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Migrate docs from MkDocs to Mintlify #1423

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1 change: 0 additions & 1 deletion docs/CNAME
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78 changes: 41 additions & 37 deletions docs/core-concepts/Agents.md → docs/concepts/agents.mdx
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---
title: crewAI Agents
description: What are crewAI Agents and how to use them.
title: Agents
description: What are CrewAI Agents and how to use them.
icon: robot
---

## What is an Agent?
!!! note "What is an Agent?"
An agent is an **autonomous unit** programmed to:
<ul>
<li class='leading-3'>Perform tasks</li>
<li class='leading-3'>Make decisions</li>
<li class='leading-3'>Communicate with other agents</li>
</ul>
<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.
## What is an agent?

## Agent Attributes
An agent is an **autonomous unit** programmed to:
<ul>
<li class='leading-3'>Perform tasks</li>
<li class='leading-3'>Make decisions</li>
<li class='leading-3'>Communicate with other agents</li>
</ul>

<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.
</Tip>

## Agent attributes

| Attribute | Parameter | Description |
| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
Expand All @@ -30,7 +33,7 @@ description: What are crewAI Agents and how to use them.
| **Verbose** *(optional)* | `verbose` | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. |
| **Allow Delegation** *(optional)* | `allow_delegation` | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.
| **Step Callback** *(optional)* | `step_callback` | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`. |
| **Cache** *(optional)* | `cache` | Indicates if the agent should use a cache for tool usage. Default is `True`. |
| gbv vbn zzdsxcdsdfc**Cache** *(optional)* | `cache` | Indicates if the agent should use a cache for tool usage. Default is `True`. |
| **System Template** *(optional)* | `system_template` | Specifies the system format for the agent. Default is `None`. |
| **Prompt Template** *(optional)* | `prompt_template` | Specifies the prompt format for the agent. Default is `None`. |
| **Response Template** *(optional)* | `response_template` | Specifies the response format for the agent. Default is `None`. |
Expand All @@ -39,25 +42,25 @@ description: What are crewAI Agents and how to use them.
| **Use System Prompt** *(optional)* | `use_system_prompt` | Adds the ability to not use system prompt (to support o1 models). Default is `True`. |
| **Respect Context Window** *(optional)* | `respect_context_window` | Summary strategy to avoid overflowing the context window. Default is `True`. |

## Creating an Agent
## Creating an agent

!!! 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.
<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.
</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:

```python
# Example: Creating an agent with all attributes
```python Code example
from crewai import Agent

agent = Agent(
role='Data Analyst',
goal='Extract actionable insights',
backstory="""You're a data analyst at a large company.
You're responsible for analyzing data and providing insights
to the business.
You're currently working on a project to analyze the
performance of our marketing campaigns.""",
You're responsible for analyzing data and providing insights
to the business.
You're currently working on a project to analyze the
performance of our marketing campaigns.""",
tools=[my_tool1, my_tool2], # Optional, defaults to an empty list
llm=my_llm, # Optional
function_calling_llm=my_llm, # Optional
Expand Down Expand Up @@ -87,32 +90,31 @@ agent = Agent(

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:

```python
```python Code example
agent = Agent(
role="{topic} specialist",
goal="Figure {goal} out",
backstory="I am the master of {role}",
system_template="""<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>""",
{{ .System }}<|eot_id|>""",
prompt_template="""<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>""",
{{ .Prompt }}<|eot_id|>""",
response_template="""<|start_header_id|>assistant<|end_header_id|>

{{ .Response }}<|eot_id|>""",
)
{{ .Response }}<|eot_id|>""",
)
```

## Bring your Third Party Agents
!!! note "Extend your Third Party Agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the crewai's BaseAgent class."
## Bring your third-party agents

BaseAgent includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
Extend your third-party agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the CrewAI's `BaseAgent` class.

CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
<Note>
**BaseAgent** includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
</Note>

CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.

```py
```python Code example
from crewai import Agent, Task, Crew
from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.

Expand Down Expand Up @@ -152,4 +154,6 @@ crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
```

## Conclusion
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents,
you can create sophisticated AI systems that leverage the power of collaborative intelligence.
62 changes: 34 additions & 28 deletions docs/core-concepts/Cli.md → docs/concepts/cli.mdx
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---
title: CLI
description: Learn how to use the CrewAI CLI to interact with CrewAI.
icon: terminal
---

# CrewAI CLI Documentation

The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews and pipelines.
Expand All @@ -6,25 +12,25 @@ The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you

To use the CrewAI CLI, make sure you have CrewAI & Poetry installed:

```
```shell
pip install crewai poetry
```

## Basic Usage

The basic structure of a CrewAI CLI command is:

```
```shell
crewai [COMMAND] [OPTIONS] [ARGUMENTS]
```

## Available Commands

### 1. create
### 1. Create

Create a new crew or pipeline.

```
```shell
crewai create [OPTIONS] TYPE NAME
```

Expand All @@ -33,71 +39,71 @@ crewai create [OPTIONS] TYPE NAME
- `--router`: (Optional) Create a pipeline with router functionality

Example:
```
```shell
crewai create crew my_new_crew
crewai create pipeline my_new_pipeline --router
```

### 2. version
### 2. Version

Show the installed version of CrewAI.

```
```shell
crewai version [OPTIONS]
```

- `--tools`: (Optional) Show the installed version of CrewAI tools

Example:
```
```shell
crewai version
crewai version --tools
```

### 3. train
### 3. Train

Train the crew for a specified number of iterations.

```
```shell
crewai train [OPTIONS]
```

- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")

Example:
```
```shell
crewai train -n 10 -f my_training_data.pkl
```

### 4. replay
### 4. Replay

Replay the crew execution from a specific task.

```
```shell
crewai replay [OPTIONS]
```

- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks

Example:
```
```shell
crewai replay -t task_123456
```

### 5. log-tasks-outputs
### 5. Log-tasks-outputs

Retrieve your latest crew.kickoff() task outputs.

```
```shell
crewai log-tasks-outputs
```

### 6. reset-memories
### 6. Reset-memories

Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).

```
```shell
crewai reset-memories [OPTIONS]
```

Expand All @@ -108,35 +114,35 @@ crewai reset-memories [OPTIONS]
- `-a, --all`: Reset ALL memories

Example:
```
```shell
crewai reset-memories --long --short
crewai reset-memories --all
```

### 7. test
### 7. Test

Test the crew and evaluate the results.

```
```shell
crewai test [OPTIONS]
```

- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")

Example:
```
```shell
crewai test -n 5 -m gpt-3.5-turbo
```

### 8. run
### 8. Run

Run the crew.

```
```shell
crewai run
```

## Note

Make sure to run these commands from the directory where your CrewAI project is set up. Some commands may require additional configuration or setup within your project structure.
<Note>
Make sure to run these commands from the directory where your CrewAI project is set up.
Some commands may require additional configuration or setup within your project structure.
</Note>
52 changes: 52 additions & 0 deletions docs/concepts/collaboration.mdx
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---
title: Collaboration
description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
icon: screen-users
---

## Collaboration Fundamentals

Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.

- **Information Sharing**: Ensures all agents are well-informed and can contribute effectively by sharing data and findings.
- **Task Assistance**: Allows agents to seek help from peers with the required expertise for specific tasks.
- **Resource Allocation**: Optimizes task execution through the efficient distribution and sharing of resources among agents.

## Enhanced Attributes for Improved Collaboration

The `Crew` class has been enriched with several attributes to support advanced functionalities:

| Feature | Description |
|:-------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Language Model Management** (`manager_llm`, `function_calling_llm`) | Manages language models for executing tasks and tools. `manager_llm` is required for hierarchical processes, while `function_calling_llm` is optional with a default value for streamlined interactions. |
| **Custom Manager Agent** (`manager_agent`) | Specifies a custom agent as the manager, replacing the default CrewAI manager. |
| **Process Flow** (`process`) | Defines execution logic (e.g., sequential, hierarchical) for task distribution. |
| **Verbose Logging** (`verbose`) | Provides detailed logging for monitoring and debugging. Accepts integer and boolean values to control verbosity level. |
| **Rate Limiting** (`max_rpm`) | Limits requests per minute to optimize resource usage. Setting guidelines depend on task complexity and load. |
| **Internationalization / Customization** (`language`, `prompt_file`) | Supports prompt customization for global usability. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json) |
| **Execution and Output Handling** (`full_output`) | Controls output granularity, distinguishing between full and final outputs. |
| **Callback and Telemetry** (`step_callback`, `task_callback`) | Enables step-wise and task-level execution monitoring and telemetry for performance analytics. |
| **Crew Sharing** (`share_crew`) | Allows sharing crew data with CrewAI for model improvement. Privacy implications and benefits should be considered. |
| **Usage Metrics** (`usage_metrics`) | Logs all LLM usage metrics during task execution for performance insights. |
| **Memory Usage** (`memory`) | Enables memory for storing execution history, aiding in agent learning and task efficiency. |
| **Embedder Configuration** (`embedder`) | Configures the embedder for language understanding and generation, with support for provider customization. |
| **Cache Management** (`cache`) | Specifies whether to cache tool execution results, enhancing performance. |
| **Output Logging** (`output_log_file`) | Defines the file path for logging crew execution output. |
| **Planning Mode** (`planning`) | Enables action planning before task execution. Set `planning=True` to activate. |
| **Replay Feature** (`replay`) | Provides CLI for listing tasks from the last run and replaying from specific tasks, aiding in task management and troubleshooting. |

## Delegation (Dividing to Conquer)

Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.

## Implementing Collaboration and Delegation

Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation, with enhanced customization and monitoring features to adapt to various operational needs.

## Example Scenario

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.
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