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33 changes: 17 additions & 16 deletions docs/core-concepts/Agents.md
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Expand Up @@ -10,30 +10,31 @@ description: What are crewAI Agents and how to use them.
<li class='leading-3'>Perform tasks</li>
<li class='leading-3'>Make decisions</li>
<li class='leading-3'>Communicate with other agents</li>
<ul>
<br/>
Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.

## Agent Attributes

| Attribute | Description |
| :------------------ | :----------------------------------- |
| **Role** | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** | The language model used by the agent to process and generate text. Defaults to using OpenAI's GPT-4 (`ChatOpenAI`), unless another model is specified through the environment variable "OPENAI_MODEL_NAME". |
| **Tools** | Set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents. It's an attribute that can be set during the initialization of an agent. |
| **Function Calling LLM** | If passed this agent will use this LLM to do function calling for tools instead of relying on the main llm output |
| **Max Iter** | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `15`. |
| **Max RPM** | The maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified. |
| **Verbose** | Enables detailed logging of the agent's execution for debugging or monitoring purposes when set to True. Default is `False` |
| **Allow Delegation**| Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. |
| **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`. |
| **Memory** | Indicates whether the agent should have memory or not, with a default value of False. This impacts the agent's ability to remember past interactions. Default is `False` |
| Attribute | Description |
| :--------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Role** | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** *(optional)* | The language model used by the agent to process and generate text. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. |
| **Tools** *(optional)* | Set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents. It's an attribute that can be set during the initialization of an agent, with a default value of an empty list. |
| **Function Calling LLM** *(optional)* | If passed, this agent will use this LLM to execute function calling for tools instead of relying on the main LLM output. |
| **Max Iter** *(optional)* | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `15`. |
| **Max RPM** *(optional)* | 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`. |
| **Verbose** *(optional)* | Enables detailed logging of the agent's execution for debugging or monitoring purposes when set to True. Default is `False`. |
| **Allow Delegation** *(optional)* | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`. |
| **Step Callback** *(optional)* | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`. |
| **Memory** *(optional)* | Indicates whether the agent should have memory or not, with a default value of False. This impacts the agent's ability to remember past interactions. Default is `False`. |

## Creating an Agent

!!! note "Agent Interaction"
Agents can interact with each other using the CrewAI's built-in delegation and communication mechanisms.<br/>This allows for dynamic task management and problem-solving within the crew.
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.

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:

Expand All @@ -49,7 +50,7 @@ agent = Agent(
to the business.
You're currently working on a project to analyze the
performance of our marketing campaigns.""",
tools=[my_tool1, my_tool2], # Optional
tools=[my_tool1, my_tool2], # Optional, defaults to an empty list
llm=my_llm, # Optional
function_calling_llm=my_llm, # Optional
max_iter=15, # Optional
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10 changes: 6 additions & 4 deletions docs/core-concepts/Collaboration.md
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@@ -1,3 +1,4 @@
```markdown
---
title: How Agents Collaborate in CrewAI
description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
Expand All @@ -14,15 +15,16 @@ description: Exploring the dynamics of agent collaboration within the CrewAI fra
## Enhanced Attributes for Improved Collaboration
The `Crew` class has been enriched with several attributes to support advanced functionalities:

- **Language Model Management (`manager_llm`, `function_calling_llm`)**: Manages language models for executing tasks and tools, facilitating sophisticated agent-tool interactions.
- **Language Model Management (`manager_llm`, `function_calling_llm`)**: Manages language models for executing tasks and tools, facilitating sophisticated agent-tool interactions. It's important to note that `manager_llm` is mandatory when using a hierarchical process for ensuring proper execution flow.
- **Process Flow (`process`)**: Defines the execution logic (e.g., sequential, hierarchical) to streamline task distribution and execution.
- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes.
- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes. It supports both integer and boolean types to indicate the verbosity level.
- **Configuration (`config`)**: Allows extensive customization to tailor the crew's behavior according to specific requirements.
- **Rate Limiting (`max_rpm`)**: Ensures efficient utilization of resources by limiting requests per minute.
- **Internationalization Support (`language`)**: Facilitates operation in multiple languages, enhancing global usability.
- **Execution and Output Handling (`full_output`)**: Distinguishes between full and final outputs for nuanced control over task results.
- **Callback and Telemetry (`step_callback`)**: Integrates callbacks for step-wise execution monitoring and telemetry for performance analytics.
- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement.
- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models.
- **Usage Metrics (`usage_metrics`)**: Store all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement, you can check it after the crew execution.

## Delegation: Dividing to Conquer
Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
Expand All @@ -34,4 +36,4 @@ Setting up a crew involves defining the roles and capabilities of each agent. Cr
Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.

## Conclusion
The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.
The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.
58 changes: 38 additions & 20 deletions docs/core-concepts/Crews.md
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@@ -1,3 +1,4 @@
```markdown
---
title: crewAI Crews
description: Understanding and utilizing crews in the crewAI framework.
Expand All @@ -9,20 +10,20 @@ description: Understanding and utilizing crews in the crewAI framework.

## Crew Attributes

| Attribute | Description |
| :------------------- | :----------------------------------------------------------- |
| **Tasks** | A list of tasks assigned to the crew. |
| **Agents** | A list of agents that are part of the crew. |
| **Process** | The process flow (e.g., sequential, hierarchical) the crew follows. |
| **Verbose** | The verbosity level for logging during execution. |
| **Manager LLM** | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
| **Function Calling LLM** | If passed crew will use this LLM to do function calling for tools for all agents in the crew |
| **Config** | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
| **Max RPM** | Maximum requests per minute the crew adheres to during execution. |
| **Language** | Language used for the crew, defaults to English. |
| **Full Output** | Whether the crew should return the full output with all tasks outputs or just the final output. |
| **Step Callback** | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
| **Share Crew** | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
| Attribute | Description |
| :---------------------- | :----------------------------------------------------------- |
| **Tasks** | A list of tasks assigned to the crew. |
| **Agents** | A list of agents that are part of the crew. |
| **Process** *(optional)* | The process flow (e.g., sequential, hierarchical) the crew follows. |
| **Verbose** *(optional)* | The verbosity level for logging during execution. |
| **Manager LLM** *(optional)* | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
| **Function Calling LLM** *(optional)* | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
| **Config** *(optional)* | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
| **Max RPM** *(optional)* | Maximum requests per minute the crew adheres to during execution. |
| **Language** *(optional)* | Language used for the crew, defaults to English. |
| **Full Output** *(optional)* | Whether the crew should return the full output with all tasks outputs or just the final output. |
| **Step Callback** *(optional)* | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
| **Share Crew** *(optional)* | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |

!!! note "Crew Max RPM"
The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
Expand All @@ -47,27 +48,45 @@ researcher = Agent(

writer = Agent(
role='Content Writer',
goal='Write engaging articles on AI discoveries'
goal='Write engaging articles on AI discoveries',
verbose=True
)

# Create tasks for the agents
research_task = Task(description='Identify breakthrough AI technologies', agent=researcher)
write_article_task = Task(description='Draft an article on the latest AI technologies', agent=writer)
research_task = Task(
description='Identify breakthrough AI technologies',
agent=researcher
)
write_article_task = Task(
description='Draft an article on the latest AI technologies',
agent=writer
)

# Assemble the crew with a sequential process
my_crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_article_task],
process=Process.sequential,
full_output=True,
verbose=True
verbose=True,
)
```

## Crew Usage Metrics

After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.

```python
# Access the crew's usage metrics
crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
crew.kickoff()
print(crew.usage_metrics)
```

## Crew Execution Process

- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` is required for this process.
- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` is required for this process and it's essential for validating the process flow.

### Kicking Off a Crew

Expand All @@ -77,4 +96,3 @@ Once your crew is assembled, initiate the workflow with the `kickoff()` method.
# Start the crew's task execution
result = my_crew.kickoff()
print(result)
```
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