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---
title: Hallucination Guardrail
description: "Prevent and detect AI hallucinations in your CrewAI tasks"
icon: "shield-check"
---
## Overview
The Hallucination Guardrail is an enterprise feature that validates AI-generated content to ensure it's grounded in facts and doesn't contain hallucinations. It analyzes task outputs against reference context and provides detailed feedback when potentially hallucinated content is detected.
## What are Hallucinations?
AI hallucinations occur when language models generate content that appears plausible but is factually incorrect or not supported by the provided context. The Hallucination Guardrail helps prevent these issues by:
- Comparing outputs against reference context
- Evaluating faithfulness to source material
- Providing detailed feedback on problematic content
- Supporting custom thresholds for validation strictness
## Basic Usage
### Setting Up the Guardrail
```python
from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
from crewai import LLM
# Basic usage - will use task's expected_output as context
guardrail = HallucinationGuardrail(
llm=LLM(model="gpt-4o-mini")
)
# With explicit reference context
context_guardrail = HallucinationGuardrail(
context="AI helps with various tasks including analysis and generation.",
llm=LLM(model="gpt-4o-mini")
)
```
### Adding to Tasks
```python
from crewai import Task
# Create your task with the guardrail
task = Task(
description="Write a summary about AI capabilities",
expected_output="A factual summary based on the provided context",
agent=my_agent,
guardrail=guardrail # Add the guardrail to validate output
)
```
## Advanced Configuration
### Custom Threshold Validation
For stricter validation, you can set a custom faithfulness threshold (0-10 scale):
```python
# Strict guardrail requiring high faithfulness score
strict_guardrail = HallucinationGuardrail(
context="Quantum computing uses qubits that exist in superposition states.",
llm=LLM(model="gpt-4o-mini"),
threshold=8.0 # Requires score >= 8 to pass validation
)
```
### Including Tool Response Context
When your task uses tools, you can include tool responses for more accurate validation:
```python
# Guardrail with tool response context
weather_guardrail = HallucinationGuardrail(
context="Current weather information for the requested location",
llm=LLM(model="gpt-4o-mini"),
tool_response="Weather API returned: Temperature 22°C, Humidity 65%, Clear skies"
)
```
## How It Works
### Validation Process
1. **Context Analysis**: The guardrail compares task output against the provided reference context
2. **Faithfulness Scoring**: Uses an internal evaluator to assign a faithfulness score (0-10)
3. **Verdict Determination**: Determines if content is faithful or contains hallucinations
4. **Threshold Checking**: If a custom threshold is set, validates against that score
5. **Feedback Generation**: Provides detailed reasons when validation fails
### Validation Logic
- **Default Mode**: Uses verdict-based validation (FAITHFUL vs HALLUCINATED)
- **Threshold Mode**: Requires faithfulness score to meet or exceed the specified threshold
- **Error Handling**: Gracefully handles evaluation errors and provides informative feedback
## Guardrail Results
The guardrail returns structured results indicating validation status:
```python
# Example of guardrail result structure
{
"valid": False,
"feedback": "Content appears to be hallucinated (score: 4.2/10, verdict: HALLUCINATED). The output contains information not supported by the provided context."
}
```
### Result Properties
- **valid**: Boolean indicating whether the output passed validation
- **feedback**: Detailed explanation when validation fails, including:
- Faithfulness score
- Verdict classification
- Specific reasons for failure
## Integration with Task System
### Automatic Validation
When a guardrail is added to a task, it automatically validates the output before the task is marked as complete:
```python
# Task output validation flow
task_output = agent.execute_task(task)
validation_result = guardrail(task_output)
if validation_result.valid:
# Task completes successfully
return task_output
else:
# Task fails with validation feedback
raise ValidationError(validation_result.feedback)
```
### Event Tracking
The guardrail integrates with CrewAI's event system to provide observability:
- **Validation Started**: When guardrail evaluation begins
- **Validation Completed**: When evaluation finishes with results
- **Validation Failed**: When technical errors occur during evaluation
## Best Practices
### Context Guidelines
<Steps>
<Step title="Provide Comprehensive Context">
Include all relevant factual information that the AI should base its output on:
```python
context = """
Company XYZ was founded in 2020 and specializes in renewable energy solutions.
They have 150 employees and generated $50M revenue in 2023.
Their main products include solar panels and wind turbines.
"""
```
</Step>
<Step title="Keep Context Relevant">
Only include information directly related to the task to avoid confusion:
```python
# Good: Focused context
context = "The current weather in New York is 18°C with light rain."
# Avoid: Unrelated information
context = "The weather is 18°C. The city has 8 million people. Traffic is heavy."
```
</Step>
<Step title="Update Context Regularly">
Ensure your reference context reflects current, accurate information.
</Step>
</Steps>
### Threshold Selection
<Steps>
<Step title="Start with Default Validation">
Begin without custom thresholds to understand baseline performance.
</Step>
<Step title="Adjust Based on Requirements">
- **High-stakes content**: Use threshold 8-10 for maximum accuracy
- **General content**: Use threshold 6-7 for balanced validation
- **Creative content**: Use threshold 4-5 or default verdict-based validation
</Step>
<Step title="Monitor and Iterate">
Track validation results and adjust thresholds based on false positives/negatives.
</Step>
</Steps>
## Performance Considerations
### Impact on Execution Time
- **Validation Overhead**: Each guardrail adds ~1-3 seconds per task
- **LLM Efficiency**: Choose efficient models for evaluation (e.g., gpt-4o-mini)
### Cost Optimization
- **Model Selection**: Use smaller, efficient models for guardrail evaluation
- **Context Size**: Keep reference context concise but comprehensive
- **Caching**: Consider caching validation results for repeated content
## Troubleshooting
<Accordion title="Validation Always Fails">
**Possible Causes:**
- Context is too restrictive or unrelated to task output
- Threshold is set too high for the content type
- Reference context contains outdated information
**Solutions:**
- Review and update context to match task requirements
- Lower threshold or use default verdict-based validation
- Ensure context is current and accurate
</Accordion>
<Accordion title="False Positives (Valid Content Marked Invalid)">
**Possible Causes:**
- Threshold too high for creative or interpretive tasks
- Context doesn't cover all valid aspects of the output
- Evaluation model being overly conservative
**Solutions:**
- Lower threshold or use default validation
- Expand context to include broader acceptable content
- Test with different evaluation models
</Accordion>
<Accordion title="Evaluation Errors">
**Possible Causes:**
- Network connectivity issues
- LLM model unavailable or rate limited
- Malformed task output or context
**Solutions:**
- Check network connectivity and LLM service status
- Implement retry logic for transient failures
- Validate task output format before guardrail evaluation
</Accordion>
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with hallucination guardrail configuration or troubleshooting.
</Card>

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---
title: Integrations
description: "Connected applications for your agents to take actions."
icon: "plug"
---
## Overview
Enable your agents to authenticate with any OAuth enabled provider and take actions. From Salesforce and HubSpot to Google and GitHub, we've got you covered with 16+ integrated services.
<Frame>
![Integrations](/images/enterprise/crew_connectors.png)
</Frame>
## Supported Integrations
### **Communication & Collaboration**
- **Gmail** - Manage emails and drafts
- **Slack** - Workspace notifications and alerts
- **Microsoft** - Office 365 and Teams integration
### **Project Management**
- **Jira** - Issue tracking and project management
- **ClickUp** - Task and productivity management
- **Asana** - Team task and project coordination
- **Notion** - Page and database management
- **Linear** - Software project and bug tracking
- **GitHub** - Repository and issue management
### **Customer Relationship Management**
- **Salesforce** - CRM account and opportunity management
- **HubSpot** - Sales pipeline and contact management
- **Zendesk** - Customer support ticket management
### **Business & Finance**
- **Stripe** - Payment processing and customer management
- **Shopify** - E-commerce store and product management
### **Productivity & Storage**
- **Google Sheets** - Spreadsheet data synchronization
- **Google Calendar** - Event and schedule management
- **Box** - File storage and document management
and more to come!
## Prerequisites
Before using Authentication Integrations, ensure you have:
- A [CrewAI Enterprise](https://app.crewai.com) account. You can get started with a free trial.
## Setting Up Integrations
### 1. Connect Your Account
1. Navigate to [CrewAI Enterprise](https://app.crewai.com)
2. Go to **Integrations** tab - https://app.crewai.com/crewai_plus/connectors
3. Click **Connect** on your desired service from the Authentication Integrations section
4. Complete the OAuth authentication flow
5. Grant necessary permissions for your use case
6. Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) account page - https://app.crewai.com/crewai_plus/settings/account
<Frame>
![Integrations](/images/enterprise/enterprise_action_auth_token.png)
</Frame>
### 2. Install Integration Tools
All you need is the latest version of `crewai-tools` package.
```bash
uv add crewai-tools
```
## Usage Examples
### Basic Usage
<Tip>
All the services you are authenticated into will be available as tools. So all you need to do is add the `CrewaiEnterpriseTools` to your agent and you are good to go.
</Tip>
```python
from crewai import Agent, Task, Crew
from crewai_tools import CrewaiEnterpriseTools
# Get enterprise tools (Gmail tool will be included)
enterprise_tools = CrewaiEnterpriseTools(
enterprise_token="your_enterprise_token"
)
# print the tools
print(enterprise_tools)
# Create an agent with Gmail capabilities
email_agent = Agent(
role="Email Manager",
goal="Manage and organize email communications",
backstory="An AI assistant specialized in email management and communication.",
tools=[enterprise_tools]
)
# Task to send an email
email_task = Task(
description="Draft and send a follow-up email to john@example.com about the project update",
agent=email_agent,
expected_output="Confirmation that email was sent successfully"
)
# Run the task
crew = Crew(
agents=[email_agent],
tasks=[email_task]
)
# Run the crew
crew.kickoff()
```
### Filtering Tools
```python
from crewai_tools import CrewaiEnterpriseTools
enterprise_tools = CrewaiEnterpriseTools(
actions_list=["gmail_find_email"] # only gmail_find_email tool will be available
)
gmail_tool = enterprise_tools[0]
gmail_agent = Agent(
role="Gmail Manager",
goal="Manage gmail communications and notifications",
backstory="An AI assistant that helps coordinate gmail communications.",
tools=[gmail_tool]
)
notification_task = Task(
description="Find the email from john@example.com",
agent=gmail_agent,
expected_output="Email found from john@example.com"
)
# Run the task
crew = Crew(
agents=[slack_agent],
tasks=[notification_task]
)
```
## Best Practices
### Security
- **Principle of Least Privilege**: Only grant the minimum permissions required for your agents' tasks
- **Regular Audits**: Periodically review connected integrations and their permissions
- **Secure Credentials**: Never hardcode credentials; use CrewAI's secure authentication flow
### Filtering Tools
On a deployed crew, you can specify which actions are avialbel for each integration from the settings page of the service you connected to.
<Frame>
![Integrations](/images/enterprise/filtering_enterprise_action_tools.png)
</Frame>
### Scoped Deployments for multi user organizations
You can deploy your crew and scope each integration to a specific user. For example, a crew that connects to google can use a specific user's gmail account.
<Tip>
This is useful for multi user organizations where you want to scope the integration to a specific user.
</Tip>
Use the `user_bearer_token` to scope the integration to a specific user so that when the crew is kicked off, it will use the user's bearer token to authenticate with the integration. If user is not logged in, then the crew will not use any connected integrations. Use the default bearer token to authenticate with the integrations thats deployed with the crew.
<Frame>
![Integrations](/images/enterprise/user_bearer_token.png)
</Frame>
### Getting Help
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with integration setup or troubleshooting.
</Card>

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---
title: Tool Repository
description: "Using the Tool Repository to manage your tools"
icon: "toolbox"
---
## Overview
The Tool Repository is a package manager for CrewAI tools. It allows users to publish, install, and manage tools that integrate with CrewAI crews and flows.
Tools can be:
- **Private**: accessible only within your organization (default)
- **Public**: accessible to all CrewAI users if published with the `--public` flag
The repository is not a version control system. Use Git to track code changes and enable collaboration.
## Prerequisites
Before using the Tool Repository, ensure you have:
- A [CrewAI Enterprise](https://app.crewai.com) account
- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed
- uv>=0.5.0 installed. Check out [how to upgrade](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)
- [Git](https://git-scm.com) installed and configured
- Access permissions to publish or install tools in your CrewAI Enterprise organization
## Installing Tools
To install a tool:
```bash
crewai tool install <tool-name>
```
This installs the tool and adds it to `pyproject.toml`.
## Creating and Publishing Tools
To create a new tool project:
```bash
crewai tool create <tool-name>
```
This generates a scaffolded tool project locally.
After making changes, initialize a Git repository and commit the code:
```bash
git init
git add .
git commit -m "Initial version"
```
To publish the tool:
```bash
crewai tool publish
```
By default, tools are published as private. To make a tool public:
```bash
crewai tool publish --public
```
For more details on how to build tools, see [Creating your own tools](https://docs.crewai.com/concepts/tools#creating-your-own-tools).
## Updating Tools
To update a published tool:
1. Modify the tool locally
2. Update the version in `pyproject.toml` (e.g., from `0.1.0` to `0.1.1`)
3. Commit the changes and publish
```bash
git commit -m "Update version to 0.1.1"
crewai tool publish
```
## Deleting Tools
To delete a tool:
1. Go to [CrewAI Enterprise](https://app.crewai.com)
2. Navigate to **Tools**
3. Select the tool
4. Click **Delete**
<Warning>
Deletion is permanent. Deleted tools cannot be restored or re-installed.
</Warning>
## Security Checks
Every published version undergoes automated security checks, and are only available to install after they pass.
You can check the security check status of a tool at:
`CrewAI Enterprise > Tools > Your Tool > Versions`
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with API integration or troubleshooting.
</Card>

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---
title: Traces
description: "Using Traces to monitor your Crews"
icon: "timeline"
---
## Overview
Traces provide comprehensive visibility into your crew executions, helping you monitor performance, debug issues, and optimize your AI agent workflows.
## What are Traces?
Traces in CrewAI Enterprise are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
- Agent thoughts and reasoning
- Task execution details
- Tool usage and outputs
- Token consumption metrics
- Execution times
- Cost estimates
<Frame>
![Traces Overview](/images/enterprise/traces-overview.png)
</Frame>
## Accessing Traces
<Steps>
<Step title="Navigate to the Traces Tab">
Once in your CrewAI Enterprise dashboard, click on the **Traces** to view all execution records.
</Step>
<Step title="Select an Execution">
You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.
</Step>
</Steps>
## Understanding the Trace Interface
The trace interface is divided into several sections, each providing different insights into your crew's execution:
### 1. Execution Summary
The top section displays high-level metrics about the execution:
- **Total Tokens**: Number of tokens consumed across all tasks
- **Prompt Tokens**: Tokens used in prompts to the LLM
- **Completion Tokens**: Tokens generated in LLM responses
- **Requests**: Number of API calls made
- **Execution Time**: Total duration of the crew run
- **Estimated Cost**: Approximate cost based on token usage
<Frame>
![Execution Summary](/images/enterprise/trace-summary.png)
</Frame>
### 2. Tasks & Agents
This section shows all tasks and agents that were part of the crew execution:
- Task name and agent assignment
- Agents and LLMs used for each task
- Status (completed/failed)
- Individual execution time of the task
<Frame>
![Task List](/images/enterprise/trace-tasks.png)
</Frame>
### 3. Final Output
Displays the final result produced by the crew after all tasks are completed.
<Frame>
![Final Output](/images/enterprise/final-output.png)
</Frame>
### 4. Execution Timeline
A visual representation of when each task started and ended, helping you identify bottlenecks or parallel execution patterns.
<Frame>
![Execution Timeline](/images/enterprise/trace-timeline.png)
</Frame>
### 5. Detailed Task View
When you click on a specific task in the timeline or task list, you'll see:
<Frame>
![Detailed Task View](/images/enterprise/trace-detailed-task.png)
</Frame>
- **Task Key**: Unique identifier for the task
- **Task ID**: Technical identifier in the system
- **Status**: Current state (completed/running/failed)
- **Agent**: Which agent performed the task
- **LLM**: Language model used for this task
- **Start/End Time**: When the task began and completed
- **Execution Time**: Duration of this specific task
- **Task Description**: What the agent was instructed to do
- **Expected Output**: What output format was requested
- **Input**: Any input provided to this task from previous tasks
- **Output**: The actual result produced by the agent
## Using Traces for Debugging
Traces are invaluable for troubleshooting issues with your crews:
<Steps>
<Step title="Identify Failure Points">
When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for:
- Failed tasks
- Unexpected agent decisions
- Tool usage errors
- Misinterpreted instructions
<Frame>
![Failure Points](/images/enterprise/failure.png)
</Frame>
</Step>
<Step title="Optimize Performance">
Use execution metrics to identify performance bottlenecks:
- Tasks that took longer than expected
- Excessive token usage
- Redundant tool operations
- Unnecessary API calls
</Step>
<Step title="Improve Cost Efficiency">
Analyze token usage and cost estimates to optimize your crew's efficiency:
- Consider using smaller models for simpler tasks
- Refine prompts to be more concise
- Cache frequently accessed information
- Structure tasks to minimize redundant operations
</Step>
</Steps>
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.
</Card>

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---
title: Webhook Streaming
description: "Using Webhook Streaming to stream events to your webhook"
icon: "webhook"
---
## Overview
Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to
CrewAI Enterprise, such as model calls, tool usage, and flow steps.
## Usage
When using the Kickoff API, include a `webhooks` object to your request, for example:
```json
{
"inputs": {"foo": "bar"},
"webhooks": {
"events": ["crew_kickoff_started", "llm_call_started"],
"url": "https://your.endpoint/webhook",
"realtime": false,
"authentication": {
"strategy": "bearer",
"token": "my-secret-token"
}
}
}
```
If `realtime` is set to `true`, each event is delivered individually and immediately, at the cost of crew/flow performance.
## Webhook Format
Each webhook sends a list of events:
```json
{
"events": [
{
"id": "event-id",
"execution_id": "crew-run-id",
"timestamp": "2025-02-16T10:58:44.965Z",
"type": "llm_call_started",
"data": {
"model": "gpt-4",
"messages": [
{"role": "system", "content": "You are an assistant."},
{"role": "user", "content": "Summarize this article."}
]
}
}
]
}
```
The `data` object structure varies by event type. Refer to the [event list](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) on GitHub.
As requests are sent over HTTP, the order of events can't be guaranteed. If you need ordering, use the `timestamp` field.
## Supported Events
CrewAI supports both system events and custom events in Enterprise Event Streaming. These events are sent to your configured webhook endpoint during crew and flow execution.
- `crew_kickoff_started`
- `crew_step_started`
- `crew_step_completed`
- `crew_execution_completed`
- `llm_call_started`
- `llm_call_completed`
- `tool_usage_started`
- `tool_usage_completed`
- `crew_test_failed`
- *...and others*
Event names match the internal event bus. See [GitHub source](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) for the full list.
You can emit your own custom events, and they will be delivered through the webhook stream alongside system events.
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with webhook integration or troubleshooting.
</Card>

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---
title: "Azure OpenAI Setup"
description: "Configure Azure OpenAI with Crew Studio for enterprise LLM connections"
icon: "microsoft"
---
This guide walks you through connecting Azure OpenAI with Crew Studio for seamless enterprise AI operations.
## Setup Process
<Steps>
<Step title="Access Azure OpenAI Studio">
1. In Azure, go to `Azure AI Services > select your deployment > open Azure OpenAI Studio`.
2. On the left menu, click `Deployments`. If you don't have one, create a deployment with your desired model.
3. Once created, select your deployment and locate the `Target URI` and `Key` on the right side of the page. Keep this page open, as you'll need this information.
<Frame>
<img src="/images/enterprise/azure-openai-studio.png" alt="Azure OpenAI Studio" />
</Frame>
</Step>
<Step title="Configure CrewAI Enterprise Connection">
4. In another tab, open `CrewAI Enterprise > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
5. On the same page, add environment variables from step 3:
- One named `AZURE_DEPLOYMENT_TARGET_URL` (using the Target URI). The URL should look like this: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
- Another named `AZURE_API_KEY` (using the Key).
6. Click `Add Connection` to save your LLM Connection.
</Step>
<Step title="Set Default Configuration">
7. In `CrewAI Enterprise > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
</Step>
<Step title="Configure Network Access">
8. Ensure network access settings:
- In Azure, go to `Azure OpenAI > select your deployment`.
- Navigate to `Resource Management > Networking`.
- Ensure that `Allow access from all networks` is enabled. If this setting is restricted, CrewAI may be blocked from accessing your Azure OpenAI endpoint.
</Step>
</Steps>
## Verification
You're all set! Crew Studio will now use your Azure OpenAI connection. Test the connection by creating a simple crew or task to ensure everything is working properly.
## Troubleshooting
If you encounter issues:
- Verify the Target URI format matches the expected pattern
- Check that the API key is correct and has proper permissions
- Ensure network access is configured to allow CrewAI connections
- Confirm the deployment model matches what you've configured in CrewAI

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---
title: "Build Crew"
description: "A Crew is a group of agents that work together to complete a task."
icon: "people-arrows"
---
## Overview
[CrewAI Enterprise](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
## Getting Started
<iframe
width="100%"
height="400"
src="https://www.youtube.com/embed/-kSOTtYzgEw"
title="Building Crews with CrewAI CLI"
frameborder="0"
style={{ borderRadius: '10px' }}
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen
></iframe>
### Installation and Setup
<Card title="Follow Standard Installation" icon="wrench" href="/installation">
Follow our standard installation guide to set up CrewAI CLI and create your first project.
</Card>
### Building Your Crew
<Card title="Quickstart Tutorial" icon="rocket" href="/quickstart">
Follow our quickstart guide to create your first agent crew using YAML configuration.
</Card>
## Support and Resources
For Enterprise-specific support or questions, contact our dedicated support team at [support@crewai.com](mailto:support@crewai.com).
<Card title="Schedule a Demo" icon="calendar" href="mailto:support@crewai.com">
Book time with our team to learn more about Enterprise features and how they can benefit your organization.
</Card>

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---
title: "Deploy Crew"
description: "Deploying a Crew on CrewAI Enterprise"
icon: "rocket"
---
<Note>
After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI Enterprise platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
</Note>
## Prerequisites
<CardGroup cols={2}>
<Card title="Crew Ready for Deployment" icon="users">
You should have a working crew either built locally or created through Crew Studio
</Card>
<Card title="GitHub Repository" icon="github">
Your crew code should be in a GitHub repository (for GitHub integration method)
</Card>
</CardGroup>
## Option 1: Deploy Using CrewAI CLI
The CLI provides the fastest way to deploy locally developed crews to the Enterprise platform.
<Steps>
<Step title="Install CrewAI CLI">
If you haven't already, install the CrewAI CLI:
```bash
pip install crewai[tools]
```
<Tip>
The CLI comes with the main CrewAI package, but the `[tools]` extra ensures you have all deployment dependencies.
</Tip>
</Step>
<Step title="Authenticate with the Enterprise Platform">
First, you need to authenticate your CLI with the CrewAI Enterprise platform:
```bash
# If you already have a CrewAI Enterprise account
crewai login
# If you're creating a new account
crewai signup
```
When you run either command, the CLI will:
1. Display a URL and a unique device code
2. Open your browser to the authentication page
3. Prompt you to confirm the device
4. Complete the authentication process
Upon successful authentication, you'll see a confirmation message in your terminal!
</Step>
<Step title="Create a Deployment">
From your project directory, run:
```bash
crewai deploy create
```
This command will:
1. Detect your GitHub repository information
2. Identify environment variables in your local `.env` file
3. Securely transfer these variables to the Enterprise platform
4. Create a new deployment with a unique identifier
On successful creation, you'll see a message like:
```shell
Deployment created successfully!
Name: your_project_name
Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
Current Status: Deploy Enqueued
```
</Step>
<Step title="Monitor Deployment Progress">
Track the deployment status with:
```bash
crewai deploy status
```
For detailed logs of the build process:
```bash
crewai deploy logs
```
<Tip>
The first deployment typically takes 10-15 minutes as it builds the container images. Subsequent deployments are much faster.
</Tip>
</Step>
</Steps>
## Additional CLI Commands
The CrewAI CLI offers several commands to manage your deployments:
```bash
# List all your deployments
crewai deploy list
# Get the status of your deployment
crewai deploy status
# View the logs of your deployment
crewai deploy logs
# Push updates after code changes
crewai deploy push
# Remove a deployment
crewai deploy remove <deployment_id>
```
## Option 2: Deploy Directly via Web Interface
You can also deploy your crews directly through the CrewAI Enterprise web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
<Steps>
<Step title="Pushing to GitHub">
You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/quickstart).
</Step>
<Step title="Connecting GitHub to CrewAI Enterprise">
1. Log in to [CrewAI Enterprise](https://app.crewai.com)
2. Click on the button "Connect GitHub"
<Frame>
![Connect GitHub Button](/images/enterprise/connect-github.png)
</Frame>
</Step>
<Step title="Select the Repository">
After connecting your GitHub account, you'll be able to select which repository to deploy:
<Frame>
![Select Repository](/images/enterprise/select-repo.png)
</Frame>
</Step>
<Step title="Set Environment Variables">
Before deploying, you'll need to set up your environment variables to connect to your LLM provider or other services:
1. You can add variables individually or in bulk
2. Enter your environment variables in `KEY=VALUE` format (one per line)
<Frame>
![Set Environment Variables](/images/enterprise/set-env-variables.png)
</Frame>
</Step>
<Step title="Deploy Your Crew">
1. Click the "Deploy" button to start the deployment process
2. You can monitor the progress through the progress bar
3. The first deployment typically takes around 10-15 minutes; subsequent deployments will be faster
<Frame>
![Deploy Progress](/images/enterprise/deploy-progress.png)
</Frame>
Once deployment is complete, you'll see:
- Your crew's unique URL
- A Bearer token to protect your crew API
- A "Delete" button if you need to remove the deployment
</Step>
</Steps>
## ⚠️ Environment Variable Security Requirements
<Warning>
**Important**: CrewAI Enterprise has security restrictions on environment variable names that can cause deployment failures if not followed.
</Warning>
### Blocked Environment Variable Patterns
For security reasons, the following environment variable naming patterns are **automatically filtered** and will cause deployment issues:
**Blocked Patterns:**
- Variables ending with `_TOKEN` (e.g., `MY_API_TOKEN`)
- Variables ending with `_PASSWORD` (e.g., `DB_PASSWORD`)
- Variables ending with `_SECRET` (e.g., `API_SECRET`)
- Variables ending with `_KEY` in certain contexts
**Specific Blocked Variables:**
- `GITHUB_USER`, `GITHUB_TOKEN`
- `AWS_REGION`, `AWS_DEFAULT_REGION`
- Various internal CrewAI system variables
### Allowed Exceptions
Some variables are explicitly allowed despite matching blocked patterns:
- `AZURE_AD_TOKEN`
- `AZURE_OPENAI_AD_TOKEN`
- `ENTERPRISE_ACTION_TOKEN`
- `CREWAI_ENTEPRISE_TOOLS_TOKEN`
### How to Fix Naming Issues
If your deployment fails due to environment variable restrictions:
```bash
# ❌ These will cause deployment failures
OPENAI_TOKEN=sk-...
DATABASE_PASSWORD=mypassword
API_SECRET=secret123
# ✅ Use these naming patterns instead
OPENAI_API_KEY=sk-...
DATABASE_CREDENTIALS=mypassword
API_CONFIG=secret123
```
### Best Practices
1. **Use standard naming conventions**: `PROVIDER_API_KEY` instead of `PROVIDER_TOKEN`
2. **Test locally first**: Ensure your crew works with the renamed variables
3. **Update your code**: Change any references to the old variable names
4. **Document changes**: Keep track of renamed variables for your team
<Tip>
If you encounter deployment failures with cryptic environment variable errors, check your variable names against these patterns first.
</Tip>
### Interact with Your Deployed Crew
Once deployment is complete, you can access your crew through:
1. **REST API**: The platform generates a unique HTTPS endpoint with these key routes:
- `/inputs`: Lists the required input parameters
- `/kickoff`: Initiates an execution with provided inputs
- `/status/{kickoff_id}`: Checks the execution status
2. **Web Interface**: Visit [app.crewai.com](https://app.crewai.com) to access:
- **Status tab**: View deployment information, API endpoint details, and authentication token
- **Run tab**: Visual representation of your crew's structure
- **Executions tab**: History of all executions
- **Metrics tab**: Performance analytics
- **Traces tab**: Detailed execution insights
### Trigger an Execution
From the Enterprise dashboard, you can:
1. Click on your crew's name to open its details
2. Select "Trigger Crew" from the management interface
3. Enter the required inputs in the modal that appears
4. Monitor progress as the execution moves through the pipeline
### Monitoring and Analytics
The Enterprise platform provides comprehensive observability features:
- **Execution Management**: Track active and completed runs
- **Traces**: Detailed breakdowns of each execution
- **Metrics**: Token usage, execution times, and costs
- **Timeline View**: Visual representation of task sequences
### Advanced Features
The Enterprise platform also offers:
- **Environment Variables Management**: Securely store and manage API keys
- **LLM Connections**: Configure integrations with various LLM providers
- **Custom Tools Repository**: Create, share, and install tools
- **Crew Studio**: Build crews through a chat interface without writing code
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with deployment issues or questions about the Enterprise platform.
</Card>

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---
title: "Enable Crew Studio"
description: "Enabling Crew Studio on CrewAI Enterprise"
icon: "comments"
---
<Tip>
Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface.
</Tip>
## What is Crew Studio?
Crew Studio is an innovative way to create AI agent crews without writing code.
<Frame>
![Crew Studio Interface](/images/enterprise/crew-studio-interface.png)
</Frame>
With Crew Studio, you can:
- Chat with the Crew Assistant to describe your problem
- Automatically generate agents and tasks
- Select appropriate tools
- Configure necessary inputs
- Generate downloadable code for customization
- Deploy directly to the CrewAI Enterprise platform
## Configuration Steps
Before you can start using Crew Studio, you need to configure your LLM connections:
<Steps>
<Step title="Set Up LLM Connection">
Go to the **LLM Connections** tab in your CrewAI Enterprise dashboard and create a new LLM connection.
<Note>
Feel free to use any LLM provider you want that is supported by CrewAI.
</Note>
Configure your LLM connection:
- Enter a `Connection Name` (e.g., `OpenAI`)
- Select your model provider: `openai` or `azure`
- Select models you'd like to use in your Studio-generated Crews
- We recommend at least `gpt-4o`, `o1-mini`, and `gpt-4o-mini`
- Add your API key as an environment variable:
- For OpenAI: Add `OPENAI_API_KEY` with your API key
- For Azure OpenAI: Refer to [this article](https://blog.crewai.com/configuring-azure-openai-with-crewai-a-comprehensive-guide/) for configuration details
- Click `Add Connection` to save your configuration
<Frame>
![LLM Connection Configuration](/images/enterprise/llm-connection-config.png)
</Frame>
</Step>
<Step title="Verify Connection Added">
Once you complete the setup, you'll see your new connection added to the list of available connections.
<Frame>
![Connection Added](/images/enterprise/connection-added.png)
</Frame>
</Step>
<Step title="Configure LLM Defaults">
In the main menu, go to **Settings → Defaults** and configure the LLM Defaults settings:
- Select default models for agents and other components
- Set default configurations for Crew Studio
Click `Save Settings` to apply your changes.
<Frame>
![LLM Defaults Configuration](/images/enterprise/llm-defaults.png)
</Frame>
</Step>
</Steps>
## Using Crew Studio
Now that you've configured your LLM connection and default settings, you're ready to start using Crew Studio!
<Steps>
<Step title="Access Studio">
Navigate to the **Studio** section in your CrewAI Enterprise dashboard.
</Step>
<Step title="Start a Conversation">
Start a conversation with the Crew Assistant by describing the problem you want to solve:
```md
I need a crew that can research the latest AI developments and create a summary report.
```
The Crew Assistant will ask clarifying questions to better understand your requirements.
</Step>
<Step title="Review Generated Crew">
Review the generated crew configuration, including:
- Agents and their roles
- Tasks to be performed
- Required inputs
- Tools to be used
This is your opportunity to refine the configuration before proceeding.
</Step>
<Step title="Deploy or Download">
Once you're satisfied with the configuration, you can:
- Download the generated code for local customization
- Deploy the crew directly to the CrewAI Enterprise platform
- Modify the configuration and regenerate the crew
</Step>
<Step title="Test Your Crew">
After deployment, test your crew with sample inputs to ensure it performs as expected.
</Step>
</Steps>
<Tip>
For best results, provide clear, detailed descriptions of what you want your crew to accomplish. Include specific inputs and expected outputs in your description.
</Tip>
## Example Workflow
Here's a typical workflow for creating a crew with Crew Studio:
<Steps>
<Step title="Describe Your Problem">
Start by describing your problem:
```md
I need a crew that can analyze financial news and provide investment recommendations
```
</Step>
<Step title="Answer Questions">
Respond to clarifying questions from the Crew Assistant to refine your requirements.
</Step>
<Step title="Review the Plan">
Review the generated crew plan, which might include:
- A Research Agent to gather financial news
- An Analysis Agent to interpret the data
- A Recommendations Agent to provide investment advice
</Step>
<Step title="Approve or Modify">
Approve the plan or request changes if necessary.
</Step>
<Step title="Download or Deploy">
Download the code for customization or deploy directly to the platform.
</Step>
<Step title="Test and Refine">
Test your crew with sample inputs and refine as needed.
</Step>
</Steps>
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with Crew Studio or any other CrewAI Enterprise features.
</Card>

53
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---
title: "HubSpot Trigger"
description: "Trigger CrewAI crews directly from HubSpot Workflows"
icon: "hubspot"
---
This guide provides a step-by-step process to set up HubSpot triggers for CrewAI Enterprise, enabling you to initiate crews directly from HubSpot Workflows.
## Prerequisites
- A CrewAI Enterprise account
- A HubSpot account with the [HubSpot Workflows](https://knowledge.hubspot.com/workflows/create-workflows) feature
## Setup Steps
<Steps>
<Step title="Connect your HubSpot account with CrewAI Enterprise">
- Log in to your `CrewAI Enterprise account > Triggers`
- Select `HubSpot` from the list of available triggers
- Choose the HubSpot account you want to connect with CrewAI Enterprise
- Follow the on-screen prompts to authorize CrewAI Enterprise access to your HubSpot account
- A confirmation message will appear once HubSpot is successfully connected with CrewAI Enterprise
</Step>
<Step title="Create a HubSpot Workflow">
- Log in to your `HubSpot account > Automations > Workflows > New workflow`
- Select the workflow type that fits your needs (e.g., Start from scratch)
- In the workflow builder, click the Plus (+) icon to add a new action.
- Choose `Integrated apps > CrewAI > Kickoff a Crew`.
- Select the Crew you want to initiate.
- Click `Save` to add the action to your workflow
<Frame>
<img src="/images/enterprise/hubspot-workflow-1.png" alt="HubSpot Workflow 1" />
</Frame>
</Step>
<Step title="Use Crew results with other actions">
- After the Kickoff a Crew step, click the Plus (+) icon to add a new action.
- For example, to send an internal email notification, choose `Communications > Send internal email notification`
- In the Body field, click `Insert data`, select `View properties or action outputs from > Action outputs > Crew Result` to include Crew data in the email
<Frame>
<img src="/images/enterprise/hubspot-workflow-2.png" alt="HubSpot Workflow 2" />
</Frame>
- Configure any additional actions as needed
- Review your workflow steps to ensure everything is set up correctly
- Activate the workflow
<Frame>
<img src="/images/enterprise/hubspot-workflow-3.png" alt="HubSpot Workflow 3" />
</Frame>
</Step>
</Steps>
## Additional Resources
For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows).

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---
title: "HITL Workflows"
description: "Learn how to implement Human-In-The-Loop workflows in CrewAI for enhanced decision-making"
icon: "user-check"
---
Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.
## Setting Up HITL Workflows
<Steps>
<Step title="Configure Your Task">
Set up your task with human input enabled:
<Frame>
<img src="/images/enterprise/crew-human-input.png" alt="Crew Human Input" />
</Frame>
</Step>
<Step title="Provide Webhook URL">
When kicking off your crew, include a webhook URL for human input:
<Frame>
<img src="/images/enterprise/crew-webhook-url.png" alt="Crew Webhook URL" />
</Frame>
</Step>
<Step title="Receive Webhook Notification">
Once the crew completes the task requiring human input, you'll receive a webhook notification containing:
- **Execution ID**
- **Task ID**
- **Task output**
</Step>
<Step title="Review Task Output">
The system will pause in the `Pending Human Input` state. Review the task output carefully.
</Step>
<Step title="Submit Human Feedback">
Call the resume endpoint of your crew with the following information:
<Frame>
<img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
</Frame>
<Warning>
**Feedback Impact on Task Execution**:
It's crucial to exercise care when providing feedback, as the entire feedback content will be incorporated as additional context for further task executions.
</Warning>
This means:
- All information in your feedback becomes part of the task's context.
- Irrelevant details may negatively influence it.
- Concise, relevant feedback helps maintain task focus and efficiency.
- Always review your feedback carefully before submission to ensure it contains only pertinent information that will positively guide the task's execution.
</Step>
<Step title="Handle Negative Feedback">
If you provide negative feedback:
- The crew will retry the task with added context from your feedback.
- You'll receive another webhook notification for further review.
- Repeat steps 4-6 until satisfied.
</Step>
<Step title="Execution Continuation">
When you submit positive feedback, the execution will proceed to the next steps.
</Step>
</Steps>
## Best Practices
- **Be Specific**: Provide clear, actionable feedback that directly addresses the task at hand
- **Stay Relevant**: Only include information that will help improve the task execution
- **Be Timely**: Respond to HITL prompts promptly to avoid workflow delays
- **Review Carefully**: Double-check your feedback before submitting to ensure accuracy
## Common Use Cases
HITL workflows are particularly valuable for:
- Quality assurance and validation
- Complex decision-making scenarios
- Sensitive or high-stakes operations
- Creative tasks requiring human judgment
- Compliance and regulatory reviews

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---
title: "Kickoff Crew"
description: "Kickoff a Crew on CrewAI Enterprise"
icon: "flag-checkered"
---
## Overview
Once you've deployed your crew to the CrewAI Enterprise platform, you can kickoff executions through the web interface or the API. This guide covers both approaches.
## Method 1: Using the Web Interface
### Step 1: Navigate to Your Deployed Crew
1. Log in to [CrewAI Enterprise](https://app.crewai.com)
2. Click on the crew name from your projects list
3. You'll be taken to the crew's detail page
<Frame>
![Crew Dashboard](/images/enterprise/crew-dashboard.png)
</Frame>
### Step 2: Initiate Execution
From your crew's detail page, you have two options to kickoff an execution:
#### Option A: Quick Kickoff
1. Click the `Kickoff` link in the Test Endpoints section
2. Enter the required input parameters for your crew in the JSON editor
3. Click the `Send Request` button
<Frame>
![Kickoff Endpoint](/images/enterprise/kickoff-endpoint.png)
</Frame>
#### Option B: Using the Visual Interface
1. Click the `Run` tab in the crew detail page
2. Enter the required inputs in the form fields
3. Click the `Run Crew` button
<Frame>
![Run Crew](/images/enterprise/run-crew.png)
</Frame>
### Step 3: Monitor Execution Progress
After initiating the execution:
1. You'll receive a response containing a `kickoff_id` - **copy this ID**
2. This ID is essential for tracking your execution
<Frame>
![Copy Task ID](/images/enterprise/copy-task-id.png)
</Frame>
### Step 4: Check Execution Status
To monitor the progress of your execution:
1. Click the "Status" endpoint in the Test Endpoints section
2. Paste the `kickoff_id` into the designated field
3. Click the "Get Status" button
<Frame>
![Get Status](/images/enterprise/get-status.png)
</Frame>
The status response will show:
- Current execution state (`running`, `completed`, etc.)
- Details about which tasks are in progress
- Any outputs produced so far
### Step 5: View Final Results
Once execution is complete:
1. The status will change to `completed`
2. You can view the full execution results and outputs
3. For a more detailed view, check the `Executions` tab in the crew detail page
## Method 2: Using the API
You can also kickoff crews programmatically using the CrewAI Enterprise REST API.
### Authentication
All API requests require a bearer token for authentication:
```bash
curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
```
Your bearer token is available on the Status tab of your crew's detail page.
### Checking Crew Health
Before executing operations, you can verify that your crew is running properly:
```bash
curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
```
A successful response will return a message indicating the crew is operational:
```
Healthy%
```
### Step 1: Retrieve Required Inputs
First, determine what inputs your crew requires:
```bash
curl -X GET \
-H "Authorization: Bearer YOUR_CREW_TOKEN" \
https://your-crew-url.crewai.com/inputs
```
The response will be a JSON object containing an array of required input parameters, for example:
```json
{"inputs":["topic","current_year"]}
```
This example shows that this particular crew requires two inputs: `topic` and `current_year`.
### Step 2: Kickoff Execution
Initiate execution by providing the required inputs:
```bash
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_CREW_TOKEN" \
-d '{"inputs": {"topic": "AI Agent Frameworks", "current_year": "2025"}}' \
https://your-crew-url.crewai.com/kickoff
```
The response will include a `kickoff_id` that you'll need for tracking:
```json
{"kickoff_id":"abcd1234-5678-90ef-ghij-klmnopqrstuv"}
```
### Step 3: Check Execution Status
Monitor the execution progress using the kickoff_id:
```bash
curl -X GET \
-H "Authorization: Bearer YOUR_CREW_TOKEN" \
https://your-crew-url.crewai.com/status/abcd1234-5678-90ef-ghij-klmnopqrstuv
```
## Handling Executions
### Long-Running Executions
For executions that may take a long time:
1. Consider implementing a polling mechanism to check status periodically
2. Use webhooks (if available) for notification when execution completes
3. Implement error handling for potential timeouts
### Execution Context
The execution context includes:
- Inputs provided at kickoff
- Environment variables configured during deployment
- Any state maintained between tasks
### Debugging Failed Executions
If an execution fails:
1. Check the "Executions" tab for detailed logs
2. Review the "Traces" tab for step-by-step execution details
3. Look for LLM responses and tool usage in the trace details
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with execution issues or questions about the Enterprise platform.
</Card>

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---
title: "React Component Export"
description: "Learn how to export and integrate CrewAI Enterprise React components into your applications"
icon: "react"
---
This guide explains how to export CrewAI Enterprise crews as React components and integrate them into your own applications.
## Exporting a React Component
<Steps>
<Step title="Export the Component">
Click on the ellipsis (three dots on the right of your deployed crew) and select the export option and save the file locally. We will be using `CrewLead.jsx` for our example.
<Frame>
<img src="/images/enterprise/export-react-component.png" alt="Export React Component" />
</Frame>
</Step>
</Steps>
## Setting Up Your React Environment
To run this React component locally, you'll need to set up a React development environment and integrate this component into a React project.
<Steps>
<Step title="Install Node.js">
- Download and install Node.js from the official website: https://nodejs.org/
- Choose the LTS (Long Term Support) version for stability.
</Step>
<Step title="Create a new React project">
- Open Command Prompt or PowerShell
- Navigate to the directory where you want to create your project
- Run the following command to create a new React project:
```bash
npx create-react-app my-crew-app
```
- Change into the project directory:
```bash
cd my-crew-app
```
</Step>
<Step title="Install necessary dependencies">
```bash
npm install react-dom
```
</Step>
<Step title="Create the CrewLead component">
- Move the downloaded file `CrewLead.jsx` into the `src` folder of your project,
</Step>
<Step title="Modify your App.js to use the CrewLead component">
- Open `src/App.js`
- Replace its contents with something like this:
```jsx
import React from 'react';
import CrewLead from './CrewLead';
function App() {
return (
<div className="App">
<CrewLead baseUrl="YOUR_API_BASE_URL" bearerToken="YOUR_BEARER_TOKEN" />
</div>
);
}
export default App;
```
- Replace `YOUR_API_BASE_URL` and `YOUR_BEARER_TOKEN` with the actual values for your API.
</Step>
<Step title="Start the development server">
- In your project directory, run:
```bash
npm start
```
- This will start the development server, and your default web browser should open automatically to http://localhost:3000, where you'll see your React app running.
</Step>
</Steps>
## Customization
You can then customise the `CrewLead.jsx` to add color, title etc
<Frame>
<img src="/images/enterprise/customise-react-component.png" alt="Customise React Component" />
</Frame>
<Frame>
<img src="/images/enterprise/customise-react-component-2.png" alt="Customise React Component" />
</Frame>
## Next Steps
- Customize the component styling to match your application's design
- Add additional props for configuration
- Integrate with your application's state management
- Add error handling and loading states

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---
title: "Salesforce Trigger"
description: "Trigger CrewAI crews from Salesforce workflows for CRM automation"
icon: "salesforce"
---
CrewAI Enterprise can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations.
## Overview
Salesforce is a leading customer relationship management (CRM) platform that helps businesses streamline their sales, service, and marketing operations. By setting up CrewAI triggers from Salesforce, you can:
- Automate lead scoring and qualification
- Generate personalized sales materials
- Enhance customer service with AI-powered responses
- Streamline data analysis and reporting
## Demo
<Frame>
<iframe width="100%" height="400" src="https://www.youtube.com/embed/oJunVqjjfu4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</Frame>
## Getting Started
To set up Salesforce triggers:
1. **Contact Support**: Reach out to CrewAI Enterprise support for assistance with Salesforce trigger setup
2. **Review Requirements**: Ensure you have the necessary Salesforce permissions and API access
3. **Configure Connection**: Work with the support team to establish the connection between CrewAI and your Salesforce instance
4. **Test Triggers**: Verify the triggers work correctly with your specific use cases
## Use Cases
Common Salesforce + CrewAI trigger scenarios include:
- **Lead Processing**: Automatically analyze and score incoming leads
- **Proposal Generation**: Create customized proposals based on opportunity data
- **Customer Insights**: Generate analysis reports from customer interaction history
- **Follow-up Automation**: Create personalized follow-up messages and recommendations
## Next Steps
For detailed setup instructions and advanced configuration options, please contact CrewAI Enterprise support who can provide tailored guidance for your specific Salesforce environment and business needs.

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---
title: "Slack Trigger"
description: "Trigger CrewAI crews directly from Slack using slash commands"
icon: "slack"
---
This guide explains how to start a crew directly from Slack using CrewAI triggers.
## Prerequisites
- CrewAI Slack trigger installed and connected to your Slack workspace
- At least one crew configured in CrewAI
## Setup Steps
<Steps>
<Step title="Ensure the CrewAI Slack trigger is set up">
In the CrewAI dashboard, navigate to the **Triggers** section.
<Frame>
<img src="/images/enterprise/slack-integration.png" alt="CrewAI Slack Integration" />
</Frame>
Verify that Slack is listed and is connected.
</Step>
<Step title="Open your Slack channel">
- Navigate to the channel where you want to kickoff the crew.
- Type the slash command "**/kickoff**" to initiate the crew kickoff process.
- You should see a "**Kickoff crew**" appear as you type:
<Frame>
<img src="/images/enterprise/kickoff-slack-crew.png" alt="Kickoff crew" />
</Frame>
- Press Enter or select the "**Kickoff crew**" option. A dialog box titled "**Kickoff an AI Crew**" will appear.
</Step>
<Step title="Select the crew you want to start">
- In the dropdown menu labeled "**Select of the crews online:**", choose the crew you want to start.
- In the example below, "**prep-for-meeting**" is selected:
<Frame>
<img src="/images/enterprise/kickoff-slack-crew-dropdown.png" alt="Kickoff crew dropdown" />
</Frame>
- If your crew requires any inputs, click the "**Add Inputs**" button to provide them.
<Note>
The "**Add Inputs**" button is shown in the example above but is not yet clicked.
</Note>
</Step>
<Step title="Click Kickoff and wait for the crew to complete">
- Once you've selected the crew and added any necessary inputs, click "**Kickoff**" to start the crew.
<Frame>
<img src="/images/enterprise/kickoff-slack-crew-kickoff.png" alt="Kickoff crew" />
</Frame>
- The crew will start executing and you will see the results in the Slack channel.
<Frame>
<img src="/images/enterprise/kickoff-slack-crew-results.png" alt="Kickoff crew results" />
</Frame>
</Step>
</Steps>
## Tips
- Make sure you have the necessary permissions to use the `/kickoff` command in your Slack workspace.
- If you don't see your desired crew in the dropdown, ensure it's properly configured and online in CrewAI.

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---
title: "Team Management"
description: "Learn how to invite and manage team members in your CrewAI Enterprise organization"
icon: "users"
---
As an administrator of a CrewAI Enterprise account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step.
## Inviting Team Members
<Steps>
<Step title="Access the Settings Page">
- Log in to your CrewAI Enterprise account
- Look for the gear icon (⚙️) in the top right corner of the dashboard
- Click on the gear icon to access the **Settings** page:
<Frame>
<img src="/images/enterprise/settings-page.png" alt="Settings Page" />
</Frame>
</Step>
<Step title="Navigate to the Members Section">
- On the Settings page, you'll see a `Members` tab
- Click on the `Members` tab to access the **Members** page:
<Frame>
<img src="/images/enterprise/members-tab.png" alt="Members Tab" />
</Frame>
</Step>
<Step title="Invite New Members">
- In the Members section, you'll see a list of current members (including yourself)
- Locate the `Email` input field
- Enter the email address of the person you want to invite
- Click the `Invite` button to send the invitation
</Step>
<Step title="Repeat as Needed">
- You can repeat this process to invite multiple team members
- Each invited member will receive an email invitation to join your organization
</Step>
</Steps>
## Adding Roles
You can add roles to your team members to control their access to different parts of the platform.
<Steps>
<Step title="Access the Settings Page">
- Log in to your CrewAI Enterprise account
- Look for the gear icon (⚙️) in the top right corner of the dashboard
- Click on the gear icon to access the **Settings** page:
<Frame>
<img src="/images/enterprise/settings-page.png" alt="Settings Page" />
</Frame>
</Step>
<Step title="Navigate to the Members Section">
- On the Settings page, you'll see a `Roles` tab
- Click on the `Roles` tab to access the **Roles** page.
<Frame>
<img src="/images/enterprise/roles-tab.png" alt="Roles Tab" />
</Frame>
- Click on the `Add Role` button to add a new role.
- Enter the details and permissions of the role and click the `Create Role` button to create the role.
<Frame>
<img src="/images/enterprise/add-role-modal.png" alt="Add Role Modal" />
</Frame>
</Step>
<Step title="Add Roles to Members">
- In the Members section, you'll see a list of current members (including yourself)
<Frame>
<img src="/images/enterprise/member-accepted-invitation.png" alt="Member Accepted Invitation" />
</Frame>
- Once the member has accepted the invitation, you can add a role to them.
- Navigate back to `Roles` tab
- Go to the member you want to add a role to and under the `Role` column, click on the dropdown
- Select the role you want to add to the member
- Click the `Update` button to save the role
<Frame>
<img src="/images/enterprise/assign-role.png" alt="Add Role to Member" />
</Frame>
</Step>
</Steps>
## Important Notes
- **Admin Privileges**: Only users with administrative privileges can invite new members
- **Email Accuracy**: Ensure you have the correct email addresses for your team members
- **Invitation Acceptance**: Invited members will need to accept the invitation to join your organization
- **Email Notifications**: You may want to inform your team members to check their email (including spam folders) for the invitation
By following these steps, you can easily expand your team and collaborate more effectively within your CrewAI Enterprise organization.

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---
title: "Update Crew"
description: "Updating a Crew on CrewAI Enterprise"
icon: "pencil"
---
<Note>
After deploying your crew to CrewAI Enterprise, you may need to make updates to the code, security settings, or configuration.
This guide explains how to perform these common update operations.
</Note>
## Why Update Your Crew?
CrewAI won't automatically pick up GitHub updates by default, so you'll need to manually trigger updates, unless you checked the `Auto-update` option when deploying your crew.
There are several reasons you might want to update your crew deployment:
- You want to update the code with a latest commit you pushed to GitHub
- You want to reset the bearer token for security reasons
- You want to update environment variables
## 1. Updating Your Crew Code for a Latest Commit
When you've pushed new commits to your GitHub repository and want to update your deployment:
1. Navigate to your crew in the CrewAI Enterprise platform
2. Click on the `Re-deploy` button on your crew details page
<Frame>
![Re-deploy Button](/images/enterprise/redeploy-button.png)
</Frame>
This will trigger an update that you can track using the progress bar. The system will pull the latest code from your repository and rebuild your deployment.
## 2. Resetting Bearer Token
If you need to generate a new bearer token (for example, if you suspect the current token might have been compromised):
1. Navigate to your crew in the CrewAI Enterprise platform
2. Find the `Bearer Token` section
3. Click the `Reset` button next to your current token
<Frame>
![Reset Token](/images/enterprise/reset-token.png)
</Frame>
<Warning>
Resetting your bearer token will invalidate the previous token immediately. Make sure to update any applications or scripts that are using the old token.
</Warning>
## 3. Updating Environment Variables
To update the environment variables for your crew:
1. First access the deployment page by clicking on your crew's name
<Frame>
![Environment Variables Button](/images/enterprise/env-vars-button.png)
</Frame>
2. Locate the `Environment Variables` section (you will need to click the `Settings` icon to access it)
3. Edit the existing variables or add new ones in the fields provided
4. Click the `Update` button next to each variable you modify
<Frame>
![Update Environment Variables](/images/enterprise/update-env-vars.png)
</Frame>
5. Finally, click the `Update Deployment` button at the bottom of the page to apply the changes
<Note>
Updating environment variables will trigger a new deployment, but this will only update the environment configuration and not the code itself.
</Note>
## After Updating
After performing any update:
1. The system will rebuild and redeploy your crew
2. You can monitor the deployment progress in real-time
3. Once complete, test your crew to ensure the changes are working as expected
<Tip>
If you encounter any issues after updating, you can view deployment logs in the platform or contact support for assistance.
</Tip>
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with updating your crew or troubleshooting deployment issues.
</Card>

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---
title: "Webhook Automation"
description: "Automate CrewAI Enterprise workflows using webhooks with platforms like ActivePieces, Zapier, and Make.com"
icon: "webhook"
---
CrewAI Enterprise allows you to automate your workflow using webhooks. This article will guide you through the process of setting up and using webhooks to kickoff your crew execution, with a focus on integration with ActivePieces, a workflow automation platform similar to Zapier and Make.com.
## Setting Up Webhooks
<Steps>
<Step title="Accessing the Kickoff Interface">
- Navigate to the CrewAI Enterprise dashboard
- Look for the `/kickoff` section, which is used to start the crew execution
<Frame>
<img src="/images/enterprise/kickoff-interface.png" alt="Kickoff Interface" />
</Frame>
</Step>
<Step title="Configuring the JSON Content">
In the JSON Content section, you'll need to provide the following information:
- **inputs**: A JSON object containing:
- `company`: The name of the company (e.g., "tesla")
- `product_name`: The name of the product (e.g., "crewai")
- `form_response`: The type of response (e.g., "financial")
- `icp_description`: A brief description of the Ideal Customer Profile
- `product_description`: A short description of the product
- `taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`: URLs for various webhook endpoints (ActivePieces, Zapier, Make.com or another compatible platform)
</Step>
<Step title="Integrating with ActivePieces">
In this example we will be using ActivePieces. You can use other platforms such as Zapier and Make.com
To integrate with ActivePieces:
1. Set up a new flow in ActivePieces
2. Add a trigger (e.g., `Every Day` schedule)
<Frame>
<img src="/images/enterprise/activepieces-trigger.png" alt="ActivePieces Trigger" />
</Frame>
3. Add an HTTP action step
- Set the action to `Send HTTP request`
- Use `POST` as the method
- Set the URL to your CrewAI Enterprise kickoff endpoint
- Add necessary headers (e.g., `Bearer Token`)
<Frame>
<img src="/images/enterprise/activepieces-headers.png" alt="ActivePieces Headers" />
</Frame>
- In the body, include the JSON content as configured in step 2
<Frame>
<img src="/images/enterprise/activepieces-body.png" alt="ActivePieces Body" />
</Frame>
- The crew will then kickoff at the pre-defined time.
</Step>
<Step title="Setting Up the Webhook">
1. Create a new flow in ActivePieces and name it
<Frame>
<img src="/images/enterprise/activepieces-flow.png" alt="ActivePieces Flow" />
</Frame>
2. Add a webhook step as the trigger:
- Select `Catch Webhook` as the trigger type
- This will generate a unique URL that will receive HTTP requests and trigger your flow
<Frame>
<img src="/images/enterprise/activepieces-webhook.png" alt="ActivePieces Webhook" />
</Frame>
- Configure the email to use crew webhook body text
<Frame>
<img src="/images/enterprise/activepieces-email.png" alt="ActivePieces Email" />
</Frame>
</Step>
</Steps>
## Webhook Output Examples
<Tabs>
<Tab title="Step Webhook">
`stepWebhookUrl` - Callback that will be executed upon each agent inner thought
```json
{
"action": "**Preliminary Research Report on the Financial Industry for crewai Enterprise Solution**\n1. Industry Overview and Trends\nThe financial industry in ....\nConclusion:\nThe financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
"task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0"
}
```
</Tab>
<Tab title="Task Webhook">
`taskWebhookUrl` - Callback that will be executed upon the end of each task
```json
{
"description": "Using the information gathered from the lead's data, conduct preliminary research on the lead's industry, company background, and potential use cases for crewai. Focus on finding relevant data that can aid in scoring the lead and planning a strategy to pitch them crewai.The financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
"task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0"
}
```
</Tab>
<Tab title="Crew Webhook">
`crewWebhookUrl` - Callback that will be executed upon the end of the crew execution
```json
{
"task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0",
"result": {
"lead_score": "Customer service enhancement, and compliance are particularly relevant.",
"talking_points": [
"Highlight how crewai's AI solutions can transform customer service with automated, personalized experiences and 24/7 support, improving both customer satisfaction and operational efficiency.",
"Discuss crewai's potential to help the institution achieve its sustainability goals through better data analysis and decision-making, contributing to responsible investing and green initiatives.",
"Emphasize crewai's ability to enhance compliance with evolving regulations through efficient data processing and reporting, reducing the risk of non-compliance penalties.",
"Stress the adaptability of crewai to support both extensive multinational operations and smaller, targeted projects, ensuring the solution grows with the institution's needs."
]
}
}
```
</Tab>
</Tabs>

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---
title: "Zapier Trigger"
description: "Trigger CrewAI crews from Zapier workflows to automate cross-app workflows"
icon: "bolt"
---
This guide will walk you through the process of setting up Zapier triggers for CrewAI Enterprise, allowing you to automate workflows between CrewAI Enterprise and other applications.
## Prerequisites
- A CrewAI Enterprise account
- A Zapier account
- A Slack account (for this specific example)
## Step-by-Step Setup
<Steps>
<Step title="Set Up the Slack Trigger">
- In Zapier, create a new Zap.
<Frame>
<img src="/images/enterprise/zapier-1.png" alt="Zapier 1" />
</Frame>
</Step>
<Step title="Choose Slack as your trigger app">
<Frame>
<img src="/images/enterprise/zapier-2.png" alt="Zapier 2" />
</Frame>
- Select `New Pushed Message` as the Trigger Event.
- Connect your Slack account if you haven't already.
</Step>
<Step title="Configure the CrewAI Enterprise Action">
- Add a new action step to your Zap.
- Choose CrewAI+ as your action app and Kickoff as the Action Event
<Frame>
<img src="/images/enterprise/zapier-3.png" alt="Zapier 5" />
</Frame>
</Step>
<Step title="Connect your CrewAI Enterprise account">
- Connect your CrewAI Enterprise account.
- Select the appropriate Crew for your workflow.
<Frame>
<img src="/images/enterprise/zapier-4.png" alt="Zapier 6" />
</Frame>
- Configure the inputs for the Crew using the data from the Slack message.
</Step>
<Step title="Format the CrewAI Enterprise Output">
- Add another action step to format the text output from CrewAI Enterprise.
- Use Zapier's formatting tools to convert the Markdown output to HTML.
<Frame>
<img src="/images/enterprise/zapier-5.png" alt="Zapier 8" />
</Frame>
<Frame>
<img src="/images/enterprise/zapier-6.png" alt="Zapier 9" />
</Frame>
</Step>
<Step title="Send the Output via Email">
- Add a final action step to send the formatted output via email.
- Choose your preferred email service (e.g., Gmail, Outlook).
- Configure the email details, including recipient, subject, and body.
- Insert the formatted CrewAI Enterprise output into the email body.
<Frame>
<img src="/images/enterprise/zapier-7.png" alt="Zapier 7" />
</Frame>
</Step>
<Step title="Kick Off the crew from Slack">
- Enter the text in your Slack channel
<Frame>
<img src="/images/enterprise/zapier-7b.png" alt="Zapier 10" />
</Frame>
- Select the 3 ellipsis button and then chose Push to Zapier
<Frame>
<img src="/images/enterprise/zapier-8.png" alt="Zapier 11" />
</Frame>
</Step>
<Step title="Select the crew and then Push to Kick Off">
<Frame>
<img src="/images/enterprise/zapier-9.png" alt="Zapier 12" />
</Frame>
</Step>
</Steps>
## Tips for Success
- Ensure that your CrewAI Enterprise inputs are correctly mapped from the Slack message.
- Test your Zap thoroughly before turning it on to catch any potential issues.
- Consider adding error handling steps to manage potential failures in the workflow.
By following these steps, you'll have successfully set up Zapier triggers for CrewAI Enterprise, allowing for automated workflows triggered by Slack messages and resulting in email notifications with CrewAI Enterprise output.

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---
title: "CrewAI Enterprise"
description: "Deploy, monitor, and scale your AI agent workflows"
icon: "globe"
---
## Introduction
CrewAI Enterprise provides a platform for deploying, monitoring, and scaling your crews and agents in a production environment.
<Frame>
<img src="/images/enterprise/crewai-enterprise-dashboard.png" alt="CrewAI Enterprise Dashboard" />
</Frame>
CrewAI Enterprise extends the power of the open-source framework with features designed for production deployments, collaboration, and scalability. Deploy your crews to a managed infrastructure and monitor their execution in real-time.
## Key Features
<CardGroup cols={2}>
<Card title="Crew Deployments" icon="rocket">
Deploy your crews to a managed infrastructure with a few clicks
</Card>
<Card title="API Access" icon="code">
Access your deployed crews via REST API for integration with existing systems
</Card>
<Card title="Observability" icon="chart-line">
Monitor your crews with detailed execution traces and logs
</Card>
<Card title="Tool Repository" icon="toolbox">
Publish and install tools to enhance your crews' capabilities
</Card>
<Card title="Webhook Streaming" icon="webhook">
Stream real-time events and updates to your systems
</Card>
<Card title="Crew Studio" icon="paintbrush">
Create and customize crews using a no-code/low-code interface
</Card>
</CardGroup>
## Deployment Options
<CardGroup cols={3}>
<Card title="GitHub Integration" icon="github">
Connect directly to your GitHub repositories to deploy code
</Card>
<Card title="Crew Studio" icon="palette">
Deploy crews created through the no-code Crew Studio interface
</Card>
<Card title="CLI Deployment" icon="terminal">
Use the CrewAI CLI for more advanced deployment workflows
</Card>
</CardGroup>
## Getting Started
<Steps>
<Step title="Sign up for an account">
Create your account at [app.crewai.com](https://app.crewai.com)
<Card
title="Sign Up"
icon="user"
href="https://app.crewai.com/signup"
>
Sign Up
</Card>
</Step>
<Step title="Build your first crew">
Use code or Crew Studio to build your crew
<Card
title="Build Crew"
icon="paintbrush"
href="/enterprise/guides/build-crew"
>
Build Crew
</Card>
</Step>
<Step title="Deploy your crew">
Deploy your crew to the Enterprise platform
<Card
title="Deploy Crew"
icon="rocket"
href="/enterprise/guides/deploy-crew"
>
Deploy Crew
</Card>
</Step>
<Step title="Access your crew">
Integrate with your crew via the generated API endpoints
<Card
title="API Access"
icon="code"
href="/enterprise/guides/use-crew-api"
>
Use the Crew API
</Card>
</Step>
</Steps>
For detailed instructions, check out our [deployment guide](/enterprise/guides/deploy-crew) or click the button below to get started.

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---
title: FAQs
description: "Frequently asked questions about CrewAI Enterprise"
icon: "circle-question"
---
<AccordionGroup>
<Accordion title="How is task execution handled in the hierarchical process?">
In the hierarchical process, a manager agent is automatically created and coordinates the workflow, delegating tasks and validating outcomes for streamlined and effective execution. The manager agent utilizes tools to facilitate task delegation and execution by agents under the manager's guidance. The manager LLM is crucial for the hierarchical process and must be set up correctly for proper function.
</Accordion>
<Accordion title="Where can I get the latest CrewAI documentation?">
The most up-to-date documentation for CrewAI is available on our official documentation website: https://docs.crewai.com/
<Card href="https://docs.crewai.com/" icon="books">CrewAI Docs</Card>
</Accordion>
<Accordion title="What are the key differences between Hierarchical and Sequential Processes in CrewAI?">
#### Hierarchical Process:
- Tasks are delegated and executed based on a structured chain of command
- A manager language model (`manager_llm`) must be specified for the manager agent
- Manager agent oversees task execution, planning, delegation, and validation
- Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities
#### Sequential Process:
- Tasks are executed one after another, ensuring tasks are completed in an orderly progression
- Output of one task serves as context for the next
- Task execution follows the predefined order in the task list
#### Which Process is Better for Complex Projects?
The hierarchical process is better suited for complex projects because it allows for:
- **Dynamic task allocation and delegation**: Manager agent can assign tasks based on agent capabilities
- **Structured validation and oversight**: Manager agent reviews task outputs and ensures completion
- **Complex task management**: Precise control over tool availability at the agent level
</Accordion>
<Accordion title="What are the benefits of using memory in the CrewAI framework?">
- **Adaptive Learning**: Crews become more efficient over time, adapting to new information and refining their approach to tasks
- **Enhanced Personalization**: Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences
- **Improved Problem Solving**: Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights
</Accordion>
<Accordion title="What is the purpose of setting a maximum RPM limit for an agent?">
Setting a maximum RPM limit for an agent prevents the agent from making too many requests to external services, which can help to avoid rate limits and improve performance.
</Accordion>
<Accordion title="What role does human input play in the execution of tasks within a CrewAI crew?">
Human input allows agents to request additional information or clarification when necessary. This feature is crucial in complex decision-making processes or when agents require more details to complete a task effectively.
To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.
For detailed implementation guidance, see our [Human-in-the-Loop guide](/how-to/human-in-the-loop).
</Accordion>
<Accordion title="What advanced customization options are available for tailoring and enhancing agent behavior and capabilities in CrewAI?">
CrewAI provides a range of advanced customization options:
- **Language Model Customization**: Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`)
- **Performance and Debugging Settings**: Adjust an agent's performance and monitor its operations
- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization
- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`)
- **Maximum Iterations**: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task
- **Delegation and Autonomy**: Control an agent's ability to delegate or ask questions with the `allow_delegation` attribute (default: True)
- **Human Input Integration**: Agents can request additional information or clarification when necessary
</Accordion>
<Accordion title="In what scenarios is human input particularly useful in agent execution?">
Human input is particularly useful when:
- **Agents require additional information or clarification**: When agents encounter ambiguity or incomplete data
- **Agents need to make complex or sensitive decisions**: Human input can assist in ethical or nuanced decision-making
- **Oversight and validation of agent output**: Human input can help validate results and prevent errors
- **Customizing agent behavior**: Human input can provide feedback to refine agent responses over time
- **Identifying and resolving errors or limitations**: Human input helps address agent capability gaps
</Accordion>
<Accordion title="What are the different types of memory that are available in crewAI?">
The different types of memory available in CrewAI are:
- **Short-term memory**: Temporary storage for immediate context
- **Long-term memory**: Persistent storage for learned patterns and information
- **Entity memory**: Focused storage for specific entities and their attributes
- **Contextual memory**: Memory that maintains context across interactions
Learn more about the different types of memory:
<Card href="https://docs.crewai.com/concepts/memory" icon="brain">CrewAI Memory</Card>
</Accordion>
<Accordion title="How do I use Output Pydantic in a Task?">
To use Output Pydantic in a task, you need to define the expected output of the task as a Pydantic model. Here's a quick example:
<Steps>
<Step title="Define a Pydantic model">
```python
from pydantic import BaseModel
class User(BaseModel):
name: str
age: int
```
</Step>
<Step title="Create a task with Output Pydantic">
```python
from crewai import Task, Crew, Agent
from my_models import User
task = Task(
description="Create a user with the provided name and age",
expected_output=User, # This is the Pydantic model
agent=agent,
tools=[tool1, tool2]
)
```
</Step>
<Step title="Set the output_pydantic attribute in your agent">
```python
from crewai import Agent
from my_models import User
agent = Agent(
role='User Creator',
goal='Create users',
backstory='I am skilled in creating user accounts',
tools=[tool1, tool2],
output_pydantic=User
)
```
</Step>
</Steps>
Here's a tutorial on how to consistently get structured outputs from your agents:
<Frame>
<iframe
height="400"
width="100%"
src="https://www.youtube.com/embed/dNpKQk5uxHw"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
</Frame>
</Accordion>
<Accordion title="How can I create custom tools for my CrewAI agents?">
You can create custom tools by subclassing the `BaseTool` class provided by CrewAI or by using the tool decorator. Subclassing involves defining a new class that inherits from `BaseTool`, specifying the name, description, and the `_run` method for operational logic. The tool decorator allows you to create a `Tool` object directly with the required attributes and a functional logic.
<Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
</Accordion>
<Accordion title="How can you control the maximum number of requests per minute that the entire crew can perform?">
The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
</Accordion>
</AccordionGroup>