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Documentation Improvements: LLM Configuration and Usage (#1684)
* docs: improve tasks documentation clarity and structure - Add Task Execution Flow section - Add variable interpolation explanation - Add Task Dependencies section with examples - Improve overall document structure and readability - Update code examples with proper syntax highlighting * docs: update agent documentation with improved examples and formatting - Replace DuckDuckGoSearchRun with SerperDevTool - Update code block formatting to be consistent - Improve template examples with actual syntax - Update LLM examples to use current models - Clean up formatting and remove redundant comments * docs: enhance LLM documentation with Cerebras provider and formatting improvements * docs: simplify LLMs documentation title * docs: improve installation guide clarity and structure - Add clear Python version requirements with check command - Simplify installation options to recommended method - Improve upgrade section clarity for existing users - Add better visual structure with Notes and Tips - Update description and formatting * docs: improve introduction page organization and clarity - Update organizational analogy in Note section - Improve table formatting and alignment - Remove emojis from component table for cleaner look - Add 'helps you' to make the note more action-oriented * docs: add enterprise and community cards - Add Enterprise deployment card in quickstart - Add community card focused on open source discussions - Remove deployment reference from community description - Clean up introduction page cards - Remove link from Enterprise description text
This commit is contained in:
Tony Kipkemboi
@@ -1,6 +1,6 @@
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---
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title: Tasks
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description: Detailed guide on managing and creating tasks within the CrewAI framework, reflecting the latest codebase updates.
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description: Detailed guide on managing and creating tasks within the CrewAI framework.
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icon: list-check
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---
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@@ -8,41 +8,171 @@ icon: list-check
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In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
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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.
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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.
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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.
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### Task Execution Flow
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Tasks can be executed in two ways:
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- **Sequential**: Tasks are executed in the order they are defined
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- **Hierarchical**: Tasks are assigned to agents based on their roles and expertise
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The execution flow is defined when creating the crew:
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```python Code
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crew = Crew(
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agents=[agent1, agent2],
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tasks=[task1, task2],
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process=Process.sequential # or Process.hierarchical
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)
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```
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## Task Attributes
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| Attribute | Parameters | Type | Description |
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| :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
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| **Description** | `description` | `str` | A clear, concise statement of what the task entails. |
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| **Agent** | `agent` | `Optional[BaseAgent]` | The agent responsible for the task, assigned either directly or by the crew's process. |
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| **Expected Output** | `expected_output` | `str` | A detailed description of what the task's completion looks like. |
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| **Tools** _(optional)_ | `tools` | `Optional[List[Any]]` | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list. |
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| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False. |
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| **Context** _(optional)_ | `context` | `Optional[List["Task"]]` | Specifies tasks whose outputs are used as context for this task. |
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| **Config** _(optional)_ | `config` | `Optional[Dict[str, Any]]` | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
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| **Output JSON** _(optional)_ | `output_json` | `Optional[Type[BaseModel]]` | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set. |
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| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
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| **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. |
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| **Output** _(optional)_ | `output` | `Optional[TaskOutput]` | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details. |
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| **Callback** _(optional)_ | `callback` | `Optional[Any]` | A callable that is executed with the task's output upon completion. |
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| **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.|
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| **Converter Class** _(optional)_ | `converter_cls` | `Optional[Type[Converter]]` | A converter class used to export structured output. Defaults to None. |
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| **Name** _(optional)_ | `name` | `Optional[str]` | A name identifier for the task. |
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| **Agent** _(optional)_ | `agent` | `Optional[BaseAgent]` | The agent responsible for executing the task. |
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| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | The tools/resources the agent is limited to use for this task. |
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| **Context** _(optional)_ | `context` | `Optional[List["Task"]]` | Other tasks whose outputs will be used as context for this task. |
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| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]` | Whether the task should be executed asynchronously. Defaults to False. |
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| **Config** _(optional)_ | `config` | `Optional[Dict[str, Any]]` | Task-specific configuration parameters. |
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| **Output File** _(optional)_ | `output_file` | `Optional[str]` | File path for storing the task output. |
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| **Output JSON** _(optional)_ | `output_json` | `Optional[Type[BaseModel]]` | A Pydantic model to structure the JSON output. |
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| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | A Pydantic model for task output. |
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| **Callback** _(optional)_ | `callback` | `Optional[Any]` | Function/object to be executed after task completion. |
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## Creating a Task
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## Creating Tasks
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Creating a task involves defining its scope, responsible agent, and any additional attributes for flexibility:
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There are two ways to create tasks in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
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### YAML Configuration (Recommended)
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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.
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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.
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<Note>
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Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
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```python Code
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crew.kickoff(inputs={'topic': 'AI Agents'})
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```
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</Note>
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Here's an example of how to configure tasks using YAML:
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```yaml tasks.yaml
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research_task:
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description: >
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Conduct a thorough research about {topic}
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Make sure you find any interesting and relevant information given
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the current year is 2024.
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expected_output: >
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A list with 10 bullet points of the most relevant information about {topic}
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agent: researcher
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reporting_task:
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description: >
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Review the context you got and expand each topic into a full section for a report.
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Make sure the report is detailed and contains any and all relevant information.
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expected_output: >
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A fully fledge reports with the mains topics, each with a full section of information.
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Formatted as markdown without '```'
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agent: reporting_analyst
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output_file: report.md
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```
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To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
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```python crew.py
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# src/latest_ai_development/crew.py
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from crewai import Agent, Crew, Process, Task
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from crewai.project import CrewBase, agent, crew, task
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from crewai_tools import SerperDevTool
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@CrewBase
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class LatestAiDevelopmentCrew():
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"""LatestAiDevelopment crew"""
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config['researcher'],
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verbose=True,
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tools=[SerperDevTool()]
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)
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@agent
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def reporting_analyst(self) -> Agent:
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return Agent(
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config=self.agents_config['reporting_analyst'],
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verbose=True
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)
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@task
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def research_task(self) -> Task:
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return Task(
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config=self.tasks_config['research_task']
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)
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@task
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def reporting_task(self) -> Task:
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return Task(
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config=self.tasks_config['reporting_task']
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)
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@crew
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def crew(self) -> Crew:
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return Crew(
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agents=[
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self.researcher(),
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self.reporting_analyst()
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],
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tasks=[
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self.research_task(),
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self.reporting_task()
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],
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process=Process.sequential
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)
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```
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<Note>
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The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
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</Note>
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### Direct Code Definition (Alternative)
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Alternatively, you can define tasks directly in your code without using YAML configuration:
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```python task.py
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from crewai import Task
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task = Task(
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description='Find and summarize the latest and most relevant news on AI',
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agent=sales_agent,
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expected_output='A bullet list summary of the top 5 most important AI news',
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research_task = Task(
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description="""
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Conduct a thorough research about AI Agents.
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Make sure you find any interesting and relevant information given
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the current year is 2024.
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""",
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expected_output="""
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A list with 10 bullet points of the most relevant information about AI Agents
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""",
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agent=researcher
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)
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reporting_task = Task(
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description="""
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Review the context you got and expand each topic into a full section for a report.
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Make sure the report is detailed and contains any and all relevant information.
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""",
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expected_output="""
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A fully fledge reports with the mains topics, each with a full section of information.
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Formatted as markdown without '```'
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""",
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agent=reporting_analyst,
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output_file="report.md"
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)
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```
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@@ -52,6 +182,8 @@ task = Task(
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## Task Output
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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.
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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.
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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.
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@@ -112,6 +244,25 @@ if task_output.pydantic:
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print(f"Pydantic Output: {task_output.pydantic}")
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```
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## Task Dependencies and Context
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Tasks can depend on the output of other tasks using the `context` attribute. For example:
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```python Code
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research_task = Task(
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description="Research the latest developments in AI",
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expected_output="A list of recent AI developments",
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agent=researcher
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)
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analysis_task = Task(
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description="Analyze the research findings and identify key trends",
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expected_output="Analysis report of AI trends",
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agent=analyst,
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context=[research_task] # This task will wait for research_task to complete
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)
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```
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## Integrating Tools with Tasks
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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.
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@@ -167,16 +318,16 @@ This is useful when you have a task that depends on the output of another task t
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# ...
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research_ai_task = Task(
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description='Find and summarize the latest AI news',
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expected_output='A bullet list summary of the top 5 most important AI news',
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description="Research the latest developments in AI",
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expected_output="A list of recent AI developments",
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async_execution=True,
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agent=research_agent,
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tools=[search_tool]
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)
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research_ops_task = Task(
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description='Find and summarize the latest AI Ops news',
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expected_output='A bullet list summary of the top 5 most important AI Ops news',
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description="Research the latest developments in AI Ops",
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expected_output="A list of recent AI Ops developments",
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async_execution=True,
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agent=research_agent,
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tools=[search_tool]
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@@ -184,7 +335,7 @@ research_ops_task = Task(
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write_blog_task = Task(
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description="Write a full blog post about the importance of AI and its latest news",
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expected_output='Full blog post that is 4 paragraphs long',
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expected_output="Full blog post that is 4 paragraphs long",
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agent=writer_agent,
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context=[research_ai_task, research_ops_task]
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)
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