # Optional
+    max_execution_time=None,  # Optional: Maximum execution time in seconds
-  max_execution_time=None, # Optional
+    max_retry_limit=2,  # Default: 2 retries on error
-  verbose=True,  # Optional
+    allow_code_execution=False,  # Default: False
-  allow_delegation=False,  # Optional
+    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
-  step_callback=my_intermediate_step_callback,  # Optional
+    respect_context_window=True,  # Default: True
-  cache=True,  # Optional
+    use_system_prompt=True,  # Default: True
-  system_template=my_system_template,  # Optional
+    tools=[SerperDevTool()],  # Optional: List of tools
-  prompt_template=my_prompt_template,  # Optional
+    knowledge_sources=None,  # Optional: List of knowledge sources
-  response_template=my_response_template,  # Optional
+    embedder_config=None,  # Optional: Custom embedder configuration
-  config=my_config,  # Optional
+    system_template=None,  # Optional: Custom system prompt template
-  crew=my_crew,  # Optional
+    prompt_template=None,  # Optional: Custom prompt template
-  tools_handler=my_tools_handler,  # Optional
+    response_template=None,  # Optional: Custom response template
-  cache_handler=my_cache_handler,  # Optional
+    step_callback=None,  # Optional: Callback function for monitoring
-  callbacks=[callback1, callback2],  # Optional
-  allow_code_execution=True,  # Optional
-  max_retry_limit=2,  # Optional
-  use_system_prompt=True,  # Optional
-  respect_context_window=True,  # Optional
-  code_execution_mode='safe',  # Optional, defaults to 'safe'
 )
 ```
 
-## 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
+#### Code Development Agent
-agent = Agent(
+```python Code
-        role="{topic} specialist",
+dev_agent = Agent(
-        goal="Figure {goal} out",
+    role="Senior Python Developer",
-        backstory="I am the master of {role}",
+    goal="Write and debug Python code",
-        system_template="""<|start_header_id|>system<|end_header_id|>
+    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>   
+#### Memory and Context
-  **BaseAgent** includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
+- `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
+Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:
-from crewai import Agent, Task, Crew
+- [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools)
-from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
+- [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(
+# Create tools
-    role="agent role",
+search_tool = SerperDevTool()
-    goal="who is {input}?",
+wiki_tool = WikipediaTools()
-    backstory="agent backstory",
+
-    verbose=True,
+# 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, 
+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.
-you can create sophisticated AI systems that leverage the power of collaborative intelligence. The `code_execution_mode` attribute provides flexibility in how agents execute code, allowing for both secure and direct execution options.
+
+```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

@@ -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"
+- `TYPE`: Choose between "crew" or "flow"
-- `NAME`: Name of the crew or pipeline
+- `NAME`: Name of the crew or flow
-- `--router`: (Optional) Create a pipeline with router functionality
 
 Example:
 ```shell
 crewai create crew my_new_crew
-crewai create pipeline my_new_pipeline --router
+crewai create flow my_new_flow
 ```
 
 ### 2. Version

docs/concepts/crews.mdx

@@ -41,6 +41,155 @@ 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
 
@@ -188,4 +337,4 @@ 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

@@ -18,63 +18,60 @@ Flows allow you to create structured, event-driven workflows. They provide a sea
 
 4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
 
-5. **Input Flexibility**: Flows can accept inputs to initialize or update their state, with different handling for structured and unstructured state management.
-
 ## Getting Started
 
 Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
 
-### Passing Inputs to Flows
+```python Code
 
-Flows can accept inputs to initialize or update their state before execution. The way inputs are handled depends on whether the flow uses structured or unstructured state management.
-
-#### Structured State Management
-
-In structured state management, the flow's state is defined using a Pydantic `BaseModel`. Inputs must match the model's schema, and any updates will overwrite the default values.
-
-```python
 from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
+from dotenv import load_dotenv
+from litellm import completion
 
-class ExampleState(BaseModel):
-    counter: int = 0
-    message: str = ""
 
-class StructuredExampleFlow(Flow[ExampleState]):
+class ExampleFlow(Flow):
+    model = "gpt-4o-mini"
+
     @start()
-    def first_method(self):
+    def generate_city(self):
-        # Implementation
+        print("Starting flow")
 
-flow = StructuredExampleFlow()
+        response = completion(
-flow.kickoff(inputs={"counter": 10})
+            model=self.model,
-```
+            messages=[
+                {
+                    "role": "user",
+                    "content": "Return the name of a random city in the world.",
+                },
+            ],
+        )
 
-In this example, the `counter` is initialized to `10`, while `message` retains its default value.
+        random_city = response["choices"][0]["message"]["content"]
+        print(f"Random City: {random_city}")
 
-#### Unstructured State Management
+        return random_city
 
-In unstructured state management, the flow's state is a dictionary. You can pass any dictionary to update the state.
+    @listen(generate_city)
+    def generate_fun_fact(self, random_city):
+        response = completion(
+            model=self.model,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Tell me a fun fact about {random_city}",
+                },
+            ],
+        )
 
-```python
+        fun_fact = response["choices"][0]["message"]["content"]
-from crewai.flow.flow import Flow, listen, start
+        return fun_fact
 
-class UnstructuredExampleFlow(Flow):
-    @start()
-    def first_method(self):
-        # Implementation
 
-flow = UnstructuredExampleFlow()
-flow.kickoff(inputs={"counter": 5, "message": "Initial message"})
-```
 
-Here, both `counter` and `message` are updated based on the provided inputs.
+flow = ExampleFlow()
+result = flow.kickoff()
 
-**Note:** Ensure that inputs for structured state management adhere to the defined schema to avoid validation errors.
+print(f"Generated fun fact: {result}")
-
-### Example Flow
-
-```python
-# Existing example code
 ```
 
 In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
@@ -97,14 +94,14 @@ The `@listen()` decorator can be used in several ways:
 
 1. **Listening to a Method by Name**: You can pass the name of the method you want to listen to as a string. When that method completes, the listener method will be triggered.
 
-   ```python
+   ```python Code
    @listen("generate_city")
    def generate_fun_fact(self, random_city):
        # Implementation
    ```
 
 2. **Listening to a Method Directly**: You can pass the method itself. When that method completes, the listener method will be triggered.
-   ```python
+   ```python Code
    @listen(generate_city)
    def generate_fun_fact(self, random_city):
        # Implementation
@@ -121,7 +118,7 @@ When you run a Flow, the final output is determined by the last method that comp
 Here's how you can access the final output:
 
 <CodeGroup>
-```python
+```python Code
 from crewai.flow.flow import Flow, listen, start
 
 class OutputExampleFlow(Flow):
@@ -133,17 +130,18 @@ class OutputExampleFlow(Flow):
     def second_method(self, first_output):
         return f"Second method received: {first_output}"
 
+
 flow = OutputExampleFlow()
 final_output = flow.kickoff()
 
 print("---- Final Output ----")
 print(final_output)
-```
+````
 
-```text
+``` text Output
 ---- Final Output ----
 Second method received: Output from first_method
-```
+````
 
 </CodeGroup>
 
@@ -158,7 +156,7 @@ Here's an example of how to update and access the state:
 
 <CodeGroup>
 
-```python
+```python Code
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
@@ -186,7 +184,7 @@ print("Final State:")
 print(flow.state)
 ```
 
-```text
+```text Output
 Final Output: Hello from first_method - updated by second_method
 Final State:
 counter=2 message='Hello from first_method - updated by second_method'
@@ -210,10 +208,10 @@ allowing developers to choose the approach that best fits their application's ne
 In unstructured state management, all state is stored in the `state` attribute of the `Flow` class.
 This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
 
-```python
+```python Code
 from crewai.flow.flow import Flow, listen, start
 
-class UnstructuredExampleFlow(Flow):
+class UntructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
@@ -232,7 +230,8 @@ class UnstructuredExampleFlow(Flow):
 
         print(f"State after third_method: {self.state}")
 
-flow = UnstructuredExampleFlow()
+
+flow = UntructuredExampleFlow()
 flow.kickoff()
 ```
 
@@ -246,14 +245,16 @@ flow.kickoff()
 Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow.
 By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
 
-```python
+```python Code
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
+
 class ExampleState(BaseModel):
     counter: int = 0
     message: str = ""
 
+
 class StructuredExampleFlow(Flow[ExampleState]):
 
     @start()
@@ -272,6 +273,7 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
         print(f"State after third_method: {self.state}")
 
+
 flow = StructuredExampleFlow()
 flow.kickoff()
 ```
@@ -305,7 +307,7 @@ The `or_` function in Flows allows you to listen to multiple methods and trigger
 
 <CodeGroup>
 
-```python
+```python Code
 from crewai.flow.flow import Flow, listen, or_, start
 
 class OrExampleFlow(Flow):
@@ -322,11 +324,13 @@ class OrExampleFlow(Flow):
     def logger(self, result):
         print(f"Logger: {result}")
 
+
+
 flow = OrExampleFlow()
 flow.kickoff()
 ```
 
-```text
+```text Output
 Logger: Hello from the start method
 Logger: Hello from the second method
 ```
@@ -342,7 +346,7 @@ The `and_` function in Flows allows you to listen to multiple methods and trigge
 
 <CodeGroup>
 
-```python
+```python Code
 from crewai.flow.flow import Flow, and_, listen, start
 
 class AndExampleFlow(Flow):
@@ -364,7 +368,7 @@ flow = AndExampleFlow()
 flow.kickoff()
 ```
 
-```text
+```text Output
 ---- Logger ----
 {'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
 ```
@@ -381,7 +385,7 @@ You can specify different routes based on the output of the method, allowing you
 
 <CodeGroup>
 
-```python
+```python Code
 import random
 from crewai.flow.flow import Flow, listen, router, start
 from pydantic import BaseModel
@@ -412,11 +416,12 @@ class RouterFlow(Flow[ExampleState]):
     def fourth_method(self):
         print("Fourth method running")
 
+
 flow = RouterFlow()
 flow.kickoff()
 ```
 
-```text
+```text Output
 Starting the structured flow
 Third method running
 Fourth method running
@@ -479,7 +484,7 @@ The `main.py` file is where you create your flow and connect the crews together.
 
 Here's an example of how you can connect the `poem_crew` in the `main.py` file:
 
-```python
+```python Code
 #!/usr/bin/env python
 from random import randint
 
@@ -555,42 +560,6 @@ uv run kickoff
 
 The flow will execute, and you should see the output in the console.
 
-
-### Adding Additional Crews Using the CLI
-
-Once you have created your initial flow, you can easily add additional crews to your project using the CLI. This allows you to expand your flow's capabilities by integrating new crews without starting from scratch.
-
-To add a new crew to your existing flow, use the following command:
-
-```bash
-crewai flow add-crew <crew_name>
-```
-
-This command will create a new directory for your crew within the `crews` folder of your flow project. It will include the necessary configuration files and a crew definition file, similar to the initial setup.
-
-#### Folder Structure
-
-After adding a new crew, your folder structure will look like this:
-
-| Directory/File         | Description                                                        |
-| :--------------------- | :----------------------------------------------------------------- |
-| `name_of_flow/`        | Root directory for the flow.                                       |
-| ├── `crews/`           | Contains directories for specific crews.                           |
-| │   ├── `poem_crew/`   | Directory for the "poem_crew" with its configurations and scripts. |
-| │   │   ├── `config/`  | Configuration files directory for the "poem_crew".                 |
-| │   │   │  ├── `agents.yaml` | YAML file defining the agents for "poem_crew".                     |
-| │   │   │  └── `tasks.yaml`  | YAML file defining the tasks for "poem_crew".                      |
-| │   │   └── `poem_crew.py`    | Script for "poem_crew" functionality.                              |
-| └── `name_of_crew/`       | Directory for the new crew.                                          |
-|     ├── `config/`         | Configuration files directory for the new crew.                     |
-|     │   ├── `agents.yaml` | YAML file defining the agents for the new crew.                     |
-|     │   └── `tasks.yaml`  | YAML file defining the tasks for the new crew.                      |
-|     └── `name_of_crew.py` | Script for the new crew functionality.                              |
-
-You can then customize the `agents.yaml` and `tasks.yaml` files to define the agents and tasks for your new crew. The `name_of_crew.py` file will contain the crew's logic, which you can modify to suit your needs.
-
-By using the CLI to add additional crews, you can efficiently build complex AI workflows that leverage multiple crews working together.
-
 ## Plot Flows
 
 Visualizing your AI workflows can provide valuable insights into the structure and execution paths of your flows. CrewAI offers a powerful visualization tool that allows you to generate interactive plots of your flows, making it easier to understand and optimize your AI workflows.
@@ -607,7 +576,7 @@ CrewAI provides two convenient methods to generate plots of your flows:
 
 If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
 
-```python
+```python Code
 # Assuming you have a flow instance
 flow.plot("my_flow_plot")
 ```
@@ -630,114 +599,13 @@ The generated plot will display nodes representing the tasks in your flow, with
 
 By visualizing your flows, you can gain a clearer understanding of the workflow's structure, making it easier to debug, optimize, and communicate your AI processes to others.
 
+### Conclusion
 
-## Advanced
+Plotting your flows is a powerful feature of CrewAI that enhances your ability to design and manage complex AI workflows. Whether you choose to use the `plot()` method or the command line, generating plots will provide you with a visual representation of your workflows, aiding in both development and presentation.
-
-In this section, we explore more complex use cases of CrewAI Flows, starting with a self-evaluation loop. This pattern is crucial for developing AI systems that can iteratively improve their outputs through feedback.
-
-### 1) Self-Evaluation Loop
-
-The self-evaluation loop is a powerful pattern that allows AI workflows to automatically assess and refine their outputs. This example demonstrates how to set up a flow that generates content, evaluates it, and iterates based on feedback until the desired quality is achieved.
-
-#### Overview
-
-The self-evaluation loop involves two main Crews:
-
-1. **ShakespeareanXPostCrew**: Generates a Shakespearean-style post on a given topic.
-2. **XPostReviewCrew**: Evaluates the generated post, providing feedback on its validity and quality.
-
-The process iterates until the post meets the criteria or a maximum retry limit is reached. This approach ensures high-quality outputs through iterative refinement.
-
-#### Importance
-
-This pattern is essential for building robust AI systems that can adapt and improve over time. By automating the evaluation and feedback loop, developers can ensure that their AI workflows produce reliable and high-quality results.
-
-#### Main Code Highlights
-
-Below is the `main.py` file for the self-evaluation loop flow:
-
-```python
-from typing import Optional
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-from self_evaluation_loop_flow.crews.shakespeare_crew.shakespeare_crew import (
-    ShakespeareanXPostCrew,
-)
-from self_evaluation_loop_flow.crews.x_post_review_crew.x_post_review_crew import (
-    XPostReviewCrew,
-)
-
-class ShakespeareXPostFlowState(BaseModel):
-    x_post: str = ""
-    feedback: Optional[str] = None
-    valid: bool = False
-    retry_count: int = 0
-
-class ShakespeareXPostFlow(Flow[ShakespeareXPostFlowState]):
-
-    @start("retry")
-    def generate_shakespeare_x_post(self):
-        print("Generating Shakespearean X post")
-        topic = "Flying cars"
-        result = (
-            ShakespeareanXPostCrew()
-            .crew()
-            .kickoff(inputs={"topic": topic, "feedback": self.state.feedback})
-        )
-        print("X post generated", result.raw)
-        self.state.x_post = result.raw
-
-    @router(generate_shakespeare_x_post)
-    def evaluate_x_post(self):
-        if self.state.retry_count > 3:
-            return "max_retry_exceeded"
-        result = XPostReviewCrew().crew().kickoff(inputs={"x_post": self.state.x_post})
-        self.state.valid = result["valid"]
-        self.state.feedback = result["feedback"]
-        print("valid", self.state.valid)
-        print("feedback", self.state.feedback)
-        self.state.retry_count += 1
-        if self.state.valid:
-            return "complete"
-        return "retry"
-
-    @listen("complete")
-    def save_result(self):
-        print("X post is valid")
-        print("X post:", self.state.x_post)
-        with open("x_post.txt", "w") as file:
-            file.write(self.state.x_post)
-
-    @listen("max_retry_exceeded")
-    def max_retry_exceeded_exit(self):
-        print("Max retry count exceeded")
-        print("X post:", self.state.x_post)
-        print("Feedback:", self.state.feedback)
-
-def kickoff():
-    shakespeare_flow = ShakespeareXPostFlow()
-    shakespeare_flow.kickoff()
-
-def plot():
-    shakespeare_flow = ShakespeareXPostFlow()
-    shakespeare_flow.plot()
-
-if __name__ == "__main__":
-    kickoff()
-```
-
-#### Code Highlights
-
-- **Retry Mechanism**: The flow uses a retry mechanism to regenerate the post if it doesn't meet the criteria, up to a maximum of three retries.
-- **Feedback Loop**: Feedback from the `XPostReviewCrew` is used to refine the post iteratively.
-- **State Management**: The flow maintains state using a Pydantic model, ensuring type safety and clarity.
-
-For a complete example and further details, please refer to the [Self Evaluation Loop Flow repository](https://github.com/crewAIInc/crewAI-examples/tree/main/self_evaluation_loop_flow).
-
 
 ## Next Steps
 
-If you're interested in exploring additional examples of flows, we have a variety of recommendations in our examples repository. Here are five specific flow examples, each showcasing unique use cases to help you match your current problem type to a specific example:
+If you're interested in exploring additional examples of flows, we have a variety of recommendations in our examples repository. Here are four specific flow examples, each showcasing unique use cases to help you match your current problem type to a specific example:
 
 1. **Email Auto Responder Flow**: This example demonstrates an infinite loop where a background job continually runs to automate email responses. It's a great use case for tasks that need to be performed repeatedly without manual intervention. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow)
 
@@ -747,8 +615,6 @@ If you're interested in exploring additional examples of flows, we have a variet
 
 4. **Meeting Assistant Flow**: This flow demonstrates how to broadcast one event to trigger multiple follow-up actions. For instance, after a meeting is completed, the flow can update a Trello board, send a Slack message, and save the results. It's a great example of handling multiple outcomes from a single event, making it ideal for comprehensive task management and notification systems. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow)
 
-5. **Self Evaluation Loop Flow**: This flow demonstrates a self-evaluation loop where AI workflows automatically assess and refine their outputs through feedback. It involves generating content, evaluating it, and iterating until the desired quality is achieved. This pattern is crucial for developing robust AI systems that can adapt and improve over time. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/self_evaluation_loop_flow)
-
 By exploring these examples, you can gain insights into how to leverage CrewAI Flows for various use cases, from automating repetitive tasks to managing complex, multi-step processes with dynamic decision-making and human feedback.
 
 Also, check out our YouTube video on how to use flows in CrewAI below!
@@ -762,4 +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

@@ -6,28 +6,54 @@ icon: book
 
 # Using Knowledge in CrewAI
 
-## Introduction
+## What is Knowledge?
 
-The Knowledge class in CrewAI provides a powerful way to manage and query knowledge sources for your AI agents. This guide will show you how to implement knowledge management in your CrewAI projects.
+Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks. 
-Additionally, we have specific tools for generate knowledge sources for strings, text files, PDF's, and Spreadsheets. You can expand on any source type by extending the `KnowledgeSource` class.
+Think of it as giving your agents a reference library they can consult while working. 
 
-## Basic Implementation
+<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>
 
-Here's a simple example of how to use the Knowledge class:
+## Supported Knowledge Sources
 
-```python
+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, metadata={"preference": "personal"}
+    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
+
+# Create an agent with the knowledge store
 agent = Agent(
     role="About User",
     goal="You know everything about the user.",
@@ -47,29 +73,240 @@ crew = Crew(
     tasks=[task],
     verbose=True,
     process=Process.sequential,
-    knowledge={"sources": [string_source], "metadata": {"preference": "personal"}}, # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+    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
+```python Code
 ...
 string_source = StringKnowledgeSource(
     content="Users name is John. He is 30 years old and lives in San Francisco.",
-    metadata={"preference": "personal"}
 )
 crew = Crew(
     ...
-    knowledge={
+    knowledge_sources=[string_source],
-        "sources": [string_source],
+    embedder={
-        "metadata": {"preference": "personal"},
+        "provider": "openai",
-        "embedder_config": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
+        "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/documentation), 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 dotenv import load_dotenv
-from langchain.agents import Tool
+from crewai import Agent, Task, Crew
-from langchain.utilities import GoogleSerperAPIWrapper
+from crewai.tools import BaseTool
+from pydantic import Field
+from langchain_community.utilities import GoogleSerperAPIWrapper
 
-# Setup API keys
+# Set up your SERPER_API_KEY key in an .env file, eg:
-os.environ["SERPER_API_KEY"] = "Your Key"
+# SERPER_API_KEY=<your api key>
+load_dotenv()
 
 search = GoogleSerperAPIWrapper()
 
-# Create and assign the search tool to an agent
+class SearchTool(BaseTool):
-serper_tool = Tool(
+    name: str = "Search"
-  name="Intermediate Answer",
+    description: str = "Useful for search-based queries. Use this to find current information about markets, companies, and trends."
-  func=search.run,
+    search: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper)
-  description="Useful for search-based queries",
-)
 
-agent = Agent(
+    def _run(self, query: str) -> str:
-  role='Research Analyst',
+        """Execute the search query and return results"""
-  goal='Provide up-to-date market analysis',
+        try:
-  backstory='An expert analyst with a keen eye for market trends.',
+            return self.search.run(query)
-  tools=[serper_tool]
+        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. 
+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, 
+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.
+and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/concepts/llms.mdx

@@ -1,205 +1,323 @@
 ---
-title: LLMs
+title: 'LLMs'
-description: Learn how to configure and optimize LLMs for your CrewAI projects.
+description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
-icon: microchip-ai
+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
+<CardGroup cols={2}>
-- **Agent**: A CrewAI entity that uses an LLM to perform tasks
+  <Card title="LLM Basics" icon="brain">
-- **Provider**: A service that offers LLM capabilities (e.g., OpenAI, Anthropic, Ollama, [more providers](https://docs.litellm.ai/docs/providers))
+    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:
-
-### 1. Default Configuration
-
-By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables if no LLM is specified:
-- `OPENAI_MODEL_NAME` (defaults to "gpt-4o-mini" if not set)
-- `OPENAI_API_BASE`
-- `OPENAI_API_KEY`
-
-### 2. Updating YAML files
-
-You can update the `agents.yml` file to refer to the LLM you want to use:
-
-```yaml Code
-researcher:
-    role: Research Specialist
-    goal: Conduct comprehensive research and analysis to gather relevant information,
-        synthesize findings, and produce well-documented insights.
-    backstory: A dedicated research professional with years of experience in academic
-        investigation, literature review, and data analysis, known for thorough and
-        methodical approaches to complex research questions.
-    verbose: true
-    llm: openai/gpt-4o
-    # llm: azure/gpt-4o-mini
-    # llm: gemini/gemini-pro
-    # llm: anthropic/claude-3-5-sonnet-20240620
-    # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
-    # llm: mistral/mistral-large-latest
-    # llm: ollama/llama3:70b
-    # llm: groq/llama-3.2-90b-vision-preview
-    # llm: watsonx/meta-llama/llama-3-1-70b-instruct
-    # llm: nvidia_nim/meta/llama3-70b-instruct
-    # llm: sambanova/Meta-Llama-3.1-8B-Instruct
-    # ...
-```
-
-Keep in mind that you will need to set certain ENV vars depending on the model you are
-using to account for the credentials or set a custom LLM object like described below.
-Here are some of the required ENV vars for some of the LLM integrations:
-
-<AccordionGroup>
-    <Accordion title="OpenAI">
-    ```python Code
-    OPENAI_API_KEY=<your-api-key>
-    OPENAI_API_BASE=<optional-custom-base-url>
-    OPENAI_MODEL_NAME=<openai-model-name>
-    OPENAI_ORGANIZATION=<your-org-id>       # OPTIONAL
-    OPENAI_API_BASE=<openaiai-api-base>     # OPTIONAL
-    ```
-    </Accordion>
-
-    <Accordion title="Anthropic">
-    ```python Code
-    ANTHROPIC_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="Google">
-    ```python Code
-    GEMINI_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="Azure">
-    ```python Code
-    AZURE_API_KEY=<your-api-key>                    # "my-azure-api-key"
-    AZURE_API_BASE=<your-resource-url>              # "https://example-endpoint.openai.azure.com"
-    AZURE_API_VERSION=<api-version>                 # "2023-05-15"
-    AZURE_AD_TOKEN=<your-azure-ad-token>            # Optional
-    AZURE_API_TYPE=<your-azure-api-type>            # Optional
-    ```
-    </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>
-    ```
-    </Accordion>
-
-    <Accordion title="Mistral">
-    ```python Code
-    MISTRAL_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="Groq">
-    ```python Code
-    GROQ_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="IBM watsonx.ai">
-    ```python Code
-    WATSONX_URL=<your-url>                          # (required) Base URL of your WatsonX instance
-    WATSONX_APIKEY=<your-apikey>                    # (required) IBM cloud API key
-    WATSONX_TOKEN=<your-token>                      # (required) IAM auth token (alternative to APIKEY)
-    WATSONX_PROJECT_ID=<your-project-id>            # (optional) Project ID of your WatsonX instance
-    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>     # (optional) ID of deployment space for deployed models
-    ```
-    </Accordion>
-</AccordionGroup>
-
-### 3. Custom LLM Objects
-
-Pass a custom LLM implementation or object from another library.
-
-See below for examples.
 
 <Tabs>
-    <Tab title="String Identifier">
+  <Tab title="OpenAI">
-    ```python Code
+    | Model | Context Window | Best For |
-    agent = Agent(llm="gpt-4o", ...)
+    |-------|---------------|-----------|
-    ```
+    | GPT-4 | 8,192 tokens | High-accuracy tasks, complex reasoning |
-    </Tab>
+    | GPT-4 Turbo | 128,000 tokens | Long-form content, document analysis |
+    | GPT-4o & GPT-4o-mini | 128,000 tokens | Cost-effective large context processing |
 
-    <Tab title="LLM Instance">
+    <Note>
-    ```python Code
+      1 token ≈ 4 characters in English. For example, 8,192 tokens ≈ 32,768 characters or about 6,000 words.
-    from crewai import LLM
+    </Note>
+  </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 |
 
-    llm = LLM(model="gpt-4", temperature=0.7)
+    <Tip>
-    agent = Agent(llm=llm, ...)
+      Groq is known for its fast inference speeds, making it suitable for real-time applications.
-    ```
+    </Tip>
-    </Tab>
+  </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 |
+
+    <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>
 
-## Connecting to OpenAI-Compatible LLMs
+## Setting Up Your LLM
 
-You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
+There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
 
 <Tabs>
-    <Tab title="Using Environment Variables">
+  <Tab title="1. Environment Variables">
-    ```python Code
+    The simplest way to get started. Set these variables in your environment:
-    import os
 
-    os.environ["OPENAI_API_KEY"] = "your-api-key"
+    ```bash
-    os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
+    # Required: Your API key for authentication
+    OPENAI_API_KEY=<your-api-key>
+
+    # Optional: Default model selection
+    OPENAI_MODEL_NAME=gpt-4o-mini  # Default if not set
+
+    # Optional: Organization ID (if applicable)
+    OPENAI_ORGANIZATION_ID=<your-org-id>
     ```
-    </Tab>
+
-    <Tab title="Using LLM Class Attributes">
+    <Warning>
-    ```python Code
+      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:
+
+    ```yaml
+    researcher:
+        # Agent Definition
+        role: Research Specialist
+        goal: Conduct comprehensive research and analysis
+        backstory: A dedicated research professional with years of experience
+        verbose: true
+
+        # 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
+
+        # Azure Models - For enterprise deployments
+        # llm: azure/gpt-4o-mini
+        # llm: azure/gpt-4
+        # llm: azure/gpt-35-turbo
+
+        # 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
+
+        # Google Models - Good for general tasks
+        # llm: gemini/gemini-pro
+        # llm: gemini/gemini-1.5-pro-latest
+        # llm: gemini/gemini-1.0-pro-latest
+
+        # 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
+
+        # Mistral Models - Open source alternative
+        # llm: mistral/mistral-large-latest
+        # llm: mistral/mistral-medium-latest
+        # llm: mistral/mistral-small-latest
+
+        # 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
+
+        # 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
+
+        # 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
+
+        # 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
+
+        # 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
+
+        # Nvidia NIM Models - GPU-optimized
+        # llm: nvidia_nim/meta/llama3-70b-instruct
+        # llm: nvidia_nim/mistral/mixtral-8x7b
+        # llm: nvidia_nim/google/gemma-7b
+
+        # SambaNova Models - Enterprise AI
+        # llm: sambanova/Meta-Llama-3.1-8B-Instruct
+        # llm: sambanova/BioMistral-7B
+        # llm: sambanova/Falcon-180B
+    ```
+
+    <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:
+
+    ```python
     from crewai import LLM
 
+    # Basic configuration
+    llm = LLM(model="gpt-4")
+
+    # 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
+    )
+    ```
+
+    <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>
+
+## Advanced Features and Optimization
+
+Learn how to get the most out of your LLM configuration:
+
+<AccordionGroup>
+  <Accordion title="Context Window Management">
+    CrewAI includes smart context management features:
+
+    ```python
+    from crewai import LLM
+
+    # CrewAI automatically handles:
+    # 1. Token counting and tracking
+    # 2. Content summarization when needed
+    # 3. Task splitting for large contexts
+
     llm = LLM(
-        model="custom-model-name",
+        model="gpt-4",
-        api_key="your-api-key",
+        max_tokens=4000,  # Limit response length
-        base_url="https://api.your-provider.com/v1"
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Tab>
-</Tabs>
 
-## LLM Configuration Options
+    <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>
 
-When configuring an LLM for your agent, you have access to a wide range of parameters:
+  <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>
 
-| Parameter         | Type            | Description                                                                                      |
+      <Step title="Best Practices">
-|:------------------|:---------------:|:-------------------------------------------------------------------------------------------------|
+        1. Monitor token usage
-| **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. |
+        2. Implement rate limiting
-| **timeout**          | `float, int`      | Maximum time (in seconds) to wait for a response.                                                |
+        3. Use caching when possible
-| **temperature**      | `float`           | Controls randomness in output (0.0 to 1.0).                                                      |
+        4. Set appropriate max_tokens limits
-| **top_p**            | `float`           | Controls diversity of output (0.0 to 1.0).                                                       |
+      </Step>
-| **n**                | `int`             | Number of completions to generate.                                                               |
+    </Steps>
-| **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.                                                                 |
 
+    <Info>
+      Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
+    </Info>
+  </Accordion>
+</AccordionGroup>
 
-These are examples of how to configure LLMs for your agent.
+## Provider Configuration Examples
 
 <AccordionGroup>
-    <Accordion title="OpenAI">
+  <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
 
@@ -211,193 +329,306 @@ These are examples of how to configure LLMs for your agent.
         frequency_penalty=0.1,
         presence_penalty=0.1,
         stop=["END"],
-        seed=42,
+        seed=42
-        base_url="https://api.openai.com/v1",
-        api_key="your-api-key-here"
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Accordion>
+  </Accordion>
-
-    <Accordion title="Cerebras">
 
+  <Accordion title="Anthropic">
     ```python Code
-    from crewai import LLM
+    ANTHROPIC_API_KEY=sk-ant-...
+    ```
 
+    Example usage:
+    ```python Code
     llm = LLM(
-        model="cerebras/llama-3.1-70b",
+        model="anthropic/claude-3-sonnet-20240229-v1:0",
-        api_key="your-api-key-here"
+        temperature=0.7
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Accordion>
-
-    <Accordion title="Ollama (Local LLMs)">
-
-    CrewAI supports using Ollama for running open-source models locally:
-
-    1. Install Ollama: [ollama.ai](https://ollama.ai/)
-    2. Run a model: `ollama run llama2`
-    3. Configure agent:
-
-    ```python Code
-    from crewai import LLM
-
-    agent = Agent(
-        llm=LLM(
-            model="ollama/llama3.1",
-            base_url="http://localhost:11434"
-        ),
-        ...
     )
     ```
-    </Accordion>
+  </Accordion>
-
-    <Accordion title="Groq">
 
+  <Accordion title="Google">
     ```python Code
-    from crewai import LLM
+    GEMINI_API_KEY=<your-api-key>
+    ```
 
+    Example usage:
+    ```python Code
     llm = LLM(
-        model="groq/llama3-8b-8192",
+        model="gemini/gemini-pro",
-        api_key="your-api-key-here"
+        temperature=0.7
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Accordion>
+  </Accordion>
-
-    <Accordion title="Anthropic">
 
+  <Accordion title="Azure">
     ```python Code
-    from crewai import LLM
+    # 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="anthropic/claude-3-5-sonnet-20241022",
+        model="azure/gpt-4",
-        api_key="your-api-key-here"
+        api_version="2023-05-15"
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Accordion>
+  </Accordion>
 
-    <Accordion title="Fireworks AI">
+  <Accordion title="AWS Bedrock">
     ```python Code
-    from crewai import LLM
+    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="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
+        model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
-        api_key="your-api-key-here"
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Accordion>
+  </Accordion>
-
-    <Accordion title="Gemini">
 
+  <Accordion title="Mistral">
     ```python Code
-    from crewai import LLM
+    MISTRAL_API_KEY=<your-api-key>
+    ```
 
+    Example usage:
+    ```python Code
     llm = LLM(
-        model="gemini/gemini-1.5-pro-002",
+        model="mistral/mistral-large-latest",
-        api_key="your-api-key-here"
+        temperature=0.7
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Accordion>
+  </Accordion>
-
-    <Accordion title="Perplexity AI (pplx-api)">
 
+  <Accordion title="Groq">
     ```python Code
-    from crewai import LLM
+    GROQ_API_KEY=<your-api-key>
+    ```
 
+    Example usage:
+    ```python Code
     llm = LLM(
-        model="llama-3.1-sonar-large-128k-online",
+        model="groq/llama-3.2-90b-text-preview",
-        base_url="https://api.perplexity.ai/",
+        temperature=0.7
-        api_key="your-api-key-here"
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Accordion>
+  </Accordion>
-
-    <Accordion title="IBM watsonx.ai">
-    You can use IBM Watson by seeting the following ENV vars:
 
+  <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>
     ```
 
-    You can then define your agents llms by updating the `agents.yml`
+    Example usage:
-
-    ```yaml Code
-    researcher:
-        role: Research Specialist
-        goal: Conduct comprehensive research and analysis to gather relevant information,
-            synthesize findings, and produce well-documented insights.
-        backstory: A dedicated research professional with years of experience in academic
-            investigation, literature review, and data analysis, known for thorough and
-            methodical approaches to complex research questions.
-        verbose: true
-        llm: watsonx/meta-llama/llama-3-1-70b-instruct
-    ```
-
-    You can also set up agents more dynamically as a base level LLM instance, like bellow:
-
     ```python Code
-    from crewai import LLM
-
     llm = LLM(
-        model="watsonx/ibm/granite-13b-chat-v2",
+        model="watsonx/meta-llama/llama-3-1-70b-instruct",
-        base_url="https://api.watsonx.ai/v1",
+        base_url="https://api.watsonx.ai/v1"
-        api_key="your-api-key-here"
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Accordion>
+  </Accordion>
 
-    <Accordion title="Hugging Face">
+  <Accordion title="Ollama (Local LLMs)">
+    1. Install Ollama: [ollama.ai](https://ollama.ai/)
+    2. Run a model: `ollama run llama2`
+    3. Configure:
 
     ```python Code
-    from crewai import LLM
+    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",
-        api_key="your-api-key-here",
         base_url="your_api_endpoint"
     )
-    agent = Agent(llm=llm, ...)
     ```
-    </Accordion>
+  </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>
 
-## Changing the Base API URL
+## Common Issues and Solutions
 
-You can change the base API URL for any LLM provider by setting the `base_url` parameter:
+<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>
 
-```python Code
+## Getting Help
-from crewai import LLM
 
-llm = LLM(
+If you need assistance, these resources are available:
-    model="custom-model-name",
-    base_url="https://api.your-provider.com/v1",
-    api_key="your-api-key"
-)
-agent = Agent(llm=llm, ...)
-```
 
-This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
+<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>
 
-## Best Practices
+<Note>
-
+  Best Practices for API Key Security:
-1. **Choose the right model**: Balance capability and cost.
+  - Use environment variables or secure vaults
-2. **Optimize prompts**: Clear, concise instructions improve output.
+  - Never commit keys to version control
-3. **Manage tokens**: Monitor and limit token usage for efficiency.
+  - Rotate keys regularly
-4. **Use appropriate temperature**: Lower for factual tasks, higher for creative ones.
+  - Use separate keys for development and production
-5. **Implement error handling**: Gracefully manage API errors and rate limits.
+  - Monitor key usage for unusual patterns
-
+</Note>
-## Troubleshooting
-
-- **API Errors**: Check your API key, network connection, and rate limits.
-- **Unexpected Outputs**: Refine your prompts and adjust temperature or top_p.
-- **Performance Issues**: Consider using a more powerful model or optimizing your queries.
-- **Timeout Errors**: Increase the `timeout` parameter or optimize your input.

docs/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.                  |
+| **Name** _(optional)_           | `name`           | `Optional[str]`               | A name identifier for the task.                                                                                      |
-| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False.    |
+| **Agent** _(optional)_          | `agent`           | `Optional[BaseAgent]`         | The agent responsible for executing the task.                                                                        |
-| **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Specifies tasks whose outputs are used as context for this task.                                                     |
+| **Tools** _(optional)_          | `tools`           | `List[BaseTool]`             | The tools/resources the agent is limited to use 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. |
+| **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Other tasks whose outputs will be used as context for this task.                                                     |
-| **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
+| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | Whether the task should be executed asynchronously. Defaults to False.                                               |
-| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
+| **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
-| **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 File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
-| **Output** _(optional)_          | `output`          | `Optional[TaskOutput]`        | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details.                  |
+| **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
-| **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | A callable that is executed with the task's output upon completion.                                                  |
+| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
-| **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.|
+| **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
-| **Converter Class** _(optional)_ | `converter_cls`   | `Optional[Type[Converter]]`   | A converter class used to export structured output. Defaults to None.                                                |
 
-## 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(
+research_task = Task(
-    description='Find and summarize the latest and most relevant news on AI',
+    description="""
-    agent=sales_agent,
+        Conduct a thorough research about AI Agents.
-    expected_output='A bullet list summary of the top 5 most important AI news',
+        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',
+    description="Research the latest developments in AI",
-    expected_output='A bullet list summary of the top 5 most important AI news',
+    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',
+    description="Research the latest developments in AI Ops",
-    expected_output='A bullet list summary of the top 5 most important AI Ops news',
+    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/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/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
+            ```python Code
-        llm = LLM(
+            llm = LLM(
-            model="custom-model-name",
+                model="custom-model-name",
-            api_key="your-api-key",
+                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. 
+<Note>
-CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. 
+  **Python Version Requirements**
-Let's get started! 🚀
+  
+  CrewAI requires `Python >=3.10 and <=3.13`. 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>
+# Installing CrewAI
-    Make sure you have `Python >=3.10 <=3.13` installed on your system before you proceed.
+
-</Tip>
+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:
+        Install CrewAI with all recommended tools using either method:
-    <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.
         ```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
+    <Step title="Upgrade CrewAI (Existing Installations Only)">
-    <CodeGroup>
+        If you have an older version of CrewAI installed, you can upgrade it:
-    ```shell Terminal
+        ```shell Terminal
-    pip freeze | grep crewai
+        pip install --upgrade crewai crewai-tools
-    ```
+        ```
-    </CodeGroup>
+
-    You should see the version number of `crewai` and `crewai-tools`.
+        <Warning>
-    <CodeGroup>
+            If you see a Poetry-related warning, you'll need to migrate to our new dependency manager:
-    ```markdown Version
+            ```shell Terminal
-    crewai==X.X.X
+            crewai update
-    crewai-tools==X.X.X
+            ```
-    ```
+            This will update your project to use [UV](https://github.com/astral-sh/uv), our new faster dependency manager.
-    </CodeGroup>
+        </Warning>
-    If you see the version number, then the installation was successful! 🎉
+
+        <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. 
+<Info>
-We recommend using the YAML Template scaffolding to get started as it provides a structured approach to defining agents and tasks.
+  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">
+  <Step title="Generate Project Structure">
-  To create a new CrewAI project, run the following CLI (Command Line Interface) command:
+    Run the CrewAI CLI command:
-  <CodeGroup>
+    ```shell Terminal
-      ```shell Terminal
+    crewai create crew <project_name>
-      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                      |
 
-  You can now start developing your crew by editing the files in the `src/my_project` folder. 
+    This creates a new project with the following structure:
-  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, 
+    <Frame>
-  and the `tasks.yaml` file is where you define your tasks.
+    ```
+    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:
+  <Step title="Customize Your Project">
-    - Modify `src/my_project/config/agents.yaml` to define your agents.
+    Your project will contain these essential files:
-    - 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.
+    | File | Purpose |
-    - Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
+    | --- | --- |
-    - Add your environment variables into the `.env` file.
+    | `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!
+<CardGroup cols={2}>
-
+  <Card
-- 👨‍💻 Build your first agent with CrewAI by following the [Quickstart](/quickstart) guide.
+    title="Build Your First Agent"
-- 💬 Join the [Community](https://community.crewai.com) to get help and share your feedback.
+    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">
+Think of it as assembling your dream team - each member (agent) brings unique skills and expertise, collaborating seamlessly to achieve your objectives.
-  <img src="crewAI-mindmap.png" alt="CrewAI Mindmap" />
-</Frame>  
 
-## Why CrewAI?
+## How CrewAI Works
-- 🤼‍♀️ **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.
 
-# 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"
+    title="Install CrewAI"
-  color="#F3A78B"
+    icon="wrench"
-  href="quickstart"
+    href="/installation"
-  icon="terminal"
-    iconType="solid"
   >
-    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"
+    Connect with other developers, get help, and share your CrewAI experiences.
-    >
+  </Card>
-      Join the CrewAI community and get help with your project!
+</CardGroup>
-    </Card>
-</CardGroup>
-
-## Next Step
-
-- [Install CrewAI](/installation) to get started with your first agent.
-

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>
-  <Step title="Modify your `crew.py` file">  
+  <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](http://app.crewai.com/), 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>

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

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.80.0"
+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"
@@ -9,14 +9,13 @@ authors = [
 ]
 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>=0.14.0",
+    "crewai-tools>=0.17.0",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
@@ -29,6 +28,8 @@ dependencies = [
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
     "chromadb>=0.5.18",
+    "pdfplumber>=0.11.4",
+    "openpyxl>=3.1.5",
 ]
 
 [project.urls]

src/crewai/__init__.py

@@ -5,9 +5,7 @@ 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(
@@ -16,14 +14,12 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.80.0"
+__version__ = "0.86.0"
 __all__ = [
     "Agent",
     "Crew",
     "Process",
     "Task",
-    "Pipeline",
-    "Router",
     "LLM",
     "Flow",
     "Knowledge",

src/crewai/agent.py

@@ -1,20 +1,25 @@
 import os
 import shutil
 import subprocess
-from typing import Any, List, Literal, Optional, Union
+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
+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.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
 
@@ -63,6 +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)
@@ -120,11 +126,23 @@ class Agent(BaseAgent):
         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
-        unnacepted_attributes = [
+        unaccepted_attributes = [
             "AWS_ACCESS_KEY_ID",
             "AWS_SECRET_ACCESS_KEY",
             "AWS_REGION_NAME",
@@ -158,30 +176,16 @@ class Agent(BaseAgent):
             for provider, env_vars in ENV_VARS.items():
                 if provider == set_provider:
                     for env_var in env_vars:
-                        if env_var["key_name"] in unnacepted_attributes:
-                            continue
                         # Check if the environment variable is set
-                        if "key_name" in env_var:
+                        key_name = env_var.get("key_name")
-                            env_value = os.environ.get(env_var["key_name"])
+                        if key_name and key_name not in unaccepted_attributes:
+                            env_value = os.environ.get(key_name)
                             if env_value:
-                                # Map key names containing "API_KEY" to "api_key"
+                                key_name = key_name.lower()
-                                key_name = (
+                                for pattern in LITELLM_PARAMS:
-                                    "api_key"
+                                    if pattern in key_name:
-                                    if "API_KEY" in env_var["key_name"]
+                                        key_name = pattern
-                                    else env_var["key_name"]
+                                        break
-                                )
-                                # Map key names containing "API_BASE" to "api_base"
-                                key_name = (
-                                    "api_base"
-                                    if "API_BASE" in env_var["key_name"]
-                                    else key_name
-                                )
-                                # Map key names containing "API_VERSION" to "api_version"
-                                key_name = (
-                                    "api_version"
-                                    if "API_VERSION" in env_var["key_name"]
-                                    else key_name
-                                )
                                 llm_params[key_name] = env_value
                         # Check for default values if the environment variable is not set
                         elif env_var.get("default", False):
@@ -235,9 +239,24 @@ 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[BaseTool]] = None,
     ) -> str:
@@ -256,6 +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
@@ -273,17 +308,21 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        # Integrate the knowledge base
+        if self._knowledge:
-        if self.crew and self.crew.knowledge:
+            agent_knowledge_snippets = self._knowledge.query([task.prompt()])
-            knowledge_snippets = self.crew.knowledge.query([task.prompt()])
+            if agent_knowledge_snippets:
-            valid_snippets = [
+                agent_knowledge_context = extract_knowledge_context(
-                result["context"] 
+                    agent_knowledge_snippets
-                for result in knowledge_snippets 
+                )
-                if result and result.get("context")
+                if agent_knowledge_context:
-            ]
+                    task_prompt += agent_knowledge_context
-            if valid_snippets:
+
-                formatted_knowledge = "\n".join(valid_snippets)
+        if self.crew:
-                task_prompt += f"\n\nAdditional Information:\n{formatted_knowledge}"
+            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)
@@ -399,7 +438,7 @@ class Agent(BaseAgent):
 
             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:

src/crewai/agents/agent_builder/base_agent.py

@@ -19,6 +19,7 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.tools import BaseTool
+from crewai.tools.base_tool import Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 
@@ -106,7 +107,7 @@ class BaseAgent(ABC, BaseModel):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: Optional[List[BaseTool]] = Field(
+    tools: Optional[List[Any]] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: Optional[int] = Field(
@@ -135,6 +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

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/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)
+            formatted_answer = self._handle_human_feedback(formatted_answer)
-            if self.crew and self.crew._train:
-                self._handle_crew_training_output(formatted_answer, human_feedback)
 
-            # Making sure we only ask for it once, so disabling for the next thought loop
-            self.ask_for_human_input = False
-            self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
-            formatted_answer = self._invoke_loop()
-
-            if self.crew and self.crew._train:
-                self._handle_crew_training_output(formatted_answer)
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
@@ -140,9 +140,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     formatted_answer = self._format_answer(answer)
 
                     if isinstance(formatted_answer, AgentAction):
-                        action_result = self._use_tool(formatted_answer)
+                        tool_result = self._execute_tool_and_check_finality(
-                        formatted_answer.text += f"\nObservation: {action_result}"
+                            formatted_answer
-                        formatted_answer.result = action_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:
@@ -239,7 +247,7 @@ 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,
@@ -255,19 +263,25 @@ 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 = []
@@ -302,16 +316,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _handle_context_length(self) -> None:
         if self.respect_context_window:
-            self._logger.log(
+            self._printer.print(
-                "debug",
+                content="Context length exceeded. Summarizing content to fit the model context window.",
-                "Context length exceeded. Summarizing content to fit the model context window.",
                 color="yellow",
             )
             self._summarize_messages()
         else:
-            self._logger.log(
+            self._printer.print(
-                "debug",
+                content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
-                "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
                 color="red",
             )
             raise SystemExit(
@@ -333,20 +345,18 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
                 if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration]["improved_output"] = (
+                    training_data[agent_id][train_iteration][
-                        result.output
+                        "improved_output"
-                    )
+                    ] = result.output
                     training_handler.save(training_data)
                 else:
-                    self._logger.log(
+                    self._printer.print(
-                        "error",
+                        content="Invalid train iteration type or agent_id not in training data.",
-                        "Invalid train iteration type or agent_id not in training data.",
                         color="red",
                     )
             else:
-                self._logger.log(
+                self._printer.print(
-                    "error",
+                    content="Crew is None or does not have _train_iteration attribute.",
-                    "Crew is None or does not have _train_iteration attribute.",
                     color="red",
                 )
 
@@ -364,15 +374,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         train_iteration, agent_id, training_data
                     )
                 else:
-                    self._logger.log(
+                    self._printer.print(
-                        "error",
+                        content="Invalid train iteration type. Expected int.",
-                        "Invalid train iteration type. Expected int.",
                         color="red",
                     )
             else:
-                self._logger.log(
+                self._printer.print(
-                    "error",
+                    content="Crew is None or does not have _train_iteration attribute.",
-                    "Crew is None or does not have _train_iteration attribute.",
                     color="red",
                 )
 
@@ -388,3 +396,82 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     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)
+            print("Human feedback: ", human_feedback)
+
+            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/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()
 
@@ -63,7 +64,22 @@ class AuthenticationCommand:
                 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

@@ -6,7 +6,6 @@ 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,
 )
@@ -26,27 +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")
 @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, pipeline, or flow."""
+    """Create a new crew, or flow."""
     if type == "crew":
         create_crew(name, provider, skip_provider)
-    elif type == "pipeline":
-        create_pipeline(name)
     elif type == "flow":
         create_flow(name)
     else:
-        click.secho(
+        click.secho("Error: Invalid type. Must be 'crew' or 'flow'.", fg="red")
-            "Error: Invalid type. Must be 'crew', 'pipeline', or 'flow'.", fg="red"
-        )
 
 
 @crewai.command()
@@ -55,7 +51,10 @@ def create(type, name, provider, skip_provider=False):
 )
 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:

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/constants.py

@@ -159,3 +159,6 @@ MODELS = {
 }
 
 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

@@ -39,6 +39,7 @@ def create_folder_structure(name, parent_folder=None):
 
     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)
@@ -52,7 +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"]
@@ -168,7 +176,9 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
     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/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/run_crew.py

@@ -3,7 +3,8 @@ import subprocess
 import click
 from packaging import version
 
-from crewai.cli.utils import get_crewai_version, read_toml
+from crewai.cli.utils import read_toml
+from crewai.cli.version import get_crewai_version
 
 
 def run_crew() -> None:

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

@@ -1,24 +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
+# If you want to run a snippet of code before or after the crew starts, 
-# from {{folder_name}}.tools.custom_tool import MyCustomTool
+# you can use the @before_kickoff and @after_kickoff decorators
-
+# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
-# Check our tools documentations for more information on how to use them
-# from crewai_tools import SerperDevTool
 
 @CrewBase
 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
 		)
 
@@ -29,6 +31,9 @@ class {{crew_name}}():
 			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(
@@ -45,6 +50,9 @@ class {{crew_name}}():
 	@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

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/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.80.0,<1.0.0"
+    "crewai[tools]>=0.86.0,<1.0.0"
 ]
 
 [project.scripts]

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():
+class PoemCrew:
-	"""Poem Crew"""
+    """Poem Crew"""
 
-	agents_config = 'config/agents.yaml'
+    # Learn more about YAML configuration files here:
-	tasks_config = 'config/tasks.yaml'
+    # 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
+    # If you would lik to add tools to your crew, you can learn more about it here:
-	def poem_writer(self) -> Agent:
+    # https://docs.crewai.com/concepts/agents#agent-tools
-		return Agent(
+    @agent
-			config=self.agents_config['poem_writer'],
+    def poem_writer(self) -> Agent:
-		)
+        return Agent(
+            config=self.agents_config["poem_writer"],
+        )
 
-	@task
+    # To learn more about structured task outputs,
-	def write_poem(self) -> Task:
+    # task dependencies, and task callbacks, check out the documentation:
-		return Task(
+    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
-			config=self.tasks_config['write_poem'],
+    @task
-		)
+    def write_poem(self) -> Task:
+        return Task(
+            config=self.tasks_config["write_poem"],
+        )
 
-	@crew
+    @crew
-	def crew(self) -> Crew:
+    def crew(self) -> Crew:
-		"""Creates the Research Crew"""
+        """Creates the Research Crew"""
-		return Crew(
+        # To learn how to add knowledge sources to your crew, check out the documentation:
-			agents=self.agents, # Automatically created by the @agent decorator
+        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
-			tasks=self.tasks, # Automatically created by the @task decorator
+
-			process=Process.sequential,
+        return Crew(
-			verbose=True,
+            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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.80.0,<1.0.0",
+    "crewai[tools]>=0.86.0,<1.0.0",
 ]
 
 [project.scripts]

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/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/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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.80.0,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.86.0,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -1,19 +0,0 @@
-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):
-    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
-        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/config/tasks.yaml

@@ -1,17 +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 the mains topics, each with a full section of information.
-    Formatted as markdown without '```'
-  agent: reporting_analyst

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]>=0.80.0,<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

@@ -1,19 +0,0 @@
-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):
-    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
-        return "this is an example of a tool output, ignore it and move along."

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

@@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.80.0"
+    "crewai[tools]>=0.86.0"
 ]
 

src/crewai/cli/utils.py

@@ -1,4 +1,3 @@
-import importlib.metadata
 import os
 import shutil
 import sys
@@ -9,7 +8,6 @@ import click
 import tomli
 from rich.console import Console
 
-from crewai.cli.authentication.utils import TokenManager
 from crewai.cli.constants import ENV_VARS
 
 if sys.version_info >= (3, 11):
@@ -137,11 +135,6 @@ def _get_nested_value(data: Dict[str, Any], keys: List[str]) -> Any:
     return reduce(dict.__getitem__, keys, data)
 
 
-def get_crewai_version() -> str:
-    """Get the version number of CrewAI running the CLI"""
-    return importlib.metadata.version("crewai")
-
-
 def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
     """Fetch the environment variables from a .env file and return them as a dictionary."""
     try:
@@ -166,14 +159,6 @@ def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
     return {}
 
 
-def get_auth_token() -> str:
-    """Get the authentication token."""
-    access_token = TokenManager().get_token()
-    if not access_token:
-        raise Exception()
-    return access_token
-
-
 def tree_copy(source, destination):
     """Copies the entire directory structure from the source to the destination."""
     for item in os.listdir(source):

src/crewai/cli/version.py

@@ -0,0 +1,6 @@
+import importlib.metadata
+
+def get_crewai_version() -> str:
+    """Get the version number of CrewAI running the CLI"""
+    return importlib.metadata.version("crewai")
+

src/crewai/crew.py

@@ -5,7 +5,7 @@ import uuid
 import warnings
 from concurrent.futures import Future
 from hashlib import md5
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -23,11 +23,12 @@ from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
 from crewai.crews.crew_output import CrewOutput
+from crewai.knowledge.knowledge import Knowledge
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.llm import LLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
-from crewai.knowledge.knowledge import Knowledge
 from crewai.memory.user.user_memory import UserMemory
 from crewai.process import Process
 from crewai.task import Task
@@ -55,8 +56,6 @@ if os.environ.get("AGENTOPS_API_KEY"):
     except ImportError:
         pass
 
-if TYPE_CHECKING:
-    from crewai.pipeline.pipeline import Pipeline
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
@@ -202,10 +201,13 @@ class Crew(BaseModel):
         default=[],
         description="List of execution logs for tasks",
     )
-    knowledge: Optional[Dict[str, Any]] = Field(
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
-        default=None, description="Knowledge for the crew. Add knowledge sources to the knowledge object."
+        default=None,
+        description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.",
+    )
+    _knowledge: Optional[Knowledge] = PrivateAttr(
+        default=None,
     )
-
 
     @field_validator("id", mode="before")
     @classmethod
@@ -282,11 +284,22 @@ class Crew(BaseModel):
 
     @model_validator(mode="after")
     def create_crew_knowledge(self) -> "Crew":
-        if self.knowledge:
+        """Create the knowledge for the crew."""
+        if self.knowledge_sources:
             try:
-                self.knowledge = Knowledge(**self.knowledge) if isinstance(self.knowledge, dict) else self.knowledge
+                if isinstance(self.knowledge_sources, list) and all(
-            except (TypeError, ValueError) as e:
+                    isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
-                raise ValueError(f"Invalid knowledge configuration: {str(e)}")
+                ):
+                    self._knowledge = Knowledge(
+                        sources=self.knowledge_sources,
+                        embedder_config=self.embedder,
+                        collection_name="crew",
+                    )
+
+            except Exception as e:
+                self._logger.log(
+                    "warning", f"Failed to init knowledge: {e}", color="yellow"
+                )
         return self
 
     @model_validator(mode="after")
@@ -942,6 +955,11 @@ class Crew(BaseModel):
         result = self._execute_tasks(self.tasks, start_index, True)
         return result
 
+    def query_knowledge(self, query: List[str]) -> Union[List[Dict[str, Any]], None]:
+        if self._knowledge:
+            return self._knowledge.query(query)
+        return None
+
     def copy(self):
         """Create a deep copy of the Crew."""
 
@@ -1053,17 +1071,5 @@ class Crew(BaseModel):
 
         evaluator.print_crew_evaluation_result()
 
-    def __rshift__(self, other: "Crew") -> "Pipeline":
-        """
-        Implements the >> operator to add another Crew to an existing Pipeline.
-        """
-        from crewai.pipeline.pipeline import Pipeline
-
-        if not isinstance(other, Crew):
-            raise TypeError(
-                f"Unsupported operand type for >>: '{type(self).__name__}' and '{type(other).__name__}'"
-            )
-        return Pipeline(stages=[self, other])
-
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"

src/crewai/knowledge/knowledge.py

@@ -1,12 +1,11 @@
 import os
+from typing import Any, Dict, List, Optional
 
-from typing import List, Optional, Dict, Any
 from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
-from crewai.utilities.logger import Logger
+
-from crewai.utilities.constants import DEFAULT_SCORE_THRESHOLD
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
 
 
@@ -18,28 +17,35 @@ class Knowledge(BaseModel):
         storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
         embedder_config: Optional[Dict[str, Any]] = None
     """
+
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     embedder_config: Optional[Dict[str, Any]] = None
+    collection_name: Optional[str] = None
 
-    def __init__(self, embedder_config: Optional[Dict[str, Any]] = None, **data):
+    def __init__(
+        self,
+        collection_name: str,
+        sources: List[BaseKnowledgeSource],
+        embedder_config: Optional[Dict[str, Any]] = None,
+        storage: Optional[KnowledgeStorage] = None,
+        **data,
+    ):
         super().__init__(**data)
-        self.storage = KnowledgeStorage(embedder_config=embedder_config or None)
+        if storage:
-
+            self.storage = storage
-        try:
+        else:
-            for source in self.sources:
+            self.storage = KnowledgeStorage(
-                source.add()
+                embedder_config=embedder_config, collection_name=collection_name
-        except Exception as e:
-            Logger(verbose=True).log(
-                "warning",
-                f"Failed to init knowledge: {e}",
-                color="yellow",
             )
+        self.sources = sources
+        self.storage.initialize_knowledge_storage()
+        for source in sources:
+            source.storage = self.storage
+            source.add()
 
-    def query(
+    def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
-        self, query: List[str], limit: int = 3, preference: Optional[str] = None
-    ) -> List[Dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
@@ -48,7 +54,10 @@ class Knowledge(BaseModel):
         results = self.storage.search(
             query,
             limit,
-            filter={"preference": preference} if preference else None,
-            score_threshold=DEFAULT_SCORE_THRESHOLD,
         )
         return results
+
+    def _add_sources(self):
+        for source in self.sources:
+            source.storage = self.storage
+            source.add()

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -1,36 +1,71 @@
+from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Union, List
+from typing import Dict, List, Union
 
 from pydantic import Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from typing import Dict, Any
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
+from crewai.utilities.logger import Logger
 
 
-class BaseFileKnowledgeSource(BaseKnowledgeSource):
+class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
     """Base class for knowledge sources that load content from files."""
 
-    file_path: Union[Path, List[Path]] = Field(...)
+    _logger: Logger = Logger(verbose=True)
+    file_path: Union[Path, List[Path], str, List[str]] = Field(
+        ..., description="The path to the file"
+    )
     content: Dict[Path, str] = Field(init=False, default_factory=dict)
     storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    safe_file_paths: List[Path] = Field(default_factory=list)
 
     def model_post_init(self, _):
         """Post-initialization method to load content."""
+        self.safe_file_paths = self._process_file_paths()
+        self.validate_paths()
         self.content = self.load_content()
 
+    @abstractmethod
     def load_content(self) -> Dict[Path, str]:
-        """Load and preprocess file content. Should be overridden by subclasses."""
+        """Load and preprocess file content. Should be overridden by subclasses. Assume that the file path is relative to the project root in the knowledge directory."""
-        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+        pass
 
-        for path in paths:
+    def validate_paths(self):
+        """Validate the paths."""
+        for path in self.safe_file_paths:
             if not path.exists():
+                self._logger.log(
+                    "error",
+                    f"File not found: {path}. Try adding sources to the knowledge directory. If its inside the knowledge directory, use the relative path.",
+                    color="red",
+                )
                 raise FileNotFoundError(f"File not found: {path}")
             if not path.is_file():
-                raise ValueError(f"Path is not a file: {path}")
+                self._logger.log(
-        return {}
+                    "error",
+                    f"Path is not a file: {path}",
+                    color="red",
+                )
 
-    def save_documents(self, metadata: Dict[str, Any]):
+    def _save_documents(self):
         """Save the documents to the storage."""
-        chunk_metadatas = [metadata.copy() for _ in self.chunks]
+        self.storage.save(self.chunks)
-        self.storage.save(self.chunks, chunk_metadatas)
+
+    def convert_to_path(self, path: Union[Path, str]) -> Path:
+        """Convert a path to a Path object."""
+        return Path(KNOWLEDGE_DIRECTORY + "/" + path) if isinstance(path, str) else path
+
+    def _process_file_paths(self) -> List[Path]:
+        """Convert file_path to a list of Path objects."""
+        paths = (
+            [self.file_path]
+            if isinstance(self.file_path, (str, Path))
+            else self.file_path
+        )
+
+        if not isinstance(paths, list):
+            raise ValueError("file_path must be a Path, str, or a list of these types")
+
+        return [self.convert_to_path(path) for path in paths]

src/crewai/knowledge/source/base_knowledge_source.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import List, Dict, Any
+from typing import Any, Dict, List, Optional
 
 import numpy as np
 from pydantic import BaseModel, ConfigDict, Field
@@ -17,7 +17,8 @@ class BaseKnowledgeSource(BaseModel, ABC):
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
-    metadata: Dict[str, Any] = Field(default_factory=dict)
+    metadata: Dict[str, Any] = Field(default_factory=dict)  # Currently unused
+    collection_name: Optional[str] = Field(default=None)
 
     @abstractmethod
     def load_content(self) -> Dict[Any, str]:
@@ -40,9 +41,9 @@ class BaseKnowledgeSource(BaseModel, ABC):
             for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
         ]
 
-    def save_documents(self, metadata: Dict[str, Any]):
+    def _save_documents(self):
         """
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        self.storage.save(self.chunks, metadata)
+        self.storage.save(self.chunks)

src/crewai/knowledge/source/csv_knowledge_source.py

@@ -1,6 +1,6 @@
 import csv
-from typing import Dict, List
 from pathlib import Path
+from typing import Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -10,19 +10,15 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource):
 
     def load_content(self) -> Dict[Path, str]:
         """Load and preprocess CSV file content."""
-        super().load_content()  # Validate the file path
+        content_dict = {}
-
+        for file_path in self.safe_file_paths:
-        file_path = (
+            with open(file_path, "r", encoding="utf-8") as csvfile:
-            self.file_path[0] if isinstance(self.file_path, list) else self.file_path
+                reader = csv.reader(csvfile)
-        )
+                content = ""
-        file_path = Path(file_path) if isinstance(file_path, str) else file_path
+                for row in reader:
-
+                    content += " ".join(row) + "\n"
-        with open(file_path, "r", encoding="utf-8") as csvfile:
+                content_dict[file_path] = content
-            reader = csv.reader(csvfile)
+        return content_dict
-            content = ""
-            for row in reader:
-                content += " ".join(row) + "\n"
-        return {file_path: content}
 
     def add(self) -> None:
         """
@@ -34,7 +30,7 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource):
         )
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -1,5 +1,6 @@
-from typing import Dict, List
 from pathlib import Path
+from typing import Dict, List
+
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
 
@@ -8,17 +9,15 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
 
     def load_content(self) -> Dict[Path, str]:
         """Load and preprocess Excel file content."""
-        super().load_content()  # Validate the file path
         pd = self._import_dependencies()
 
-        if isinstance(self.file_path, list):
+        content_dict = {}
-            file_path = self.file_path[0]
+        for file_path in self.safe_file_paths:
-        else:
+            file_path = self.convert_to_path(file_path)
-            file_path = self.file_path
+            df = pd.read_excel(file_path)
-
+            content = df.to_csv(index=False)
-        df = pd.read_excel(file_path)
+            content_dict[file_path] = content
-        content = df.to_csv(index=False)
+        return content_dict
-        return {file_path: content}
 
     def _import_dependencies(self):
         """Dynamically import dependencies."""
@@ -46,7 +45,7 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
 
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/json_knowledge_source.py

@@ -1,6 +1,6 @@
 import json
-from typing import Any, Dict, List
 from pathlib import Path
+from typing import Any, Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -10,11 +10,9 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource):
 
     def load_content(self) -> Dict[Path, str]:
         """Load and preprocess JSON file content."""
-        super().load_content()  # Validate the file path
-        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
-
         content: Dict[Path, str] = {}
-        for path in paths:
+        for path in self.safe_file_paths:
+            path = self.convert_to_path(path)
             with open(path, "r", encoding="utf-8") as json_file:
                 data = json.load(json_file)
             content[path] = self._json_to_text(data)
@@ -44,7 +42,7 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource):
         )
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/pdf_knowledge_source.py

@@ -1,5 +1,5 @@
-from typing import List, Dict
 from pathlib import Path
+from typing import Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -9,14 +9,13 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
 
     def load_content(self) -> Dict[Path, str]:
         """Load and preprocess PDF file content."""
-        super().load_content()  # Validate the file paths
         pdfplumber = self._import_pdfplumber()
 
-        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
         content = {}
 
-        for path in paths:
+        for path in self.safe_file_paths:
             text = ""
+            path = self.convert_to_path(path)
             with pdfplumber.open(path) as pdf:
                 for page in pdf.pages:
                     page_text = page.extract_text()
@@ -44,7 +43,7 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
         for _, text in self.content.items():
             new_chunks = self._chunk_text(text)
             self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/string_knowledge_source.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Optional
 
 from pydantic import Field
 
@@ -9,6 +9,7 @@ class StringKnowledgeSource(BaseKnowledgeSource):
     """A knowledge source that stores and queries plain text content using embeddings."""
 
     content: str = Field(...)
+    collection_name: Optional[str] = Field(default=None)
 
     def model_post_init(self, _):
         """Post-initialization method to validate content."""
@@ -23,7 +24,7 @@ class StringKnowledgeSource(BaseKnowledgeSource):
         """Add string content to the knowledge source, chunk it, compute embeddings, and save them."""
         new_chunks = self._chunk_text(self.content)
         self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/text_file_knowledge_source.py

@@ -1,5 +1,5 @@
-from typing import Dict, List
 from pathlib import Path
+from typing import Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -9,12 +9,11 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource):
 
     def load_content(self) -> Dict[Path, str]:
         """Load and preprocess text file content."""
-        super().load_content()
-        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
         content = {}
-        for path in paths:
+        for path in self.safe_file_paths:
-            with path.open("r", encoding="utf-8") as f:
+            path = self.convert_to_path(path)
-                content[path] = f.read()  # type: ignore
+            with open(path, "r", encoding="utf-8") as f:
+                content[path] = f.read()
         return content
 
     def add(self) -> None:
@@ -25,7 +24,7 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource):
         for _, text in self.content.items():
             new_chunks = self._chunk_text(text)
             self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/storage/knowledge_storage.py

@@ -1,14 +1,20 @@
 import contextlib
+import hashlib
 import io
 import logging
-import chromadb
 import os
-from crewai.utilities.paths import db_storage_path
+from typing import Any, Dict, List, Optional, Union, cast
-from typing import Optional, List
+
-from typing import Dict, Any
+import chromadb
-from crewai.utilities import EmbeddingConfigurator
+import chromadb.errors
+from chromadb.api import ClientAPI
+from chromadb.api.types import OneOrMany
+from chromadb.config import Settings
+
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
-import hashlib
+from crewai.utilities import EmbeddingConfigurator
+from crewai.utilities.logger import Logger
+from crewai.utilities.paths import db_storage_path
 
 
 @contextlib.contextmanager
@@ -35,9 +41,16 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     """
 
     collection: Optional[chromadb.Collection] = None
+    collection_name: Optional[str] = "knowledge"
+    app: Optional[ClientAPI] = None
 
-    def __init__(self, embedder_config: Optional[Dict[str, Any]] = None):
+    def __init__(
-        self._initialize_app(embedder_config or {})
+        self,
+        embedder_config: Optional[Dict[str, Any]] = None,
+        collection_name: Optional[str] = None,
+    ):
+        self.collection_name = collection_name
+        self._set_embedder_config(embedder_config)
 
     def search(
         self,
@@ -67,43 +80,80 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             else:
                 raise Exception("Collection not initialized")
 
-    def _initialize_app(self, embedder_config: Optional[Dict[str, Any]] = None):
+    def initialize_knowledge_storage(self):
-        import chromadb
+        base_path = os.path.join(db_storage_path(), "knowledge")
-        from chromadb.config import Settings
-
-        self._set_embedder_config(embedder_config)
-
         chroma_client = chromadb.PersistentClient(
-            path=f"{db_storage_path()}/knowledge",
+            path=base_path,
             settings=Settings(allow_reset=True),
         )
 
         self.app = chroma_client
 
         try:
-            self.collection = self.app.get_or_create_collection(name="knowledge")
+            collection_name = (
+                f"knowledge_{self.collection_name}"
+                if self.collection_name
+                else "knowledge"
+            )
+            if self.app:
+                self.collection = self.app.get_or_create_collection(
+                    name=collection_name, embedding_function=self.embedder_config
+                )
+            else:
+                raise Exception("Vector Database Client not initialized")
         except Exception:
             raise Exception("Failed to create or get collection")
 
     def reset(self):
         if self.app:
             self.app.reset()
+        else:
+            base_path = os.path.join(db_storage_path(), "knowledge")
+            self.app = chromadb.PersistentClient(
+                path=base_path,
+                settings=Settings(allow_reset=True),
+            )
+            self.app.reset()
 
     def save(
-        self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]]
+        self,
+        documents: List[str],
+        metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
     ):
         if self.collection:
-            metadatas = [metadata] if isinstance(metadata, dict) else metadata
+            try:
+                if metadata is None:
+                    metadatas: Optional[OneOrMany[chromadb.Metadata]] = None
+                elif isinstance(metadata, list):
+                    metadatas = [cast(chromadb.Metadata, m) for m in metadata]
+                else:
+                    metadatas = cast(chromadb.Metadata, metadata)
 
-            ids = [
+                ids = [
-                hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
+                    hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
-            ]
+                ]
 
-            self.collection.upsert(
+                self.collection.upsert(
-                documents=documents,
+                    documents=documents,
-                metadatas=metadatas,
+                    metadatas=metadatas,
-                ids=ids,
+                    ids=ids,
-            )
+                )
+            except chromadb.errors.InvalidDimensionException as e:
+                Logger(verbose=True).log(
+                    "error",
+                    "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
+                    "red",
+                )
+                raise ValueError(
+                    "Embedding dimension mismatch. Make sure you're using the same embedding model "
+                    "across all operations with this collection."
+                    "Try resetting the collection using `crewai reset-memories -a`"
+                ) from e
+            except Exception as e:
+                Logger(verbose=True).log(
+                    "error", f"Failed to upsert documents: {e}", "red"
+                )
+                raise
         else:
             raise Exception("Collection not initialized")
 

src/crewai/knowledge/utils/knowledge_utils.py

@@ -0,0 +1,12 @@
+from typing import Any, Dict, List
+
+
+def extract_knowledge_context(knowledge_snippets: List[Dict[str, Any]]) -> str:
+    """Extract knowledge from the task prompt."""
+    valid_snippets = [
+        result["context"]
+        for result in knowledge_snippets
+        if result and result.get("context")
+    ]
+    snippet = "\n".join(valid_snippets)
+    return f"Additional Information: {snippet}" if valid_snippets else ""