hey there,
I cannot for the life of me find the actual documentation for the classes. They are referenced in the “docs” - i.e. For a complete list of parameters and their descriptions, refer to the LLM class documentation.
however, I cannot actually find the docs anywhere. can anyone point me to them?
I don’t really think there is any documentation for the classes yet. In any case, I just look at https://github.com/crewAIInc/crewAI/tree/main/src/crewai:
class Agent(BaseAgent):
"""Represents an agent in a system.
Each agent has a role, a goal, a backstory, and an optional language model (llm).
The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
Attributes:
agent_executor: An instance of the CrewAgentExecutor class.
role: The role of the agent.
goal: The objective of the agent.
backstory: The backstory of the agent.
knowledge: The knowledge base of the agent.
config: Dict representation of agent configuration.
llm: The language model that will run the agent.
function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
max_iter: Maximum number of iterations for an agent to execute a task.
max_rpm: Maximum number of requests per minute for the agent execution to be respected.
verbose: Whether the agent execution should be in verbose mode.
allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
tools: Tools at agents disposal
step_callback: Callback to be executed after each step of the agent execution.
knowledge_sources: Knowledge sources for the agent.
embedder: Embedder configuration for the agent.
"""
class Task(BaseModel):
"""Class that represents a task to be executed.
Each task must have a description, an expected output and an agent responsible for execution.
Attributes:
agent: Agent responsible for task execution. Represents entity performing task.
async_execution: Boolean flag indicating asynchronous task execution.
callback: Function/object executed post task completion for additional actions.
config: Dictionary containing task-specific configuration parameters.
context: List of Task instances providing task context or input data.
description: Descriptive text detailing task's purpose and execution.
expected_output: Clear definition of expected task outcome.
output_file: File path for storing task output.
output_json: Pydantic model for structuring JSON output.
output_pydantic: Pydantic model for task output.
security_config: Security configuration including fingerprinting.
tools: List of tools/resources limited for task execution.
"""
class Crew(BaseModel):
"""
Represents a group of agents, defining how they should collaborate and the tasks they should perform.
Attributes:
tasks: List of tasks assigned to the crew.
agents: List of agents part of this crew.
manager_llm: The language model that will run manager agent.
manager_agent: Custom agent that will be used as manager.
memory: Whether the crew should use memory to store memories of it's execution.
memory_config: Configuration for the memory to be used for the crew.
cache: Whether the crew should use a cache to store the results of the tools execution.
function_calling_llm: The language model that will run the tool calling for all the agents.
process: The process flow that the crew will follow (e.g., sequential, hierarchical).
verbose: Indicates the verbosity level for logging during execution.
config: Configuration settings for the crew.
max_rpm: Maximum number of requests per minute for the crew execution to be respected.
prompt_file: Path to the prompt json file to be used for the crew.
id: A unique identifier for the crew instance.
task_callback: Callback to be executed after each task for every agents execution.
step_callback: Callback to be executed after each step for every agents execution.
share_crew: Whether you want to share the complete crew information and execution with crewAI to make the library better, and allow us to train models.
planning: Plan the crew execution and add the plan to the crew.
chat_llm: The language model used for orchestrating chat interactions with the crew.
security_config: Security configuration for the crew, including fingerprinting.
"""
class LLM:
def __init__(
self,
model: str,
timeout: Optional[Union[float, int]] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
n: Optional[int] = None,
stop: Optional[Union[str, List[str]]] = None,
max_completion_tokens: Optional[int] = None,
max_tokens: Optional[int] = None,
presence_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
logit_bias: Optional[Dict[int, float]] = None,
response_format: Optional[Type[BaseModel]] = None,
seed: Optional[int] = None,
logprobs: Optional[int] = None,
top_logprobs: Optional[int] = None,
base_url: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
api_key: Optional[str] = None,
callbacks: List[Any] = [],
reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
stream: bool = False,
**kwargs,
):