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33
docs/core-concepts/Agents.md
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@@ -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:
@@ -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

10
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.
@@ -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.
@@ -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
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.
@@ -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.
@@ -47,12 +48,19 @@ 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(
@@ -60,14 +68,25 @@ my_crew = Crew(
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
@@ -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)
```

31
docs/core-concepts/Processes.md
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@@ -10,26 +10,37 @@ description: Detailed guide on workflow management through processes in CrewAI,
## Process Implementations
- **Sequential**: Executes tasks sequentially, ensuring tasks are completed in an orderly progression.
- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command, the manager for delegation is automatically created by crewAI.
- **Consensual (Planned)**: A future process type aiming for collaborative decision-making among agents on task execution, introducing a more democratic approach to task management within CrewAI.
- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. Note: A manager language model (`manager_llm`) must be specified in the crew to enable the hierarchical process, allowing for the creation and management of tasks by the manager.
- **Consensual Process (Planned)**: Currently under consideration for future development, this process type aims for collaborative decision-making among agents on task execution, introducing a more democratic approach to task management within CrewAI. As of now, it is not implemented in the codebase.
## The Role of Processes in Teamwork
Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.
## Assigning Processes to a Crew
Specify the process type upon crew creation to set the execution strategy:
To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. Note: For a hierarchical process, ensure to define `manager_llm` for the manager agent.
```python
from crewai import Crew
from crewai.process import Process
from langchain_openai import ChatOpenAI
# Example: Creating a crew with a sequential process
crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.sequential)
crew = Crew(
agents=my_agents,
tasks=my_tasks,
process=Process.sequential
)
# Example: Creating a crew with a hierarchical process
crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.hierarchical)
# Ensure to provide a manager_llm
crew = Crew(
agents=my_agents,
tasks=my_tasks,
process=Process.hierarchical,
manager_llm=ChatOpenAI(model="gpt-4")
)
```
**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object.
**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, `manager_llm` is also required.
## Sequential Process
This method mirrors dynamic team workflows, progressing through tasks in a thoughtful and systematic manner. Task execution follows the predefined order in the task list, with the output of one task serving as context for the next.
@@ -37,13 +48,13 @@ This method mirrors dynamic team workflows, progressing through tasks in a thoug
To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
## Hierarchical Process
Emulates a corporate hierarchy. A "manager" agent is automatically created so it oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents, reviews outputs, and assesses task completion.
Emulates a corporate hierarchy, crewAI creates a manager automatically for you, requiring the specification of a manager language model (`manager_llm`) for the manager agent. This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
## Process Class: Detailed Overview
The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential` and `hierarchical`). This design choice guarantees that only valid processes are utilized within the CrewAI framework.
The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`, and future `consensual`). This design choice guarantees that only valid processes are utilized within the CrewAI framework.
## Planned Future Processes
- **Consensual Process**: A collaborative decision-making process among agents on task execution is planned but not currently implemented. This future enhancement will introduce a more democratic approach to task management within CrewAI.
- **Consensual Process**: This collaborative decision-making process among agents on task execution is under consideration but not currently implemented. This future enhancement aims to introduce a more democratic approach to task management within CrewAI.
## Conclusion
The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. Documentation will be updated to reflect new processes and enhancements, ensuring users have access to the most current and comprehensive information.
The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. Documentation will be regularly updated to reflect new processes and enhancements, ensuring users have access to the most current and comprehensive information.

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docs/core-concepts/Tasks.md
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@@ -1,3 +1,4 @@
```markdown
---
title: crewAI Tasks
description: Overview and management of tasks within the crewAI framework.
@@ -17,10 +18,10 @@ Tasks in CrewAI can be designed to require collaboration between agents. For exa
| **Agent** | Optionally, you can specify which agent is responsible for the task. If not, the crew's process will determine who takes it on. |
| **Expected Output** | Clear and detailed definition of expected output for the task. |
| **Tools** *(optional)* | These are the functions or capabilities the agent can utilize to perform the task. They can be anything from simple actions like 'search' to more complex interactions with other agents or APIs. |
| **Async Execution** *(optional)* | If the task should be executed asynchronously. This indicates that the crew will not wait for the task to be completed to continue with the next task. |
| **Async Execution** *(optional)* | Indicates whether the task should be executed asynchronously, allowing the crew to continue with the next task without waiting for completion. |
| **Context** *(optional)* | Other tasks that will have their output used as context for this task. If a task is asynchronous, the system will wait for that to finish before using its output as context. |
| **Output JSON** *(optional)* | Takes a pydantic model and returns the output as a JSON object. **Agent LLM needs to be using OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
| **Output Pydantic** *(optional)* | Takes a pydantic model and returns the output as a pydantic object. **Agent LLM needs to be using OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
| **Output JSON** *(optional)* | Takes a pydantic model and returns the output as a JSON object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
| **Output Pydantic** *(optional)* | Takes a pydantic model and returns the output as a pydantic object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
| **Output File** *(optional)* | Takes a file path and saves the output of the task on it. |
| **Callback** *(optional)* | A function to be executed after the task is completed. |
@@ -48,10 +49,10 @@ Tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and
```python
import os
os.environ["OPENAI_API_KEY"] = "Your Key"
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
from crewai import Agent, Task, Crew
from langchain.agents import Tool
from langchain_community.tools import DuckDuckGoSearchRun
from crewai_tools import SerperDevTool
research_agent = Agent(
role='Researcher',
@@ -62,9 +63,7 @@ research_agent = Agent(
verbose=True
)
# Install duckduckgo-search for this example:
# !pip install -U duckduckgo-search
search_tool = DuckDuckGoSearchRun()
search_tool = SerperDevTool()
task = Task(
description='Find and summarize the latest AI news',
@@ -87,16 +86,25 @@ This demonstrates how tasks with specific tools can override an agent's default
## Referring to Other Tasks
In crewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output should be used as context for another task.
In crewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple should be used as context for another task.
This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
```python
# ...
research_task = Task(
research_ai_task = Task(
description='Find and summarize the latest AI news',
expected_output='A bullet list summary of the top 5 most important AI news',
async_execution=True,
agent=research_agent,
tools=[search_tool]
)
research_ops_task = Task(
description='Find and summarize the latest AI Ops news',
expected_output='A bullet list summary of the top 5 most important AI Ops news',
async_execution=True,
agent=research_agent,
tools=[search_tool]
)
@@ -105,7 +113,7 @@ 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',
agent=writer_agent,
context=[research_task]
context=[research_ai_task, research_ops_task]
)
#...
@@ -146,7 +154,7 @@ write_article = Task(
## Callback Mechanism
You can define a callback function that will be executed after the task is completed. This is useful for tasks that need to trigger some side effect after they are completed, while the crew is still running.
The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.
```python
# ...
@@ -217,5 +225,4 @@ These validations help in maintaining the consistency and reliability of task ex
## Conclusion
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 and following robust validation practices is crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
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.

49
docs/core-concepts/Tools.md
View File

@@ -1,3 +1,4 @@
```
---
title: crewAI Tools
description: Understanding and leveraging tools within the crewAI framework for agent collaboration and task execution.
@@ -12,41 +13,40 @@ CrewAI tools empower agents with capabilities ranging from web searching and dat
## Key Characteristics of Tools
- **Utility**: Designed for various tasks such as web searching, data analysis, content generation, and agent collaboration.
- **Integration**: Enhances agent capabilities by integrating tools directly into their workflow.
- **Customizability**: Offers flexibility to develop custom tools or use existing ones, catering to specific agent needs.
- **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
- **Integration**: Boosts agent capabilities by seamlessly integrating tools into their workflow.
- **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
## Using crewAI Tools
crewAI comes with a series to built-in tools that can be used to extend the capabilities of your agents. Start by installing our extra tools package:
To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:
```bash
pip install 'crewai[tools]'
```
Here is an example on how to use them:
Here's an example demonstrating their use:
```python
import os
from crewai import Agent, Task, Crew
# Importing some of the crewAI tools
# Importing crewAI tools
from crewai_tools import (
DirectoryReadTool,
FileReadTool,
SeperDevTool,
SerperDevTool,
WebsiteSearchTool
)
# get a free account in serper.dev
# Set up API keys
os.environ["SERPER_API_KEY"] = "Your Key"
os.environ["OPENAI_API_KEY"] = "Your Key"
# Instantiate tools
# Assumes this ./blog-posts exists with existing blog posts on it
docs_tools = DirectoryReadTool(directory='./blog-posts')
file_read_tool = FileReadTool()
docs_tool = DirectoryReadTool(directory='./blog-posts')
file_tool = FileReadTool()
search_tool = SeperDevTool()
website_rag = WebsiteSearchTool()
web_rag_tool = WebsiteSearchTool()
# Create agents
researcher = Agent(
@@ -59,35 +59,34 @@ researcher = Agent(
writer = Agent(
role='Content Writer',
goal='Write amazing, super engaging blog post about the AI industry',
goal='Craft engaging blog posts about the AI industry',
backstory='A skilled writer with a passion for technology.',
tools=[docs_tools, file_read_tool],
tools=[docs_tool, file_tool],
verbose=True
)
# Create tasks
# Define tasks
research = Task(
description='Research the AI industry and provide a summary of the latest most trending matters and developments.',
expected_output='A summary of the top 3 latest most trending matters and developments in the AI industry with you unique take on why they matter.',
description='Research the latest trends in the AI industry and provide a summary.',
expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
agent=researcher
)
write = Task(
description='Write an engaging blog post about the AI industry, using the summary provided by the research analyst. Read the latest blog posts in the directory to get inspiration.',
expected_output='A 4 paragraph blog post formatted as markdown with proper subtitles about the latest trends that is engaging and informative and funny, avoid complex words and make it easy to read.',
description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
agent=writer,
output_file='blog-posts/new_post.md' # The final blog post will be written here
output_file='blog-posts/new_post.md' # The final blog post will be saved here
)
# Create a crew
# Assemble a crew
crew = Crew(
agents=[researcher, writer],
tasks=[research, write],
verbose=2
)
# Execute the tasks
# Execute tasks
crew.kickoff()
```
@@ -156,7 +155,7 @@ class MyCustomTool(BaseTool):
def _run(self, argument: str) -> str:
# Implementation goes here
pass
return "Result from custom tool"
```
Define a new class inheriting from `BaseTool`, specifying `name`, `description`, and the `_run` method for operational logic.
@@ -230,4 +229,4 @@ agent = Agent(
```
## Conclusion
Tools are crucial for extending the capabilities of CrewAI agents, allowing them to undertake a diverse array of tasks and collaborate efficiently. When building your AI solutions with CrewAI, consider both custom and existing tools to empower your agents and foster a dynamic AI ecosystem.
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.