AI SummaryA comprehensive guide for creating and managing autonomous agents within the CrewAI framework, enabling developers to build specialized AI team members with defined roles, goals, and collaborative capabilities. Ideal for developers building multi-agent AI systems who need clear patterns for agent design and configuration.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to set up the "src/latest_ai_development/config/agents.yaml" agent in my project. Please run this command in my terminal: # Add AGENTS.md to your project root curl --retry 3 --retry-delay 2 --retry-all-errors -o AGENTS.md "https://raw.githubusercontent.com/opahopa/crewai-factory-crew/main/docs_crewai/concepts/agents.mdx" Then explain what the agent does and how to invoke it.
Description
Detailed guide on creating and managing agents within the CrewAI framework.
Overview of an Agent
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>
Agent Attributes
| 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. | | Memory _(optional)_ | memory | bool | Whether the agent should maintain memory of interactions. Default is True. | | 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'. | | Embedder Config _(optional)_ | embedder_config | 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. |
Creating Agents
There are two ways to create agents in CrewAI: using YAML configuration (recommended) or defining them directly in code.
YAML Configuration (Recommended)
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
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