social-app/.opencode/skills/crewai/llms-full.md

Agents

Source: https://docs.crewai.com/en/concepts/agents

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

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. 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.

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

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.
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.

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.

Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:

crew.kickoff(inputs={'topic': 'AI Agents'})

Here's an example of how to configure agents using YAML:

# src/latest_ai_development/config/agents.yaml
researcher:
  role: >
    {topic} Senior Data Researcher
  goal: >
    Uncover cutting-edge developments in {topic}
  backstory: >
    You're a seasoned researcher with a knack for uncovering the latest
    developments in {topic}. Known for your ability to find the most relevant
    information and present it in a clear and concise manner.

reporting_analyst:
  role: >
    {topic} Reporting Analyst
  goal: >
    Create detailed reports based on {topic} data analysis and research findings
  backstory: >
    You're a meticulous analyst with a keen eye for detail. You're known for
    your ability to turn complex data into clear and concise reports, making
    it easy for others to understand and act on the information you provide.

To use this YAML configuration in your code, create a crew class that inherits from CrewBase:

# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process
from crewai.project import CrewBase, agent, crew
from crewai_tools import SerperDevTool

@CrewBase
class LatestAiDevelopmentCrew():
  """LatestAiDevelopment crew"""

  agents_config = "config/agents.yaml"

  @agent
  def researcher(self) -> Agent:
    return Agent(
      config=self.agents_config['researcher'], # type: ignore[index]
      verbose=True,
      tools=[SerperDevTool()]
    )

  @agent
  def reporting_analyst(self) -> Agent:
    return Agent(
      config=self.agents_config['reporting_analyst'], # type: ignore[index]
      verbose=True
    )

The names you use in your YAML files (`agents.yaml`) should match the method names in your Python code.

Direct Code Definition

You can create agents directly in code by instantiating the Agent class. Here's a comprehensive example showing all available parameters:

from crewai import Agent
from crewai_tools import SerperDevTool

# Create an agent with all available parameters
agent = Agent(
    role="Senior Data Scientist",
    goal="Analyze and interpret complex datasets to provide actionable insights",
    backstory="With over 10 years of experience in data science and machine learning, "
              "you excel at finding patterns in complex datasets.",
    llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
    function_calling_llm=None,  # Optional: Separate LLM for tool calling
    verbose=False,  # Default: False
    allow_delegation=False,  # Default: False
    max_iter=20,  # Default: 20 iterations
    max_rpm=None,  # Optional: Rate limit for API calls
    max_execution_time=None,  # Optional: Maximum execution time in seconds
    max_retry_limit=2,  # Default: 2 retries on error
    allow_code_execution=False,  # Default: False
    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
    respect_context_window=True,  # Default: True
    use_system_prompt=True,  # Default: True
    multimodal=False,  # Default: False
    inject_date=False,  # Default: False
    date_format="%Y-%m-%d",  # Default: ISO format
    reasoning=False,  # Default: False
    max_reasoning_attempts=None,  # Default: None
    tools=[SerperDevTool()],  # Optional: List of tools
    knowledge_sources=None,  # Optional: List of knowledge sources
    embedder=None,  # Optional: Custom embedder configuration
    system_template=None,  # Optional: Custom system prompt template
    prompt_template=None,  # Optional: Custom prompt template
    response_template=None,  # Optional: Custom response template
    step_callback=None,  # Optional: Callback function for monitoring
)

Let's break down some key parameter combinations for common use cases:

Basic Research Agent

research_agent = Agent(
    role="Research Analyst",
    goal="Find and summarize information about specific topics",
    backstory="You are an experienced researcher with attention to detail",
    tools=[SerperDevTool()],
    verbose=True  # Enable logging for debugging
)

Code Development Agent

dev_agent = Agent(
    role="Senior Python Developer",
    goal="Write and debug Python code",
    backstory="Expert Python developer with 10 years of experience",
    allow_code_execution=True,
    code_execution_mode="safe",  # Uses Docker for safety
    max_execution_time=300,  # 5-minute timeout
    max_retry_limit=3  # More retries for complex code tasks
)

Long-Running Analysis Agent

analysis_agent = Agent(
    role="Data Analyst",
    goal="Perform deep analysis of large datasets",
    backstory="Specialized in big data analysis and pattern recognition",
    memory=True,
    respect_context_window=True,
    max_rpm=10,  # Limit API calls
    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
)

Custom Template Agent

custom_agent = Agent(
    role="Customer Service Representative",
    goal="Assist customers with their inquiries",
    backstory="Experienced in customer support with a focus on satisfaction",
    system_template="""<|start_header_id|>system<|end_header_id|>
                        {{ .System }}<|eot_id|>""",
    prompt_template="""<|start_header_id|>user<|end_header_id|>
                        {{ .Prompt }}<|eot_id|>""",
    response_template="""<|start_header_id|>assistant<|end_header_id|>
                        {{ .Response }}<|eot_id|>""",
)

Date-Aware Agent with Reasoning

strategic_agent = Agent(
    role="Market Analyst",
    goal="Track market movements with precise date references and strategic planning",
    backstory="Expert in time-sensitive financial analysis and strategic reporting",
    inject_date=True,  # Automatically inject current date into tasks
    date_format="%B %d, %Y",  # Format as "May 21, 2025"
    reasoning=True,  # Enable strategic planning
    max_reasoning_attempts=2,  # Limit planning iterations
    verbose=True
)

Reasoning Agent

reasoning_agent = Agent(
    role="Strategic Planner",
    goal="Analyze complex problems and create detailed execution plans",
    backstory="Expert strategic planner who methodically breaks down complex challenges",
    reasoning=True,  # Enable reasoning and planning
    max_reasoning_attempts=3,  # Limit reasoning attempts
    max_iter=30,  # Allow more iterations for complex planning
    verbose=True
)

Multimodal Agent

multimodal_agent = Agent(
    role="Visual Content Analyst",
    goal="Analyze and process both text and visual content",
    backstory="Specialized in multimodal analysis combining text and image understanding",
    multimodal=True,  # Enable multimodal capabilities
    verbose=True
)

Parameter Details

Critical Parameters

• role, goal, and backstory are required and shape the agent's behavior
• llm determines the language model used (default: OpenAI's GPT-4)

Memory and Context

• memory: Enable to maintain conversation history
• respect_context_window: Prevents token limit issues
• knowledge_sources: Add domain-specific knowledge bases

Execution Control

• max_iter: Maximum attempts before giving best answer
• max_execution_time: Timeout in seconds
• max_rpm: Rate limiting for API calls
• max_retry_limit: Retries on error

Code Execution

• allow_code_execution: Must be True to run code
• code_execution_mode:
  • "safe": Uses Docker (recommended for production)
  • "unsafe": Direct execution (use only in trusted environments)

This runs a default Docker image. If you want to configure the docker image, the checkout the Code Interpreter Tool in the tools section. Add the code interpreter tool as a tool in the agent as a tool parameter.

Advanced Features

• multimodal: Enable multimodal capabilities for processing text and visual content
• reasoning: Enable agent to reflect and create plans before executing tasks
• inject_date: Automatically inject current date into task descriptions

Templates

• system_template: Defines agent's core behavior
• prompt_template: Structures input format
• response_template: Formats agent responses

When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting. When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.

Agent Tools

Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:

• CrewAI Toolkit
• LangChain Tools

Here's how to add tools to an agent:

from crewai import Agent
from crewai_tools import SerperDevTool, WikipediaTools

# Create tools
search_tool = SerperDevTool()
wiki_tool = WikipediaTools()

# Add tools to agent
researcher = Agent(
    role="AI Technology Researcher",
    goal="Research the latest AI developments",
    tools=[search_tool, wiki_tool],
    verbose=True
)

Agent Memory and Context

Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks.

from crewai import Agent

analyst = Agent(
    role="Data Analyst",
    goal="Analyze and remember complex data patterns",
    memory=True,  # Enable memory
    verbose=True
)

When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.

Context Window Management

CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model's token limits. This powerful feature is controlled by the respect_context_window parameter.

How Context Window Management Works

When an agent's conversation history grows too large for the LLM's context window, CrewAI automatically detects this situation and can either:

1. Automatically summarize content (when respect_context_window=True)
2. Stop execution with an error (when respect_context_window=False)

Automatic Context Handling (respect_context_window=True)

This is the default and recommended setting for most use cases. When enabled, CrewAI will:

# Agent with automatic context management (default)
smart_agent = Agent(
    role="Research Analyst",
    goal="Analyze large documents and datasets",
    backstory="Expert at processing extensive information",
    respect_context_window=True,  # 🔑 Default: auto-handle context limits
    verbose=True
)

What happens when context limits are exceeded:

• ⚠️ Warning message: "Context length exceeded. Summarizing content to fit the model context window."
• 🔄 Automatic summarization: CrewAI intelligently summarizes the conversation history
• ✅ Continued execution: Task execution continues seamlessly with the summarized context
• 📝 Preserved information: Key information is retained while reducing token count

Strict Context Limits (respect_context_window=False)

When you need precise control and prefer execution to stop rather than lose any information:

# Agent with strict context limits
strict_agent = Agent(
    role="Legal Document Reviewer",
    goal="Provide precise legal analysis without information loss",
    backstory="Legal expert requiring complete context for accurate analysis",
    respect_context_window=False,  # ❌ Stop execution on context limit
    verbose=True
)

What happens when context limits are exceeded:

• ❌ Error message: "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."
• 🛑 Execution stops: Task execution halts immediately
• 🔧 Manual intervention required: You need to modify your approach

Choosing the Right Setting

Use respect_context_window=True (Default) when:

• Processing large documents that might exceed context limits
• Long-running conversations where some summarization is acceptable
• Research tasks where general context is more important than exact details
• Prototyping and development where you want robust execution

# Perfect for document processing
document_processor = Agent(
    role="Document Analyst",
    goal="Extract insights from large research papers",
    backstory="Expert at analyzing extensive documentation",
    respect_context_window=True,  # Handle large documents gracefully
    max_iter=50,  # Allow more iterations for complex analysis
    verbose=True
)

Use respect_context_window=False when:

• Precision is critical and information loss is unacceptable
• Legal or medical tasks requiring complete context
• Code review where missing details could introduce bugs
• Financial analysis where accuracy is paramount

# Perfect for precision tasks
precision_agent = Agent(
    role="Code Security Auditor",
    goal="Identify security vulnerabilities in code",
    backstory="Security expert requiring complete code context",
    respect_context_window=False,  # Prefer failure over incomplete analysis
    max_retry_limit=1,  # Fail fast on context issues
    verbose=True
)

Alternative Approaches for Large Data

When dealing with very large datasets, consider these strategies:

1. Use RAG Tools

from crewai_tools import RagTool

# Create RAG tool for large document processing
rag_tool = RagTool()

rag_agent = Agent(
    role="Research Assistant",
    goal="Query large knowledge bases efficiently",
    backstory="Expert at using RAG tools for information retrieval",
    tools=[rag_tool],  # Use RAG instead of large context windows
    respect_context_window=True,
    verbose=True
)

2. Use Knowledge Sources

# Use knowledge sources instead of large prompts
knowledge_agent = Agent(
    role="Knowledge Expert",
    goal="Answer questions using curated knowledge",
    backstory="Expert at leveraging structured knowledge sources",
    knowledge_sources=[your_knowledge_sources],  # Pre-processed knowledge
    respect_context_window=True,
    verbose=True
)

Context Window Best Practices

1. Monitor Context Usage: Enable verbose=True to see context management in action
2. Design for Efficiency: Structure tasks to minimize context accumulation
3. Use Appropriate Models: Choose LLMs with context windows suitable for your tasks
4. Test Both Settings: Try both True and False to see which works better for your use case
5. Combine with RAG: Use RAG tools for very large datasets instead of relying solely on context windows

Troubleshooting Context Issues

If you're getting context limit errors:

# Quick fix: Enable automatic handling
agent.respect_context_window = True

# Better solution: Use RAG tools for large data
from crewai_tools import RagTool
agent.tools = [RagTool()]

# Alternative: Break tasks into smaller pieces
# Or use knowledge sources instead of large prompts

If automatic summarization loses important information:

# Disable auto-summarization and use RAG instead
agent = Agent(
    role="Detailed Analyst",
    goal="Maintain complete information accuracy",
    backstory="Expert requiring full context",
    respect_context_window=False,  # No summarization
    tools=[RagTool()],  # Use RAG for large data
    verbose=True
)

The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!

Direct Agent Interaction with kickoff()

Agents can be used directly without going through a task or crew workflow using the kickoff() method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.

How kickoff() Works

The kickoff() method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).

from crewai import Agent
from crewai_tools import SerperDevTool

# Create an agent
researcher = Agent(
    role="AI Technology Researcher",
    goal="Research the latest AI developments",
    tools=[SerperDevTool()],
    verbose=True
)

# Use kickoff() to interact directly with the agent
result = researcher.kickoff("What are the latest developments in language models?")

# Access the raw response
print(result.raw)

Parameters and Return Values

Parameter	Type	Description
messages	Union[str, List[Dict[str, str]]]	Either a string query or a list of message dictionaries with role/content
response_format	Optional[Type[Any]]	Optional Pydantic model for structured output

The method returns a LiteAgentOutput object with the following properties:

• raw: String containing the raw output text
• pydantic: Parsed Pydantic model (if a response_format was provided)
• agent_role: Role of the agent that produced the output
• usage_metrics: Token usage metrics for the execution

Structured Output

You can get structured output by providing a Pydantic model as the response_format:

from pydantic import BaseModel
from typing import List

class ResearchFindings(BaseModel):
    main_points: List[str]
    key_technologies: List[str]
    future_predictions: str

# Get structured output
result = researcher.kickoff(
    "Summarize the latest developments in AI for 2025",
    response_format=ResearchFindings
)

# Access structured data
print(result.pydantic.main_points)
print(result.pydantic.future_predictions)

Multiple Messages

You can also provide a conversation history as a list of message dictionaries:

messages = [
    {"role": "user", "content": "I need information about large language models"},
    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
    {"role": "user", "content": "What are the latest developments in 2025?"}
]

result = researcher.kickoff(messages)

Async Support

An asynchronous version is available via kickoff_async() with the same parameters:

import asyncio

async def main():
    result = await researcher.kickoff_async("What are the latest developments in AI?")
    print(result.raw)

asyncio.run(main())

The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler execution flow while preserving all of the agent's configuration (role, goal, backstory, tools, etc.).

Important Considerations and Best Practices

Security and Code Execution

• When using allow_code_execution, be cautious with user input and always validate it
• Use code_execution_mode: "safe" (Docker) in production environments
• Consider setting appropriate max_execution_time limits to prevent infinite loops

Performance Optimization

• Use respect_context_window: true to prevent token limit issues
• Set appropriate max_rpm to avoid rate limiting
• Enable cache: true to improve performance for repetitive tasks
• Adjust max_iter and max_retry_limit based on task complexity

Memory and Context Management

• Leverage knowledge_sources for domain-specific information
• Configure embedder when using custom embedding models
• Use custom templates (system_template, prompt_template, response_template) for fine-grained control over agent behavior

Advanced Features

• Enable reasoning: true for agents that need to plan and reflect before executing complex tasks
• Set appropriate max_reasoning_attempts to control planning iterations (None for unlimited attempts)
• Use inject_date: true to provide agents with current date awareness for time-sensitive tasks
• Customize the date format with date_format using standard Python datetime format codes
• Enable multimodal: true for agents that need to process both text and visual content

Agent Collaboration

• Enable allow_delegation: true when agents need to work together
• Use step_callback to monitor and log agent interactions
• Consider using different LLMs for different purposes:
  • Main llm for complex reasoning
  • function_calling_llm for efficient tool usage

Date Awareness and Reasoning

• Use inject_date: true to provide agents with current date awareness for time-sensitive tasks
• Customize the date format with date_format using standard Python datetime format codes
• Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
• Invalid date formats will be logged as warnings and will not modify the task description
• Enable reasoning: true for complex tasks that benefit from upfront planning and reflection

Model Compatibility

• Set use_system_prompt: false for older models that don't support system messages
• Ensure your chosen llm supports the features you need (like function calling)

Troubleshooting Common Issues

1. Rate Limiting: If you're hitting API rate limits:

   • Implement appropriate max_rpm
   • Use caching for repetitive operations
   • Consider batching requests

2. Context Window Errors: If you're exceeding context limits:

   • Enable respect_context_window
   • Use more efficient prompts
   • Clear agent memory periodically

3. Code Execution Issues: If code execution fails:

   • Verify Docker is installed for safe mode
   • Check execution permissions
   • Review code sandbox settings

4. Memory Issues: If agent responses seem inconsistent:

   • Check knowledge source configuration
   • Review conversation history management

Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly.

CLI

Source: https://docs.crewai.com/en/concepts/cli

Learn how to use the CrewAI CLI to interact with CrewAI.

Since release 0.140.0, CrewAI AMP started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.

Overview

The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.

Installation

To use the CrewAI CLI, make sure you have CrewAI installed:

pip install crewai

Basic Usage

The basic structure of a CrewAI CLI command is:

crewai [COMMAND] [OPTIONS] [ARGUMENTS]

Available Commands

1. Create

Create a new crew or flow.

crewai create [OPTIONS] TYPE NAME

• TYPE: Choose between "crew" or "flow"
• NAME: Name of the crew or flow

Example:

crewai create crew my_new_crew
crewai create flow my_new_flow

2. Version

Show the installed version of CrewAI.

crewai version [OPTIONS]

• --tools: (Optional) Show the installed version of CrewAI tools

Example:

crewai version
crewai version --tools

3. Train

Train the crew for a specified number of iterations.

crewai train [OPTIONS]

• -n, --n_iterations INTEGER: Number of iterations to train the crew (default: 5)
• -f, --filename TEXT: Path to a custom file for training (default: "trained_agents_data.pkl")

Example:

crewai train -n 10 -f my_training_data.pkl

4. Replay

Replay the crew execution from a specific task.

crewai replay [OPTIONS]

• -t, --task_id TEXT: Replay the crew from this task ID, including all subsequent tasks

Example:

crewai replay -t task_123456

5. Log-tasks-outputs

Retrieve your latest crew.kickoff() task outputs.

crewai log-tasks-outputs

6. Reset-memories

Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).

crewai reset-memories [OPTIONS]

• -l, --long: Reset LONG TERM memory
• -s, --short: Reset SHORT TERM memory
• -e, --entities: Reset ENTITIES memory
• -k, --kickoff-outputs: Reset LATEST KICKOFF TASK OUTPUTS
• -kn, --knowledge: Reset KNOWLEDGE storage
• -akn, --agent-knowledge: Reset AGENT KNOWLEDGE storage
• -a, --all: Reset ALL memories

Example:

crewai reset-memories --long --short
crewai reset-memories --all

7. Test

Test the crew and evaluate the results.

crewai test [OPTIONS]

• -n, --n_iterations INTEGER: Number of iterations to test the crew (default: 3)
• -m, --model TEXT: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")

Example:

crewai test -n 5 -m gpt-3.5-turbo

8. Run

Run the crew or flow.

crewai run

Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows. Make sure to run these commands from the directory where your CrewAI project is set up. Some commands may require additional configuration or setup within your project structure.

9. Chat

Starting in version 0.98.0, when you run the crewai chat command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.

After receiving the results, you can continue interacting with the assistant for further instructions or questions.

crewai chat

Ensure you execute these commands from your CrewAI project's root directory. IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.

@crew
def crew(self) -> Crew:
    return Crew(
        agents=self.agents,
        tasks=self.tasks,
        process=Process.sequential,
        verbose=True,
        chat_llm="gpt-4o",  # LLM for chat orchestration
    )

10. Deploy

Deploy the crew or flow to CrewAI AMP.

• Authentication: You need to be authenticated to deploy to CrewAI AMP. You can login or create an account with:

  crewai login
  

• Create a deployment: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.

  crewai deploy create
  

  • Reads your local project configuration.
  • Prompts you to confirm the environment variables (like OPENAI_API_KEY, SERPER_API_KEY) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a .env file) before running this.

11. Organization Management

Manage your CrewAI AMP organizations.

crewai org [COMMAND] [OPTIONS]

Commands:

• list: List all organizations you belong to

crewai org list

• current: Display your currently active organization

crewai org current

• switch: Switch to a specific organization

crewai org switch <organization_id>

You must be authenticated to CrewAI AMP to use these organization management commands.

• Create a deployment (continued):

  • Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).

• Deploy the Crew: Once you are authenticated, you can deploy your crew or flow to CrewAI AMP.

  crewai deploy push
  

  • Initiates the deployment process on the CrewAI AMP platform.
  • Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).

• Deployment Status: You can check the status of your deployment with:

  crewai deploy status
  

  This fetches the latest deployment status of your most recent deployment attempt (e.g., Building Images for Crew, Deploy Enqueued, Online).

• Deployment Logs: You can check the logs of your deployment with:

  crewai deploy logs
  

  This streams the deployment logs to your terminal.

• List deployments: You can list all your deployments with:

  crewai deploy list
  

  This lists all your deployments.

• Delete a deployment: You can delete a deployment with:

  crewai deploy remove
  

  This deletes the deployment from the CrewAI AMP platform.

• Help Command: You can get help with the CLI with:

  crewai deploy --help
  

  This shows the help message for the CrewAI Deploy CLI.

Watch this video tutorial for a step-by-step demonstration of deploying your crew to CrewAI AMP using the CLI.