Which Task and Agent Attributes Can Be Put in the YAML File?

CrewAI Community Support
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Update - LLMs defined in a YAML file

Hi everyone, hope you’re all doing well.

I saw that the discussion continued, so I decided to draft a poor man’s solution that allows us to define our LLMs in a YAML file as well, making things more organized.

Before the code, some considerations:

  • The agents.yaml file defines agents that can receive these attributes.
  • The tasks.yaml file defines tasks that can receive these attributes.
  • The llms.yaml file defines LLMs that can receive the attributes of a crewai.LLM, for example: model, timeout, temperature, top_p, n, stop, max_completion_tokens, max_tokens, presence_penalty, frequency_penalty, logit_bias, seed, logprobs, top_logprobs, base_url, api_base, api_version, api_key, reasoning_effort.
  • The llm attribute of the agents defined in agents.yaml must have the same name as a method wrapped with the @llm decorator.
  • The same applies to the tools attribute of the agents defined in agents.yaml, which must reference the name of a method wrapped with the @tool decorator.
  • The proposed llm_from_yaml method returns a dict that must be unwrapped with a double asterisk.
  • The code uses Gemini, so you should adapt it to your preferred LLM.

Enough talk, let’s code. First, this is the directory structure:

crazy_crew
├── config
│   ├── agents.yaml
│   ├── llms.yaml
│   └── tasks.yaml
└── crew.py

agents.yaml file:

crazy_scientist:
  role: >
    Eccentric Experimental Physicist
  goal: >
    Unravel the mysteries of the universe, no matter how bizarre!
  backstory: >
    A hyper-enthusiastic, slightly unhinged physicist with a penchant for
    wild theories and dramatic pronouncements. Speaks rapidly, often
    trailing off into excited muttering. Believes in the most outlandish
    possibilities. Prone to sudden, loud exclamations!
  verbose: true
  llm: gemini_high_temp_llm
  tools:
    - my_tool_1

tasks.yaml file:

crazy_task:
  description: >
    Answer the following question with a single paragraph,
    and keep it under 400 characters: {user_question}
  expected_output: >
    A single, short, and eccentric paragraph.
  agent: crazy_scientist

llms.yaml file:

gemini_high_temp_llm:
  model: gemini/gemini-2.0-flash
  temperature: 1.2
  max_tokens: 512

gemini_low_temp_llm:
  model: gemini/gemini-2.0-flash
  temperature: 0.2
  max_tokens: 512

Finally, the crew.py file putting it all together:

from typing import Dict, Any
from pathlib import Path
import logging
import os
import yaml

from crewai import Agent, Crew, Task, Process, LLM
from crewai.project import CrewBase, agent, task, crew, tool, llm
from crewai.tools import BaseTool
from crewai_tools import DirectoryReadTool


os.environ['GEMINI_API_KEY'] = 'YOUR_KEY_NOT_MINE'


@CrewBase
class CrazyCrew:
    """
    CrazyCrew class for AI agent configuration and task execution.

    This class configures and manages the AI agents, tasks, and LLMs
    using YAML configurations to provide an organized and flexible framework.
    """

    agents_config = 'config/agents.yaml'
    tasks_config = 'config/tasks.yaml'
    llms_config = 'config/llms.yaml'

    @agent
    def crazy_scientist(self) -> Agent:
        return Agent(
            config=self.agents_config['crazy_scientist'],
        )

    @task
    def crazy_task(self) -> Task:
        return Task(
            config=self.tasks_config['crazy_task'],
        )

    @llm
    def gemini_high_temp_llm(self) -> LLM:
        return LLM(
            **self.llm_from_yaml('gemini_high_temp_llm')
        )
    
    @llm
    def gemini_low_temp_llm(self) -> LLM:
        return LLM(
            **self.llm_from_yaml('gemini_low_temp_llm')
        )

    @tool
    def my_tool_1(self) -> BaseTool:
        return DirectoryReadTool(directory='./')

    @crew
    def crew(self) -> Crew:
        return Crew(
            agents=self.agents,
            tasks=self.tasks,
            process=Process.sequential,
            verbose=True,
        )

    def llm_from_yaml(self, llm_name: str) -> Dict[str, Any]:
        """
        Load LLM configuration from YAML file.

        This method reads the LLM configuration from a YAML file and returns
        the configuration for the specified LLM name.

        Args:
            llm_name: Name of the LLM configuration to load.

        Returns:
            Dictionary containing the LLM configuration parameters.

        Raises:
            FileNotFoundError: If the LLMs config file doesn't exist.
            KeyError: If the specified LLM name is not found in the config.
        """
        base_directory = Path(__file__).parent
        original_llms_config_path = getattr(
            self, 'llms_config', 'config/llms.yaml'
        )
        llms_config_path = base_directory / original_llms_config_path

        try:
            with open(llms_config_path, 'r', encoding='utf-8') as file:
                llms_config: Dict[str, Dict[str, Any]] = yaml.safe_load(file)
        except FileNotFoundError:
            logging.error(f"LLMs config file not found at {llms_config_path}.")
            raise

        try:
            llm_config: Dict[str, Any] = llms_config[llm_name]
        except KeyError:
            logging.error(
                f"LLM config for '{llm_name}' not found in {llms_config_path}."
            )
            raise

        return llm_config


def main() -> None:
    """
    Main function to initialize and run the CrazyCrew.

    This function creates an instance of CrazyCrew, assembles the crew,
    and kicks off the process with a specific input question.
    """
    crazy_crew = CrazyCrew().crew()
    crew_output = crazy_crew.kickoff(
        inputs={"user_question": "Can I run faster than light?"}
    )
    print(f'\n[🧠 ANSWER] {crew_output.raw}\n')


if __name__ == "__main__":
    main()

Hope this helps clarify the additional questions that came up, as well as contribute to even more organized work!

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