andre/crewAI
1
0
Fork 0
mirror of https://github.com/crewAIInc/crewAI.git synced 2026-01-09 08:08:32 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Merge pull request #1675 from rokbenko/rok

Browse Source 
[DOCS] Update Agents docs to include two approaches for creating an agent
This commit is contained in:
Tony Kipkemboi
2024-11-30 11:26:10 -05:00
committed by GitHub
parent cd9a17a281 5735496227
commit 404da4066b
1 changed files with 138 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files

170
docs/concepts/agents.mdx
View File

@@ -7,49 +7,154 @@ icon: robot
## What is an agent? ## What is an agent?
An agent is an **autonomous unit** programmed to: An agent is an **autonomous unit** programmed to:
<ul> <ul>
<li class='leading-3'>Perform tasks</li> <li class="leading-3">Perform tasks</li>
<li class='leading-3'>Make decisions</li> <li class="leading-3">Make decisions</li>
<li class='leading-3'>Communicate with other agents</li> <li class="leading-3">Communicate with other agents</li>
</ul> </ul>
<Tip> <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 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> </Tip>
## Agent attributes ## Agent attributes
| Attribute | Parameter | Description | | Attribute | Parameter | Description |
| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | :-------------------------------------- | :----------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Role** | `role` | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. | | **Role** | `role` | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | `goal` | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. | | **Goal** | `goal` | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | `backstory`| Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. | | **Backstory** | `backstory` | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** *(optional)* | `llm` | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. | | **LLM** _(optional)_ | `llm` | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. |
| **Tools** *(optional)* | `tools` | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list. | | **Tools** _(optional)_ | `tools` | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list. |
| **Function Calling LLM** *(optional)* | `function_calling_llm` | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`. | | **Function Calling LLM** _(optional)_ | `function_calling_llm` | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`. |
| **Max Iter** *(optional)* | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`. | | **Max Iter** _(optional)_ | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`. |
| **Max RPM** *(optional)* | `max_rpm` | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`. | | **Max RPM** _(optional)_ | `max_rpm` | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`. |
| **Max Execution Time** *(optional)* | `max_execution_time` | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time. | | **Max Execution Time** _(optional)_ | `max_execution_time` | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time. |
| **Verbose** *(optional)* | `verbose` | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. | | **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`. | | **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`. | | **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`. | | **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`. | | **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`. | | **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`. | | **Response Template** _(optional)_ | `response_template` | Specifies the response format for the agent. Default is `None`. |
| **Allow Code Execution** *(optional)* | `allow_code_execution` | Enable code execution for the agent. Default is `False`. | | **Allow Code Execution** _(optional)_ | `allow_code_execution` | Enable code execution for the agent. Default is `False`. |
| **Max Retry Limit** *(optional)* | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`. | | **Max Retry Limit** _(optional)_ | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`. |
| **Use System Prompt** *(optional)* | `use_system_prompt` | Adds the ability to not use system prompt (to support o1 models). Default is `True`. | | **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`. | | **Respect Context Window** _(optional)_ | `respect_context_window` | Summary strategy to avoid overflowing the context window. 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` | Determines the mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution on the host machine). Default is `safe`. |
## Creating an agent ## Creating an agent
<Note> <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. **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> </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: Agents can be created using one of the following methods:
<ul>
<li class="leading-3">
**With a YAML configuration (recommended):** Define agent properties in a
structured YAML file, promoting reusability and cleaner code.
</li>
<li class="leading-3">
**Without a YAML configuration:** Define agent properties directly in your
code.
</li>
</ul>
### Creating an agent with a YAML configuration (recommended)
The YAML configuration approach allows you to separate the agent's required properties (i.e., `role`, `goal`, and `backstory`) from the code logic. This makes your setup modular, easier to manage, and reusable across multiple projects.
#### Step 1: Define an agent in a YAML file
The YAML file contains required properties of the agent:
<ul>
<li class="leading-3">
`role`: A short description of the agent's purpose or position.
</li>
<li class="leading-3">
`goal`: The primary objective the agent is designed to accomplish.
</li>
<li class="leading-3">
`backstory`: A detailed description of the agent's context to enhance its
understanding and responses.
</li>
</ul>
<Note>
The YAML file should only include these three properties (i.e., `role`,
`goal`, and `backstory`). Any additional configurations must be defined in the
`crew.py` file.
</Note>
```yaml agents.yaml
data_analyst:
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.
```
#### Step 2: Initialize the agent in `crew.py`
After defining the agent in the YAML file, you need to create an instance of the `Agent` class in your code and link it to the YAML configuration. This is done in the `crew.py` file, where additional properties can be specified.
```python crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
@CrewBase
class ResearchCrew():
"""Research crew"""
agents_config = "your/path/to/agents.yaml"
@agent
def data_analyst(self) -> Agent:
return Agent(
config=self.agents_config['data_analyst'],
tools=[my_tool1, my_tool2], # Optional, defaults to an empty list
llm=my_llm, # Optional
function_calling_llm=my_llm, # Optional
max_iter=15, # Optional
max_rpm=None, # Optional
max_execution_time=None, # Optional
verbose=True, # Optional
allow_delegation=False, # Optional
step_callback=my_intermediate_step_callback, # Optional
cache=True, # Optional
system_template=my_system_template, # Optional
prompt_template=my_prompt_template, # Optional
response_template=my_response_template, # Optional
config=my_config, # Optional
crew=my_crew, # Optional
tools_handler=my_tools_handler, # Optional
cache_handler=my_cache_handler, # Optional
callbacks=[callback1, callback2], # Optional
allow_code_execution=True, # Optional
max_retry_limit=2, # Optional
use_system_prompt=True, # Optional
respect_context_window=True, # Optional
code_execution_mode='safe', # Optional, defaults to 'safe'
)
# ... remaining code
```
### Creating an agent without a YAML configuration
The non-YAML configuration approach allows you to define the agent's properties directly in your code. Initialize an instance of the `Agent` class with the desired properties.
```python Code example ```python Code example
from crewai import Agent from crewai import Agent
@@ -110,8 +215,9 @@ agent = Agent(
Extend your third-party agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the CrewAI's `BaseAgent` class. Extend your third-party agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the CrewAI's `BaseAgent` class.
<Note> <Note>
**BaseAgent** includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew. **BaseAgent** includes attributes and methods required to integrate with your
crews to run and delegate tasks to other agents within your own crew.
</Note> </Note>
CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems. CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
@@ -157,5 +263,5 @@ crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
## Conclusion ## Conclusion
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, 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. The `code_execution_mode` attribute provides flexibility in how agents execute code, allowing for both secure and direct execution options. 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.