mirror of
https://github.com/crewAIInc/crewAI.git
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docs: add CrewAI Enterprise docs (#2691)
* Add enterprise deployment documentation to CLI docs * Update CrewAI Enterprise documentation with comprehensive guides for Traces, Tool Repository, Webhook Streaming, and FAQ structure * Add Enterprise documentation images * Update Enterprise introduction with visual CardGroups and Steps components
This commit is contained in:
Tony Kipkemboi
@@ -180,6 +180,42 @@
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}
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]
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},
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{
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"tab": "Enterprise",
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"groups": [
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{
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"group": "Getting Started",
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"pages": [
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"enterprise/introduction"
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]
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},
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{
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"group": "How-To Guides",
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"pages": [
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"enterprise/guides/build-crew",
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"enterprise/guides/deploy-crew",
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"enterprise/guides/kickoff-crew",
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"enterprise/guides/update-crew",
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"enterprise/guides/use-crew-api",
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"enterprise/guides/enable-crew-studio"
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]
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},
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{
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"group": "Features",
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"pages": [
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"enterprise/features/tool-repository",
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"enterprise/features/webhook-streaming",
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"enterprise/features/traces"
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]
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},
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{
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"group": "Resources",
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"pages": [
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"enterprise/resources/frequently-asked-questions"
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]
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}
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]
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},
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{
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"tab": "Examples",
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"groups": [
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106
docs/enterprise/features/tool-repository.mdx
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106
docs/enterprise/features/tool-repository.mdx
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@@ -0,0 +1,106 @@
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---
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title: Tool Repository
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description: "Using the Tool Repository to manage your tools"
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icon: "toolbox"
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---
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## Overview
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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.
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Tools can be:
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- **Private**: accessible only within your organization (default)
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- **Public**: accessible to all CrewAI users if published with the `--public` flag
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The repository is not a version control system. Use Git to track code changes and enable collaboration.
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## Prerequisites
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Before using the Tool Repository, ensure you have:
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- A [CrewAI Enterprise](https://app.crewai.com) account
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- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed
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- [Git](https://git-scm.com) installed and configured
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- Access permissions to publish or install tools in your CrewAI Enterprise organization
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## Installing Tools
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To install a tool:
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```bash
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crewai tool install <tool-name>
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```
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This installs the tool and adds it to `pyproject.toml`.
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## Creating and Publishing Tools
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To create a new tool project:
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```bash
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crewai tool create <tool-name>
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```
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This generates a scaffolded tool project locally.
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After making changes, initialize a Git repository and commit the code:
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```bash
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git init
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git add .
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git commit -m "Initial version"
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```
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To publish the tool:
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```bash
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crewai tool publish
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```
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By default, tools are published as private. To make a tool public:
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```bash
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crewai tool publish --public
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```
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For more details on how to build tools, see [Creating your own tools](https://docs.crewai.com/concepts/tools#creating-your-own-tools).
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## Updating Tools
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To update a published tool:
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1. Modify the tool locally
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2. Update the version in `pyproject.toml` (e.g., from `0.1.0` to `0.1.1`)
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3. Commit the changes and publish
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```bash
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git commit -m "Update version to 0.1.1"
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crewai tool publish
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```
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## Deleting Tools
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To delete a tool:
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1. Go to [CrewAI Enterprise](https://app.crewai.com)
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2. Navigate to **Tools**
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3. Select the tool
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4. Click **Delete**
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<Warning>
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Deletion is permanent. Deleted tools cannot be restored or re-installed.
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</Warning>
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## Security Checks
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Every published version undergoes automated security checks, and are only available to install after they pass.
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You can check the security check status of a tool at:
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`CrewAI Enterprise > Tools > Your Tool > Versions`
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<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
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Contact our support team for assistance with API integration or troubleshooting.
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</Card>
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146
docs/enterprise/features/traces.mdx
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146
docs/enterprise/features/traces.mdx
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---
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title: Traces
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description: "Using Traces to monitor your Crews"
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icon: "timeline"
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---
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## Overview
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Traces provide comprehensive visibility into your crew executions, helping you monitor performance, debug issues, and optimize your AI agent workflows.
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## What are Traces?
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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:
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- Agent thoughts and reasoning
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- Task execution details
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- Tool usage and outputs
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- Token consumption metrics
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- Execution times
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- Cost estimates
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<Frame>
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</Frame>
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## Accessing Traces
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<Steps>
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<Step title="Navigate to the Traces Tab">
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Once in your CrewAI Enterprise dashboard, click on the **Traces** to view all execution records.
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</Step>
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<Step title="Select an Execution">
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You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.
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</Step>
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</Steps>
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## Understanding the Trace Interface
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The trace interface is divided into several sections, each providing different insights into your crew's execution:
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### 1. Execution Summary
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The top section displays high-level metrics about the execution:
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- **Total Tokens**: Number of tokens consumed across all tasks
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- **Prompt Tokens**: Tokens used in prompts to the LLM
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- **Completion Tokens**: Tokens generated in LLM responses
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- **Requests**: Number of API calls made
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- **Execution Time**: Total duration of the crew run
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- **Estimated Cost**: Approximate cost based on token usage
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<Frame>
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</Frame>
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### 2. Tasks & Agents
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This section shows all tasks and agents that were part of the crew execution:
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- Task name and agent assignment
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- Agents and LLMs used for each task
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- Status (completed/failed)
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- Individual execution time of the task
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<Frame>
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</Frame>
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### 3. Final Output
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Displays the final result produced by the crew after all tasks are completed.
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<Frame>
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</Frame>
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### 4. Execution Timeline
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A visual representation of when each task started and ended, helping you identify bottlenecks or parallel execution patterns.
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<Frame>
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</Frame>
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### 5. Detailed Task View
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When you click on a specific task in the timeline or task list, you'll see:
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<Frame>
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</Frame>
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- **Task Key**: Unique identifier for the task
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- **Task ID**: Technical identifier in the system
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- **Status**: Current state (completed/running/failed)
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- **Agent**: Which agent performed the task
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- **LLM**: Language model used for this task
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- **Start/End Time**: When the task began and completed
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- **Execution Time**: Duration of this specific task
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- **Task Description**: What the agent was instructed to do
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- **Expected Output**: What output format was requested
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- **Input**: Any input provided to this task from previous tasks
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- **Output**: The actual result produced by the agent
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## Using Traces for Debugging
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Traces are invaluable for troubleshooting issues with your crews:
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<Steps>
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<Step title="Identify Failure Points">
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When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for:
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- Failed tasks
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- Unexpected agent decisions
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- Tool usage errors
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- Misinterpreted instructions
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<Frame>
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</Frame>
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</Step>
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<Step title="Optimize Performance">
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Use execution metrics to identify performance bottlenecks:
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- Tasks that took longer than expected
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- Excessive token usage
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- Redundant tool operations
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- Unnecessary API calls
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</Step>
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<Step title="Improve Cost Efficiency">
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Analyze token usage and cost estimates to optimize your crew's efficiency:
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- Consider using smaller models for simpler tasks
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- Refine prompts to be more concise
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- Cache frequently accessed information
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- Structure tasks to minimize redundant operations
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</Step>
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</Steps>
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<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
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Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.
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</Card>
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82
docs/enterprise/features/webhook-streaming.mdx
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82
docs/enterprise/features/webhook-streaming.mdx
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@@ -0,0 +1,82 @@
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---
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title: Webhook Streaming
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description: "Using Webhook Streaming to stream events to your webhook"
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icon: "webhook"
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---
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## Overview
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Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to
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CrewAI Enterprise, such as model calls, tool usage, and flow steps.
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## Usage
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When using the Kickoff API, include a `webhooks` object to your request, for example:
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```json
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{
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"inputs": {"foo": "bar"},
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"webhooks": {
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"events": ["crew_kickoff_started", "llm_call_started"],
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"url": "https://your.endpoint/webhook",
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"realtime": false,
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"authentication": {
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"strategy": "bearer",
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"token": "my-secret-token"
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}
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}
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}
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```
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If `realtime` is set to `true`, each event is delivered individually and immediately, at the cost of crew/flow performance.
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## Webhook Format
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Each webhook sends a list of events:
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```json
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{
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"events": [
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{
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"id": "event-id",
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"execution_id": "crew-run-id",
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"timestamp": "2025-02-16T10:58:44.965Z",
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"type": "llm_call_started",
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"data": {
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"model": "gpt-4",
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"messages": [
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{"role": "system", "content": "You are an assistant."},
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{"role": "user", "content": "Summarize this article."}
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]
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}
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}
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]
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}
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```
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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.
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As requests are sent over HTTP, the order of events can't be guaranteed. If you need ordering, use the `timestamp` field.
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## Supported Events
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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.
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- `crew_kickoff_started`
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- `crew_step_started`
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- `crew_step_completed`
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- `crew_execution_completed`
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- `llm_call_started`
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- `llm_call_completed`
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- `tool_usage_started`
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- `tool_usage_completed`
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- `crew_test_failed`
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- *...and others*
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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.
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You can emit your own custom events, and they will be delivered through the webhook stream alongside system events.
|
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|
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<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
|
||||
Contact our support team for assistance with webhook integration or troubleshooting.
|
||||
</Card>
|
||||
43
docs/enterprise/guides/build-crew.mdx
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43
docs/enterprise/guides/build-crew.mdx
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@@ -0,0 +1,43 @@
|
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---
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title: "Build Crew"
|
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description: "A Crew is a group of agents that work together to complete a task."
|
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icon: "people-arrows"
|
||||
---
|
||||
|
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<Tip>
|
||||
[CrewAI Enterprise](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
|
||||
</Tip>
|
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|
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## Getting Started
|
||||
|
||||
<iframe
|
||||
width="100%"
|
||||
height="400"
|
||||
src="https://www.youtube.com/embed/d1Yp8eeknDk?si=tIxnTRI5UlyCp3z_"
|
||||
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>
|
||||
216
docs/enterprise/guides/deploy-crew.mdx
Normal file
216
docs/enterprise/guides/deploy-crew.mdx
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@@ -0,0 +1,216 @@
|
||||
---
|
||||
title: "Deploy Crew"
|
||||
description: "Deploy your local CrewAI project to the Enterprise platform"
|
||||
icon: "cloud-arrow-up"
|
||||
---
|
||||
|
||||
## Option 1: CLI Deployment
|
||||
|
||||
<Tip>
|
||||
This video tutorial walks you through the process of deploying your locally developed CrewAI project to the CrewAI Enterprise platform,
|
||||
transforming it into a production-ready API endpoint.
|
||||
</Tip>
|
||||
|
||||
<iframe
|
||||
width="100%"
|
||||
height="400"
|
||||
src="https://www.youtube.com/embed/3EqSV-CYDZA"
|
||||
title="Deploying a Crew to CrewAI Enterprise"
|
||||
frameborder="0"
|
||||
style={{ borderRadius: '10px' }}
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
|
||||
allowfullscreen
|
||||
></iframe>
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Before starting the deployment process, make sure you have:
|
||||
|
||||
- A CrewAI project built locally ([follow our quickstart guide](/quickstart) if you haven't created one yet)
|
||||
- Your code pushed to a GitHub repository
|
||||
- The latest version of the CrewAI CLI installed (`uv tool install crewai`)
|
||||
|
||||
<Note>
|
||||
For a quick reference project, you can clone our example repository at [github.com/tonykipkemboi/crewai-latest-ai-development](https://github.com/tonykipkemboi/crewai-latest-ai-development).
|
||||
</Note>
|
||||
|
||||
|
||||
|
||||
### Step 1: 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 2: 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 3: 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>
|
||||
|
||||
### 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.
|
||||
|
||||
### Step 1: Pushing to GitHub
|
||||
|
||||
First, 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 2: Connecting GitHub to CrewAI Enterprise
|
||||
|
||||
1. Log in to [CrewAI Enterprise](https://app.crewai.com)
|
||||
2. Click on the button "Connect GitHub"
|
||||
|
||||
<Frame>
|
||||
|
||||
</Frame>
|
||||
|
||||
### Step 3: Select the Repository
|
||||
|
||||
After connecting your GitHub account, you'll be able to select which repository to deploy:
|
||||
|
||||
<Frame>
|
||||
|
||||
</Frame>
|
||||
|
||||
### Step 4: 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>
|
||||
|
||||
</Frame>
|
||||
|
||||
### Step 5: 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>
|
||||
|
||||
</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
|
||||
|
||||
### 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>
|
||||
166
docs/enterprise/guides/enable-crew-studio.mdx
Normal file
166
docs/enterprise/guides/enable-crew-studio.mdx
Normal file
@@ -0,0 +1,166 @@
|
||||
---
|
||||
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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
186
docs/enterprise/guides/kickoff-crew.mdx
Normal file
186
docs/enterprise/guides/kickoff-crew.mdx
Normal file
@@ -0,0 +1,186 @@
|
||||
---
|
||||
title: "Kickoff Crew"
|
||||
description: "Kickoff a Crew on CrewAI Enterprise"
|
||||
icon: "flag-checkered"
|
||||
---
|
||||
|
||||
# Kickoff a Crew on CrewAI Enterprise
|
||||
|
||||
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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
89
docs/enterprise/guides/update-crew.mdx
Normal file
89
docs/enterprise/guides/update-crew.mdx
Normal file
@@ -0,0 +1,89 @@
|
||||
---
|
||||
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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
</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>
|
||||
|
||||
319
docs/enterprise/guides/use-crew-api.mdx
Normal file
319
docs/enterprise/guides/use-crew-api.mdx
Normal file
@@ -0,0 +1,319 @@
|
||||
---
|
||||
title: "Trigger Deployed Crew API"
|
||||
description: "Using your deployed crew's API on CrewAI Enterprise"
|
||||
icon: "arrow-up-right-from-square"
|
||||
---
|
||||
|
||||
Once you have deployed your crew to CrewAI Enterprise, it automatically becomes available as a REST API. This guide explains how to interact with your crew programmatically.
|
||||
|
||||
## API Basics
|
||||
|
||||
Your deployed crew exposes several endpoints that allow you to:
|
||||
1. Discover required inputs
|
||||
2. Start crew executions
|
||||
3. Monitor execution status
|
||||
4. Receive results
|
||||
|
||||
### Authentication
|
||||
|
||||
All API requests require a bearer token for authentication, which is generated when you deploy your crew:
|
||||
|
||||
```bash
|
||||
curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com/...
|
||||
```
|
||||
|
||||
<Tip>
|
||||
You can find your bearer token in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
|
||||
</Tip>
|
||||
|
||||
<Frame>
|
||||
|
||||
</Frame>
|
||||
|
||||
## Available Endpoints
|
||||
|
||||
Your crew API provides three main endpoints:
|
||||
|
||||
| Endpoint | Method | Description |
|
||||
|----------|--------|-------------|
|
||||
| `/inputs` | GET | Lists all required inputs for crew execution |
|
||||
| `/kickoff` | POST | Starts a crew execution with provided inputs |
|
||||
| `/status/{kickoff_id}` | GET | Retrieves the status and results of an execution |
|
||||
|
||||
## GET /inputs
|
||||
|
||||
The inputs endpoint allows you to discover what parameters your crew requires:
|
||||
|
||||
```bash
|
||||
curl -X GET \
|
||||
-H "Authorization: Bearer YOUR_CREW_TOKEN" \
|
||||
https://your-crew-url.crewai.com/inputs
|
||||
```
|
||||
|
||||
### Response
|
||||
|
||||
```json
|
||||
{
|
||||
"inputs": ["budget", "interests", "duration", "age"]
|
||||
}
|
||||
```
|
||||
|
||||
This response indicates that your crew expects four input parameters: `budget`, `interests`, `duration`, and `age`.
|
||||
|
||||
## POST /kickoff
|
||||
|
||||
The kickoff endpoint starts a new crew execution:
|
||||
|
||||
```bash
|
||||
curl -X POST \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "Authorization: Bearer YOUR_CREW_TOKEN" \
|
||||
-d '{
|
||||
"inputs": {
|
||||
"budget": "1000 USD",
|
||||
"interests": "games, tech, ai, relaxing hikes, amazing food",
|
||||
"duration": "7 days",
|
||||
"age": "35"
|
||||
}
|
||||
}' \
|
||||
https://your-crew-url.crewai.com/kickoff
|
||||
```
|
||||
|
||||
### Request Parameters
|
||||
|
||||
| Parameter | Type | Required | Description |
|
||||
|-----------|------|----------|-------------|
|
||||
| `inputs` | Object | Yes | Key-value pairs of all required inputs |
|
||||
| `meta` | Object | No | Additional metadata to pass to the crew |
|
||||
| `taskWebhookUrl` | String | No | Callback URL executed after each task |
|
||||
| `stepWebhookUrl` | String | No | Callback URL executed after each agent thought |
|
||||
| `crewWebhookUrl` | String | No | Callback URL executed when the crew finishes |
|
||||
|
||||
### Example with Webhooks
|
||||
|
||||
```json
|
||||
{
|
||||
"inputs": {
|
||||
"budget": "1000 USD",
|
||||
"interests": "games, tech, ai, relaxing hikes, amazing food",
|
||||
"duration": "7 days",
|
||||
"age": "35"
|
||||
},
|
||||
"meta": {
|
||||
"requestId": "user-request-12345",
|
||||
"source": "mobile-app"
|
||||
},
|
||||
"taskWebhookUrl": "https://your-server.com/webhooks/task",
|
||||
"stepWebhookUrl": "https://your-server.com/webhooks/step",
|
||||
"crewWebhookUrl": "https://your-server.com/webhooks/crew"
|
||||
}
|
||||
```
|
||||
|
||||
### Response
|
||||
|
||||
```json
|
||||
{
|
||||
"kickoff_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv"
|
||||
}
|
||||
```
|
||||
|
||||
The `kickoff_id` is used to track and retrieve the execution results.
|
||||
|
||||
## GET /status/{kickoff_id}
|
||||
|
||||
The status endpoint allows you to check the progress and results of a crew execution:
|
||||
|
||||
```bash
|
||||
curl -X GET \
|
||||
-H "Authorization: Bearer YOUR_CREW_TOKEN" \
|
||||
https://your-crew-url.crewai.com/status/abcd1234-5678-90ef-ghij-klmnopqrstuv
|
||||
```
|
||||
|
||||
### Response Structure
|
||||
|
||||
The response structure will vary depending on the execution state:
|
||||
|
||||
#### In Progress
|
||||
|
||||
```json
|
||||
{
|
||||
"status": "running",
|
||||
"current_task": "research_task",
|
||||
"progress": {
|
||||
"completed_tasks": 0,
|
||||
"total_tasks": 2
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Completed
|
||||
|
||||
```json
|
||||
{
|
||||
"status": "completed",
|
||||
"result": {
|
||||
"output": "Comprehensive travel itinerary...",
|
||||
"tasks": [
|
||||
{
|
||||
"task_id": "research_task",
|
||||
"output": "Research findings...",
|
||||
"agent": "Researcher",
|
||||
"execution_time": 45.2
|
||||
},
|
||||
{
|
||||
"task_id": "planning_task",
|
||||
"output": "7-day itinerary plan...",
|
||||
"agent": "Trip Planner",
|
||||
"execution_time": 62.8
|
||||
}
|
||||
]
|
||||
},
|
||||
"execution_time": 108.5
|
||||
}
|
||||
```
|
||||
|
||||
## Webhook Integration
|
||||
|
||||
When you provide webhook URLs in your kickoff request, the system will make POST requests to those URLs at specific points in the execution:
|
||||
|
||||
### taskWebhookUrl
|
||||
|
||||
Called when each task completes:
|
||||
|
||||
```json
|
||||
{
|
||||
"kickoff_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
|
||||
"task_id": "research_task",
|
||||
"status": "completed",
|
||||
"output": "Research findings...",
|
||||
"agent": "Researcher",
|
||||
"execution_time": 45.2
|
||||
}
|
||||
```
|
||||
|
||||
### stepWebhookUrl
|
||||
|
||||
Called after each agent thought or action:
|
||||
|
||||
```json
|
||||
{
|
||||
"kickoff_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
|
||||
"task_id": "research_task",
|
||||
"agent": "Researcher",
|
||||
"step_type": "thought",
|
||||
"content": "I should first search for popular destinations that match these interests..."
|
||||
}
|
||||
```
|
||||
|
||||
### crewWebhookUrl
|
||||
|
||||
Called when the entire crew execution completes:
|
||||
|
||||
```json
|
||||
{
|
||||
"kickoff_id": "abcd1234-5678-90ef-ghij-klmnopqrstuv",
|
||||
"status": "completed",
|
||||
"result": {
|
||||
"output": "Comprehensive travel itinerary...",
|
||||
"tasks": [
|
||||
{
|
||||
"task_id": "research_task",
|
||||
"output": "Research findings...",
|
||||
"agent": "Researcher",
|
||||
"execution_time": 45.2
|
||||
},
|
||||
{
|
||||
"task_id": "planning_task",
|
||||
"output": "7-day itinerary plan...",
|
||||
"agent": "Trip Planner",
|
||||
"execution_time": 62.8
|
||||
}
|
||||
]
|
||||
},
|
||||
"execution_time": 108.5,
|
||||
"meta": {
|
||||
"requestId": "user-request-12345",
|
||||
"source": "mobile-app"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Handling Long-Running Executions
|
||||
|
||||
Crew executions can take anywhere from seconds to minutes depending on their complexity. Consider these approaches:
|
||||
|
||||
1. **Webhooks (Recommended)**: Set up webhook endpoints to receive notifications when the execution completes
|
||||
2. **Polling**: Implement a polling mechanism with exponential backoff
|
||||
3. **Client-Side Timeout**: Set appropriate timeouts for your API requests
|
||||
|
||||
### Error Handling
|
||||
|
||||
The API may return various error codes:
|
||||
|
||||
| Code | Description | Recommended Action |
|
||||
|------|-------------|-------------------|
|
||||
| 401 | Unauthorized | Check your bearer token |
|
||||
| 404 | Not Found | Verify your crew URL and kickoff_id |
|
||||
| 422 | Validation Error | Ensure all required inputs are provided |
|
||||
| 500 | Server Error | Contact support with the error details |
|
||||
|
||||
### Sample Code
|
||||
|
||||
Here's a complete Python example for interacting with your crew API:
|
||||
|
||||
```python
|
||||
import requests
|
||||
import time
|
||||
|
||||
# Configuration
|
||||
CREW_URL = "https://your-crew-url.crewai.com"
|
||||
BEARER_TOKEN = "your-crew-token"
|
||||
HEADERS = {
|
||||
"Authorization": f"Bearer {BEARER_TOKEN}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
# 1. Get required inputs
|
||||
response = requests.get(f"{CREW_URL}/inputs", headers=HEADERS)
|
||||
required_inputs = response.json()["inputs"]
|
||||
print(f"Required inputs: {required_inputs}")
|
||||
|
||||
# 2. Start crew execution
|
||||
payload = {
|
||||
"inputs": {
|
||||
"budget": "1000 USD",
|
||||
"interests": "games, tech, ai, relaxing hikes, amazing food",
|
||||
"duration": "7 days",
|
||||
"age": "35"
|
||||
}
|
||||
}
|
||||
|
||||
response = requests.post(f"{CREW_URL}/kickoff", headers=HEADERS, json=payload)
|
||||
kickoff_id = response.json()["kickoff_id"]
|
||||
print(f"Execution started with ID: {kickoff_id}")
|
||||
|
||||
# 3. Poll for results
|
||||
MAX_RETRIES = 30
|
||||
POLL_INTERVAL = 10 # seconds
|
||||
for i in range(MAX_RETRIES):
|
||||
print(f"Checking status (attempt {i+1}/{MAX_RETRIES})...")
|
||||
response = requests.get(f"{CREW_URL}/status/{kickoff_id}", headers=HEADERS)
|
||||
data = response.json()
|
||||
|
||||
if data["status"] == "completed":
|
||||
print("Execution completed!")
|
||||
print(f"Result: {data['result']['output']}")
|
||||
break
|
||||
elif data["status"] == "error":
|
||||
print(f"Execution failed: {data.get('error', 'Unknown error')}")
|
||||
break
|
||||
else:
|
||||
print(f"Status: {data['status']}, waiting {POLL_INTERVAL} seconds...")
|
||||
time.sleep(POLL_INTERVAL)
|
||||
```
|
||||
|
||||
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
|
||||
Contact our support team for assistance with API integration or troubleshooting.
|
||||
</Card>
|
||||
67
docs/enterprise/introduction.mdx
Normal file
67
docs/enterprise/introduction.mdx
Normal file
@@ -0,0 +1,67 @@
|
||||
---
|
||||
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.
|
||||
|
||||
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)
|
||||
</Step>
|
||||
<Step title="Create your first crew">
|
||||
Use code or Crew Studio to create your crew
|
||||
</Step>
|
||||
<Step title="Deploy your crew">
|
||||
Deploy your crew to the Enterprise platform
|
||||
</Step>
|
||||
<Step title="Access your crew">
|
||||
Integrate with your crew via the generated API endpoints
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
For detailed instructions, check out our [deployment guide](/enterprise/guides/deploy-crew) or click the button below to get started.
|
||||
181
docs/enterprise/resources/frequently-asked-questions.mdx
Normal file
181
docs/enterprise/resources/frequently-asked-questions.mdx
Normal file
@@ -0,0 +1,181 @@
|
||||
---
|
||||
title: FAQs
|
||||
description: "Frequently asked questions about CrewAI Enterprise"
|
||||
icon: "code"
|
||||
---
|
||||
|
||||
<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 Suited 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, allowing for efficient resource utilization.
|
||||
- **Structured validation and oversight**: Manager agent reviews task outputs and ensures task completion, increasing reliability and accuracy.
|
||||
- **Complex task management**: Assigning tools at the agent level allows for precise control over tool availability, facilitating the execution of intricate tasks.
|
||||
</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?">
|
||||
It 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.
|
||||
</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 to tailor and enhance agent behavior and capabilities:
|
||||
|
||||
- **Language Model Customization**: Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`), offering advanced control over their processing and decision-making abilities.
|
||||
|
||||
- **Performance and Debugging Settings**: Adjust an agent's performance and monitor its operations for efficient task execution.
|
||||
|
||||
- **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 for Task Execution**: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions.
|
||||
|
||||
- **Delegation and Autonomy**: Control an agent's ability to delegate or ask questions, tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default, the `allow_delegation` attribute is set to True, enabling agents to seek assistance or delegate tasks as needed. This default behavior promotes collaborative problem-solving and efficiency within the CrewAI ecosystem. If needed, delegation can be disabled to suit specific operational requirements.
|
||||
|
||||
- **Human Input in Agent Execution**: Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary. This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="In what scenarios is human input particularly useful in agent execution?">
|
||||
Human input is particularly useful in agent execution when:
|
||||
- **Agents require additional information or clarification**: When agents encounter ambiguity or incomplete data, human input can provide the necessary context to complete the task effectively.
|
||||
- **Agents need to make complex or sensitive decisions**: Human input can assist agents in ethical or nuanced decision-making, ensuring responsible and informed outcomes.
|
||||
- **Oversight and validation of agent output**: Human input can help validate the results generated by agents, ensuring accuracy and preventing any misinterpretation or errors.
|
||||
- **Customizing agent behavior**: Human input can provide feedback on agent responses, allowing users to refine the agent's behavior and responses over time.
|
||||
- **Identifying and resolving errors or limitations**: Human input can help identify and address any errors or limitations in the agent's capabilities, enabling continuous improvement and optimization.
|
||||
</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`
|
||||
- `long-term memory`
|
||||
- `entity memory`
|
||||
- `contextual memory`
|
||||
|
||||
Learn more about the different types of memory here:
|
||||
<Card href="https://docs.crewai.com/concepts/memory" icon="brain">CrewAI Memory</Card>
|
||||
</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.
|
||||
Click here for more details:
|
||||
<Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI Tools</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 an example:
|
||||
<Steps>
|
||||
<Step title="Define a Pydantic model">
|
||||
First, you need to define a Pydantic model. For instance, let's create a simple model for a user:
|
||||
|
||||
```python
|
||||
from pydantic import BaseModel
|
||||
|
||||
class User(BaseModel):
|
||||
name: str
|
||||
age: int
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Then, when creating a task, specify the expected output as this Pydantic model:">
|
||||
|
||||
```python
|
||||
from crewai import Task, Crew, Agent
|
||||
|
||||
# Import the User model
|
||||
from my_models import User
|
||||
|
||||
# Create a task with Output Pydantic
|
||||
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="In your agent, make sure to set the output_pydantic attribute to the Pydantic model you're using:">
|
||||
|
||||
```python
|
||||
from crewai import Agent
|
||||
|
||||
# Import the User model
|
||||
from my_models import User
|
||||
|
||||
# Create an agent with Output Pydantic
|
||||
agent = Agent(
|
||||
role='User Creator',
|
||||
goal='Create users',
|
||||
backstory='I am skilled in creating user accounts',
|
||||
tools=[tool1, tool2],
|
||||
output_pydantic=User
|
||||
)
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="When executing the crew, the output of the task will be a User object:">
|
||||
|
||||
```python
|
||||
from crewai import Crew
|
||||
|
||||
# Create a crew with the agent and task
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
|
||||
# Kick off the crew
|
||||
result = crew.kickoff()
|
||||
|
||||
# The output of the task will be a User object
|
||||
print(result.tasks[0].output)
|
||||
```
|
||||
</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>
|
||||
</AccordionGroup>
|
||||
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Reference in New Issue
Block a user