src/latest_ai_development/config/agents.yaml

In the CrewAI framework, an Agent is an autonomous unit that can: • Perform specific tasks • Make decisions based on its role and goal • Use tools to accomplish objectives • Communicate and collaborate with other agents • Maintain memory of interactions • Delegate tasks when allowed <Tip> Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a Researcher agent might excel at gathering and analyzing information, while a Writer agent might be better at creating content. </Tip> <Note type="info" title="Enterprise Enhancement: Visual Agent Builder"> CrewAI AMP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time. !Visual Agent Builder Screenshot The Visual Agent Builder enables: • Intuitive agent configuration with form-based interfaces • Real-time testing and validation • Template library with pre-configured agent types • Easy customization of agent attributes and behaviors </Note>

| Attribute | Parameter | Type | Description | | :-------------------------------------- | :----------------------- | :------------------------------------ | :------------------------------------------------------------------------------------------------------- | | Role | role | str | Defines the agent's function and expertise within the crew. | | Goal | goal | str | The individual objective that guides the agent's decision-making. | | Backstory | backstory | str | Provides context and personality to the agent, enriching interactions. | | LLM _(optional)_ | llm | Union[str, LLM, Any] | Language model that powers the agent. Defaults to the model specified in OPENAI_MODEL_NAME or "gpt-4". | | Tools _(optional)_ | tools | List[BaseTool] | Capabilities or functions available to the agent. Defaults to an empty list. | | Function Calling LLM _(optional)_ | function_calling_llm | Optional[Any] | Language model for tool calling, overrides crew's LLM if specified. | | Max Iterations _(optional)_ | max_iter | int | Maximum iterations before the agent must provide its best answer. Default is 20. | | Max RPM _(optional)_ | max_rpm | Optional[int] | Maximum requests per minute to avoid rate limits. | | Max Execution Time _(optional)_ | max_execution_time | Optional[int] | Maximum time (in seconds) for task execution. | | Verbose _(optional)_ | verbose | bool | Enable detailed execution logs for debugging. Default is False. | | Allow Delegation _(optional)_ | allow_delegation | bool | Allow the agent to delegate tasks to other agents. Default is False. | | Step Callback _(optional)_ | step_callback | Optional[Any] | Function called after each agent step, overrides crew callback. | | Cache _(optional)_ | cache | bool | Enable caching for tool usage. Default is True. | | System Template _(optional)_ | system_template | Optional[str] | Custom system prompt template for the agent. | | Prompt Template _(optional)_ | prompt_template | Optional[str] | Custom prompt template for the agent. | | Response Template _(optional)_ | response_template | Optional[str] | Custom response template for the agent. | | Allow Code Execution _(optional)_ | allow_code_execution | Optional[bool] | Enable code execution for the agent. Default is False. | | Max Retry Limit _(optional)_ | max_retry_limit | int | Maximum number of retries when an error occurs. Default is 2. | | Respect Context Window _(optional)_ | respect_context_window | bool | Keep messages under context window size by summarizing. Default is True. | | Code Execution Mode _(optional)_ | code_execution_mode | Literal["safe", "unsafe"] | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'. | | Multimodal _(optional)_ | multimodal | bool | Whether the agent supports multimodal capabilities. Default is False. | | Inject Date _(optional)_ | inject_date | bool | Whether to automatically inject the current date into tasks. Default is False. | | Date Format _(optional)_ | date_format | str | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format). | | Reasoning _(optional)_ | reasoning | bool | Whether the agent should reflect and create a plan before executing a task. Default is False. | | Max Reasoning Attempts _(optional)_ | max_reasoning_attempts | Optional[int] | Maximum number of reasoning attempts before executing the task. If None, will try until ready. | | Embedder _(optional)_ | embedder | Optional[Dict[str, Any]] | Configuration for the embedder used by the agent. | | Knowledge Sources _(optional)_ | knowledge_sources | Optional[List[BaseKnowledgeSource]] | Knowledge sources available to the agent. | | Use System Prompt _(optional)_ | use_system_prompt | Optional[bool] | Whether to use system prompt (for o1 model support). Default is True. |

There are two ways to create agents in CrewAI: using YAML configuration (recommended) or defining them directly in code.

Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects. After creating your CrewAI project as outlined in the Installation section, navigate to the src/latest_ai_development/config/agents.yaml file and modify the template to match your requirements. <Note> Variables in your YAML files (like {topic}) will be replaced with values from your inputs when running the crew: `python Code crew.kickoff(inputs={'topic': 'AI Agents'}) ` </Note> Here's an example of how to configure agents using YAML: `yaml agents.yaml