andre/crewAI
1
0
Fork 0
mirror of https://github.com/crewAIInc/crewAI.git synced 2025-12-16 04:18:35 +00:00
Code Issues Actions 4 Packages Projects Releases Wiki Activity

Merge branch 'main' into devin/1763567753-fix-task-planner-ordering

Browse Source
This commit is contained in:
Greyson LaLonde
2025-11-29 10:58:07 -05:00
committed by GitHub
parent 4e0ee7d570 bc4e6a3127
commit 93116895d9
327 changed files with 23789 additions and 3099 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
README.md
View File

@@ -62,9 +62,9 @@
With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
standard for enterprise-ready AI automation.
# CrewAI AMP Suite
# CrewAI AOP Suite
CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
CrewAI AOP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
@@ -76,9 +76,9 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
- **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
- **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
- **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
- **On-premise and Cloud Deployment Options**: Deploy CrewAI AMP on-premise or in the cloud, depending on your security and compliance requirements.
- **On-premise and Cloud Deployment Options**: Deploy CrewAI AOP on-premise or in the cloud, depending on your security and compliance requirements.
CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
CrewAI AOP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
intelligent automations.
## Table of contents
@@ -674,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
### Enterprise Features
- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-amp-offer)
- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free)
- [What additional features does CrewAI AOP offer?](#q-what-additional-features-does-crewai-amp-offer)
- [Is CrewAI AOP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
- [Can I try CrewAI AOP for free?](#q-can-i-try-crewai-amp-for-free)
### Q: What exactly is CrewAI?
@@ -732,17 +732,17 @@ A: Check out practical examples in the [CrewAI-examples repository](https://gith
A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
### Q: What additional features does CrewAI AMP offer?
### Q: What additional features does CrewAI AOP offer?
A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
A: CrewAI AOP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
### Q: Is CrewAI AMP available for cloud and on-premise deployments?
### Q: Is CrewAI AOP available for cloud and on-premise deployments?
A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
A: Yes, CrewAI AOP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
### Q: Can I try CrewAI AMP for free?
### Q: Can I try CrewAI AOP for free?
A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
A: Yes, you can explore part of the CrewAI AOP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
### Q: Does CrewAI support fine-tuning or training custom models?
@@ -762,7 +762,7 @@ A: CrewAI is highly scalable, supporting simple automations and large-scale ente
### Q: Does CrewAI offer debugging and monitoring tools?
A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
A: Yes, CrewAI AOP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
### Q: What programming languages does CrewAI support?

4
docs/docs.json
View File

@@ -326,7 +326,7 @@
]
},
{
"tab": "AMP",
"tab": "AOP",
"icon": "briefcase",
"groups": [
{
@@ -753,7 +753,7 @@
]
},
{
"tab": "AMP",
"tab": "AOP",
"icon": "briefcase",
"groups": [
{

12
docs/en/api-reference/introduction.mdx
View File

@@ -1,19 +1,19 @@
---
title: "Introduction"
description: "Complete reference for the CrewAI AMP REST API"
description: "Complete reference for the CrewAI AOP REST API"
icon: "code"
mode: "wide"
---
# CrewAI AMP API
# CrewAI AOP API
Welcome to the CrewAI AMP API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
Welcome to the CrewAI AOP API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
## Quick Start
<Steps>
<Step title="Get Your API Credentials">
Navigate to your crew's detail page in the CrewAI AMP dashboard and copy your Bearer Token from the Status tab.
Navigate to your crew's detail page in the CrewAI AOP dashboard and copy your Bearer Token from the Status tab.
</Step>
<Step title="Discover Required Inputs">
@@ -46,7 +46,7 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
<Tip>
You can find both token types in the Status tab of your crew's detail page in the CrewAI AMP dashboard.
You can find both token types in the Status tab of your crew's detail page in the CrewAI AOP dashboard.
</Tip>
## Base URL
@@ -82,7 +82,7 @@ The API uses standard HTTP status codes:
## Interactive Testing
<Info>
**Why no "Send" button?** Since each CrewAI AMP user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
**Why no "Send" button?** Since each CrewAI AOP user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
</Info>
Each endpoint page shows you:

2
docs/en/concepts/agents.mdx
View File

@@ -20,7 +20,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
</Tip>
<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
CrewAI AMP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
CrewAI AOP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)

24
docs/en/concepts/cli.mdx
View File

@@ -5,7 +5,7 @@ icon: terminal
mode: "wide"
---
<Warning>Since release 0.140.0, CrewAI AMP started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
<Warning>Since release 0.140.0, CrewAI AOP started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
## Overview
@@ -186,9 +186,9 @@ def crew(self) -> Crew:
### 10. Deploy
Deploy the crew or flow to [CrewAI AMP](https://app.crewai.com).
Deploy the crew or flow to [CrewAI AOP](https://app.crewai.com).
- **Authentication**: You need to be authenticated to deploy to CrewAI AMP.
- **Authentication**: You need to be authenticated to deploy to CrewAI AOP.
You can login or create an account with:
```shell Terminal
crewai login
@@ -203,7 +203,7 @@ Deploy the crew or flow to [CrewAI AMP](https://app.crewai.com).
### 11. Organization Management
Manage your CrewAI AMP organizations.
Manage your CrewAI AOP organizations.
```shell Terminal
crewai org [COMMAND] [OPTIONS]
@@ -227,17 +227,17 @@ crewai org switch <organization_id>
```
<Note>
You must be authenticated to CrewAI AMP to use these organization management commands.
You must be authenticated to CrewAI AOP to use these organization management commands.
</Note>
- **Create a deployment** (continued):
- Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AMP.
- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AOP.
```shell Terminal
crewai deploy push
```
- Initiates the deployment process on the CrewAI AMP platform.
- Initiates the deployment process on the CrewAI AOP platform.
- Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
- **Deployment Status**: You can check the status of your deployment with:
@@ -262,7 +262,7 @@ You must be authenticated to CrewAI AMP to use these organization management com
```shell Terminal
crewai deploy remove
```
This deletes the deployment from the CrewAI AMP platform.
This deletes the deployment from the CrewAI AOP platform.
- **Help Command**: You can get help with the CLI with:
```shell Terminal
@@ -270,7 +270,7 @@ You must be authenticated to CrewAI AMP to use these organization management com
```
This shows the help message for the CrewAI Deploy CLI.
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI.
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AOP](http://app.crewai.com) using the CLI.
<iframe
className="w-full aspect-video rounded-xl"
@@ -283,7 +283,7 @@ Watch this video tutorial for a step-by-step demonstration of deploying your cre
### 11. Login
Authenticate with CrewAI AMP using a secure device code flow (no email entry required).
Authenticate with CrewAI AOP using a secure device code flow (no email entry required).
```shell Terminal
crewai login
@@ -354,7 +354,7 @@ crewai config reset
#### Available Configuration Parameters
- `enterprise_base_url`: Base URL of the CrewAI AMP instance
- `enterprise_base_url`: Base URL of the CrewAI AOP instance
- `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
- `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource
- `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests
@@ -370,7 +370,7 @@ crewai config list
Example output:
| Setting | Value | Description |
| :------------------ | :----------------------- | :---------------------------------------------------------- |
| enterprise_base_url | https://app.crewai.com | Base URL of the CrewAI AMP instance |
| enterprise_base_url | https://app.crewai.com | Base URL of the CrewAI AOP instance |
| org_name | Not set | Name of the currently active organization |
| org_uuid | Not set | UUID of the currently active organization |
| oauth2_provider | workos | OAuth2 provider (e.g., workos, okta, auth0) |

24
docs/en/concepts/crews.mdx
View File

@@ -33,6 +33,7 @@ A crew in crewAI represents a collaborative group of agents working together to
| **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description. |
| **Planning LLM** *(optional)* | `planning_llm` | The language model used by the AgentPlanner in a planning process. |
| **Knowledge Sources** _(optional)_ | `knowledge_sources` | Knowledge sources available at the crew level, accessible to all the agents. |
| **Stream** _(optional)_ | `stream` | Enable streaming output to receive real-time updates during crew execution. Returns a `CrewStreamingOutput` object that can be iterated for chunks. Defaults to `False`. |
<Tip>
**Crew Max RPM**: 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.
@@ -338,6 +339,29 @@ for async_result in async_results:
These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
### Streaming Crew Execution
For real-time visibility into crew execution, you can enable streaming to receive output as it's generated:
```python Code
# Enable streaming
crew = Crew(
agents=[researcher],
tasks=[task],
stream=True
)
# Iterate over streaming output
streaming = crew.kickoff(inputs={"topic": "AI"})
for chunk in streaming:
print(chunk.content, end="", flush=True)
# Access final result
result = streaming.result
```
Learn more about streaming in the [Streaming Crew Execution](/en/learn/streaming-crew-execution) guide.
### Replaying from a Specific Task
You can now replay from a specific task using our CLI command `replay`.

2
docs/en/concepts/event-listener.mdx
View File

@@ -20,7 +20,7 @@ CrewAI uses an event bus architecture to emit events throughout the execution li
When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
CrewAI AMP provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
CrewAI AOP provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)

25
docs/en/concepts/flows.mdx
View File

@@ -897,6 +897,31 @@ flow = ExampleFlow()
result = flow.kickoff()
```
### Streaming Flow Execution
For real-time visibility into flow execution, you can enable streaming to receive output as it's generated:
```python
class StreamingFlow(Flow):
stream = True # Enable streaming
@start()
def research(self):
# Your flow implementation
pass
# Iterate over streaming output
flow = StreamingFlow()
streaming = flow.kickoff()
for chunk in streaming:
print(chunk.content, end="", flush=True)
# Access final result
result = streaming.result
```
Learn more about streaming in the [Streaming Flow Execution](/en/learn/streaming-flow-execution) guide.
### Using the CLI
Starting from version 0.103.0, you can run flows using the `crewai run` command:

8
docs/en/concepts/knowledge.mdx
View File

@@ -388,8 +388,8 @@ crew = Crew(
agents=[sales_agent, tech_agent, support_agent],
tasks=[...],
embedder={ # Fallback embedder for agents without their own
"provider": "google",
"config": {"model": "text-embedding-004"}
"provider": "google-generativeai",
"config": {"model_name": "gemini-embedding-001"}
}
)
@@ -629,9 +629,9 @@ agent = Agent(
backstory="Expert researcher",
knowledge_sources=[knowledge_source],
embedder={
"provider": "google",
"provider": "google-generativeai",
"config": {
"model": "models/text-embedding-004",
"model_name": "gemini-embedding-001",
"api_key": "your-google-key"
}
}

18
docs/en/concepts/memory.mdx
View File

@@ -341,7 +341,7 @@ crew = Crew(
embedder={
"provider": "openai",
"config": {
"model": "text-embedding-3-small" # or "text-embedding-3-large"
"model_name": "text-embedding-3-small" # or "text-embedding-3-large"
}
}
)
@@ -353,7 +353,7 @@ crew = Crew(
"provider": "openai",
"config": {
"api_key": "your-openai-api-key", # Optional: override env var
"model": "text-embedding-3-large",
"model_name": "text-embedding-3-large",
"dimensions": 1536, # Optional: reduce dimensions for smaller storage
"organization_id": "your-org-id" # Optional: for organization accounts
}
@@ -375,7 +375,7 @@ crew = Crew(
"api_base": "https://your-resource.openai.azure.com/",
"api_type": "azure",
"api_version": "2023-05-15",
"model": "text-embedding-3-small",
"model_name": "text-embedding-3-small",
"deployment_id": "your-deployment-name" # Azure deployment name
}
}
@@ -390,10 +390,10 @@ Use Google's text embedding models for integration with Google Cloud services.
crew = Crew(
memory=True,
embedder={
"provider": "google",
"provider": "google-generativeai",
"config": {
"api_key": "your-google-api-key",
"model": "text-embedding-004" # or "text-embedding-preview-0409"
"model_name": "gemini-embedding-001" # or "text-embedding-005", "text-multilingual-embedding-002"
}
}
)
@@ -461,7 +461,7 @@ crew = Crew(
"provider": "cohere",
"config": {
"api_key": "your-cohere-api-key",
"model": "embed-english-v3.0" # or "embed-multilingual-v3.0"
"model_name": "embed-english-v3.0" # or "embed-multilingual-v3.0"
}
}
)
@@ -478,7 +478,7 @@ crew = Crew(
"provider": "voyageai",
"config": {
"api_key": "your-voyage-api-key",
"model": "voyage-large-2", # or "voyage-code-2" for code
"model": "voyage-3", # or "voyage-3-lite", "voyage-code-3"
"input_type": "document" # or "query"
}
}
@@ -912,10 +912,10 @@ crew = Crew(
crew = Crew(
memory=True,
embedder={
"provider": "google",
"provider": "google-generativeai",
"config": {
"api_key": "your-api-key",
"model": "text-embedding-004"
"model_name": "gemini-embedding-001"
}
}
)

2
docs/en/concepts/tasks.mdx
View File

@@ -14,7 +14,7 @@ Tasks provide all necessary details for execution, such as a description, the ag
Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
<Note type="info" title="Enterprise Enhancement: Visual Task Builder">
CrewAI AMP includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
CrewAI AOP includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)

2
docs/en/concepts/tools.mdx
View File

@@ -17,7 +17,7 @@ This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/cre
enabling everything from simple searches to complex interactions and effective teamwork among agents.
<Note type="info" title="Enterprise Enhancement: Tools Repository">
CrewAI AMP provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
CrewAI AOP provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
The Enterprise Tools Repository includes:
- Pre-built connectors for popular enterprise systems

8
docs/en/enterprise/features/rbac.mdx
View File

@@ -7,10 +7,10 @@ mode: "wide"
## Overview
RBAC in CrewAI AMP enables secure, scalable access management through a combination of organization‑level roles and automation‑level visibility controls.
RBAC in CrewAI AOP enables secure, scalable access management through a combination of organization‑level roles and automation‑level visibility controls.
<Frame>
<img src="/images/enterprise/users_and_roles.png" alt="RBAC overview in CrewAI AMP" />
<img src="/images/enterprise/users_and_roles.png" alt="RBAC overview in CrewAI AOP" />
</Frame>
@@ -28,7 +28,7 @@ You can configure users and roles in Settings → Roles.
<Steps>
<Step title="Open Roles settings">
Go to <b>Settings → Roles</b> in CrewAI AMP.
Go to <b>Settings → Roles</b> in CrewAI AOP.
</Step>
<Step title="Choose a role type">
Use a predefined role (<b>Owner</b>, <b>Member</b>) or click <b>Create role</b> to define a custom one.
@@ -93,7 +93,7 @@ The organization owner always has access. In private mode, only whitelisted user
</Tip>
<Frame>
<img src="/images/enterprise/visibility.png" alt="Automation Visibility settings in CrewAI AMP" />
<img src="/images/enterprise/visibility.png" alt="Automation Visibility settings in CrewAI AOP" />
</Frame>

8
docs/en/enterprise/features/tools-and-integrations.mdx
View File

@@ -189,10 +189,10 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma
## Internal Tools
Create custom tools locally, publish them on CrewAI AMP Tool Repository and use them in your agents.
Create custom tools locally, publish them on CrewAI AOP Tool Repository and use them in your agents.
<Tip>
Before running the commands below, make sure you log in to your CrewAI AMP account by running this command:
Before running the commands below, make sure you log in to your CrewAI AOP account by running this command:
```bash
crewai login
```
@@ -210,13 +210,13 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma
```
</Step>
<Step title="Publish">
Publish the tool to the CrewAI AMP Tool Repository.
Publish the tool to the CrewAI AOP Tool Repository.
```bash
crewai tool publish
```
</Step>
<Step title="Install">
Install the tool from the CrewAI AMP Tool Repository.
Install the tool from the CrewAI AOP Tool Repository.
```bash
crewai tool install your-tool
```

6
docs/en/enterprise/features/traces.mdx
View File

@@ -11,7 +11,7 @@ Traces provide comprehensive visibility into your crew executions, helping you m
## What are Traces?
Traces in CrewAI AMP are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
Traces in CrewAI AOP 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
@@ -28,7 +28,7 @@ Traces in CrewAI AMP are detailed execution records that capture every aspect of
<Steps>
<Step title="Navigate to the Traces Tab">
Once in your CrewAI AMP dashboard, click on the **Traces** to view all execution records.
Once in your CrewAI AOP dashboard, click on the **Traces** to view all execution records.
</Step>
<Step title="Select an Execution">
@@ -153,5 +153,5 @@ CrewAI batches trace uploads to reduce overhead on high-volume runs:
This yields more stable tracing under load while preserving detailed task/agent telemetry.
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with trace analysis or any other CrewAI AMP features.
Contact our support team for assistance with trace analysis or any other CrewAI AOP features.
</Card>

2
docs/en/enterprise/features/webhook-streaming.mdx
View File

@@ -8,7 +8,7 @@ mode: "wide"
## Overview
Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to
CrewAI AMP, such as model calls, tool usage, and flow steps.
CrewAI AOP, such as model calls, tool usage, and flow steps.
## Usage

4
docs/en/enterprise/guides/automation-triggers.mdx
View File

@@ -1,11 +1,11 @@
---
title: "Triggers Overview"
description: "Understand how CrewAI AMP triggers work, how to manage them, and where to find integration-specific playbooks"
description: "Understand how CrewAI AOP triggers work, how to manage them, and where to find integration-specific playbooks"
icon: "face-smile"
mode: "wide"
---
CrewAI AMP triggers connect your automations to real-time events across the tools your teams already use. Instead of polling systems or relying on manual kickoffs, triggers listen for changes—new emails, calendar updates, CRM status changes—and immediately launch the crew or flow you specify.
CrewAI AOP triggers connect your automations to real-time events across the tools your teams already use. Instead of polling systems or relying on manual kickoffs, triggers listen for changes—new emails, calendar updates, CRM status changes—and immediately launch the crew or flow you specify.
<Frame>
![Automation Triggers Overview](/images/enterprise/crew_connectors.png)

6
docs/en/enterprise/guides/azure-openai-setup.mdx
View File

@@ -19,8 +19,8 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle
</Frame>
</Step>
<Step title="Configure CrewAI AMP Connection">
4. In another tab, open `CrewAI AMP > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
<Step title="Configure CrewAI AOP Connection">
4. In another tab, open `CrewAI AOP > 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).
@@ -28,7 +28,7 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle
</Step>
<Step title="Set Default Configuration">
7. In `CrewAI AMP > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
7. In `CrewAI AOP > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
</Step>
<Step title="Configure Network Access">

2
docs/en/enterprise/guides/build-crew.mdx
View File

@@ -7,7 +7,7 @@ mode: "wide"
## Overview
[CrewAI AMP](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
[CrewAI AOP](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
## Getting Started

4
docs/en/enterprise/guides/capture_telemetry_logs.mdx
View File

@@ -1,11 +1,11 @@
---
title: "Open Telemetry Logs"
description: "Understand how to capture telemetry logs from your CrewAI AMP deployments"
description: "Understand how to capture telemetry logs from your CrewAI AOP deployments"
icon: "magnifying-glass-chart"
mode: "wide"
---
CrewAI AMP provides a powerful way to capture telemetry logs from your deployments. This allows you to monitor the performance of your agents and workflows, and to debug issues that may arise.
CrewAI AOP provides a powerful way to capture telemetry logs from your deployments. This allows you to monitor the performance of your agents and workflows, and to debug issues that may arise.
## Prerequisites

16
docs/en/enterprise/guides/deploy-crew.mdx
View File

@@ -1,12 +1,12 @@
---
title: "Deploy Crew"
description: "Deploying a Crew on CrewAI AMP"
description: "Deploying a Crew on CrewAI AOP"
icon: "rocket"
mode: "wide"
---
<Note>
After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI AMP platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI AOP platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
</Note>
## Prerequisites
@@ -39,10 +39,10 @@ The CLI provides the fastest way to deploy locally developed crews to the Enterp
</Step>
<Step title="Authenticate with the Enterprise Platform">
First, you need to authenticate your CLI with the CrewAI AMP platform:
First, you need to authenticate your CLI with the CrewAI AOP platform:
```bash
# If you already have a CrewAI AMP account, or want to create one:
# If you already have a CrewAI AOP account, or want to create one:
crewai login
```
@@ -124,7 +124,7 @@ The CrewAI CLI offers several commands to manage your deployments:
## Option 2: Deploy Directly via Web Interface
You can also deploy your crews directly through the CrewAI AMP web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
You can also deploy your crews directly through the CrewAI AOP web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
<Steps>
@@ -134,9 +134,9 @@ You can also deploy your crews directly through the CrewAI AMP web interface by
</Step>
<Step title="Connecting GitHub to CrewAI AMP">
<Step title="Connecting GitHub to CrewAI AOP">
1. Log in to [CrewAI AMP](https://app.crewai.com)
1. Log in to [CrewAI AOP](https://app.crewai.com)
2. Click on the button "Connect GitHub"
<Frame>
@@ -190,7 +190,7 @@ You can also deploy your crews directly through the CrewAI AMP web interface by
## ⚠️ Environment Variable Security Requirements
<Warning>
**Important**: CrewAI AMP has security restrictions on environment variable names that can cause deployment failures if not followed.
**Important**: CrewAI AOP has security restrictions on environment variable names that can cause deployment failures if not followed.
</Warning>
### Blocked Environment Variable Patterns

12
docs/en/enterprise/guides/enable-crew-studio.mdx
View File

@@ -1,6 +1,6 @@
---
title: "Enable Crew Studio"
description: "Enabling Crew Studio on CrewAI AMP"
description: "Enabling Crew Studio on CrewAI AOP"
icon: "comments"
mode: "wide"
---
@@ -24,7 +24,7 @@ With Crew Studio, you can:
- Select appropriate tools
- Configure necessary inputs
- Generate downloadable code for customization
- Deploy directly to the CrewAI AMP platform
- Deploy directly to the CrewAI AOP platform
## Configuration Steps
@@ -32,7 +32,7 @@ Before you can start using Crew Studio, you need to configure your LLM connectio
<Steps>
<Step title="Set Up LLM Connection">
Go to the **LLM Connections** tab in your CrewAI AMP dashboard and create a new LLM connection.
Go to the **LLM Connections** tab in your CrewAI AOP dashboard and create a new LLM connection.
<Note>
Feel free to use any LLM provider you want that is supported by CrewAI.
@@ -82,7 +82,7 @@ Now that you've configured your LLM connection and default settings, you're read
<Steps>
<Step title="Access Studio">
Navigate to the **Studio** section in your CrewAI AMP dashboard.
Navigate to the **Studio** section in your CrewAI AOP dashboard.
</Step>
<Step title="Start a Conversation">
@@ -110,7 +110,7 @@ Now that you've configured your LLM connection and default settings, you're read
Once you're satisfied with the configuration, you can:
- Download the generated code for local customization
- Deploy the crew directly to the CrewAI AMP platform
- Deploy the crew directly to the CrewAI AOP platform
- Modify the configuration and regenerate the crew
</Step>
@@ -162,5 +162,5 @@ Here's a typical workflow for creating a crew with Crew Studio:
</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 AMP features.
Contact our support team for assistance with Crew Studio or any other CrewAI AOP features.
</Card>

2
docs/en/enterprise/guides/gmail-trigger.mdx
View File

@@ -15,7 +15,7 @@ Use the Gmail Trigger to kick off your deployed crews when Gmail events happen i
## Enabling the Gmail Trigger
1. Open your deployment in CrewAI AMP
1. Open your deployment in CrewAI AOP
2. Go to the **Triggers** tab
3. Locate **Gmail** and switch the toggle to enable

2
docs/en/enterprise/guides/google-calendar-trigger.mdx
View File

@@ -15,7 +15,7 @@ Use the Google Calendar trigger to launch automations whenever calendar events c
## Enabling the Google Calendar Trigger
1. Open your deployment in CrewAI AMP
1. Open your deployment in CrewAI AOP
2. Go to the **Triggers** tab
3. Locate **Google Calendar** and switch the toggle to enable

2
docs/en/enterprise/guides/google-drive-trigger.mdx
View File

@@ -15,7 +15,7 @@ Trigger your automations when files are created, updated, or removed in Google D
## Enabling the Google Drive Trigger
1. Open your deployment in CrewAI AMP
1. Open your deployment in CrewAI AOP
2. Go to the **Triggers** tab
3. Locate **Google Drive** and switch the toggle to enable

14
docs/en/enterprise/guides/hubspot-trigger.mdx
View File

@@ -5,22 +5,22 @@ icon: "hubspot"
mode: "wide"
---
This guide provides a step-by-step process to set up HubSpot triggers for CrewAI AMP, enabling you to initiate crews directly from HubSpot Workflows.
This guide provides a step-by-step process to set up HubSpot triggers for CrewAI AOP, enabling you to initiate crews directly from HubSpot Workflows.
## Prerequisites
- A CrewAI AMP account
- A CrewAI AOP 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 AMP">
- Log in to your `CrewAI AMP account > Triggers`
<Step title="Connect your HubSpot account with CrewAI AOP">
- Log in to your `CrewAI AOP account > Triggers`
- Select `HubSpot` from the list of available triggers
- Choose the HubSpot account you want to connect with CrewAI AMP
- Follow the on-screen prompts to authorize CrewAI AMP access to your HubSpot account
- A confirmation message will appear once HubSpot is successfully connected with CrewAI AMP
- Choose the HubSpot account you want to connect with CrewAI AOP
- Follow the on-screen prompts to authorize CrewAI AOP access to your HubSpot account
- A confirmation message will appear once HubSpot is successfully connected with CrewAI AOP
</Step>
<Step title="Create a HubSpot Workflow">
- Log in to your `HubSpot account > Automations > Workflows > New workflow`

8
docs/en/enterprise/guides/kickoff-crew.mdx
View File

@@ -1,19 +1,19 @@
---
title: "Kickoff Crew"
description: "Kickoff a Crew on CrewAI AMP"
description: "Kickoff a Crew on CrewAI AOP"
icon: "flag-checkered"
mode: "wide"
---
## Overview
Once you've deployed your crew to the CrewAI AMP platform, you can kickoff executions through the web interface or the API. This guide covers both approaches.
Once you've deployed your crew to the CrewAI AOP 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 AMP](https://app.crewai.com)
1. Log in to [CrewAI AOP](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
@@ -83,7 +83,7 @@ Once execution is complete:
## Method 2: Using the API
You can also kickoff crews programmatically using the CrewAI AMP REST API.
You can also kickoff crews programmatically using the CrewAI AOP REST API.
### Authentication

2
docs/en/enterprise/guides/microsoft-teams-trigger.mdx
View File

@@ -15,7 +15,7 @@ Use the Microsoft Teams trigger to start automations whenever a new chat is crea
## Enabling the Microsoft Teams Trigger
1. Open your deployment in CrewAI AMP
1. Open your deployment in CrewAI AOP
2. Go to the **Triggers** tab
3. Locate **Microsoft Teams** and switch the toggle to enable

2
docs/en/enterprise/guides/onedrive-trigger.mdx
View File

@@ -15,7 +15,7 @@ Start automations when files change inside OneDrive. You can generate audit summ
## Enabling the OneDrive Trigger
1. Open your deployment in CrewAI AMP
1. Open your deployment in CrewAI AOP
2. Go to the **Triggers** tab
3. Locate **OneDrive** and switch the toggle to enable

2
docs/en/enterprise/guides/outlook-trigger.mdx
View File

@@ -15,7 +15,7 @@ Automate responses when Outlook delivers a new message or when an event is remov
## Enabling the Outlook Trigger
1. Open your deployment in CrewAI AMP
1. Open your deployment in CrewAI AOP
2. Go to the **Triggers** tab
3. Locate **Outlook** and switch the toggle to enable

4
docs/en/enterprise/guides/react-component-export.mdx
View File

@@ -1,11 +1,11 @@
---
title: "React Component Export"
description: "Learn how to export and integrate CrewAI AMP React components into your applications"
description: "Learn how to export and integrate CrewAI AOP React components into your applications"
icon: "react"
mode: "wide"
---
This guide explains how to export CrewAI AMP crews as React components and integrate them into your own applications.
This guide explains how to export CrewAI AOP crews as React components and integrate them into your own applications.
## Exporting a React Component

6
docs/en/enterprise/guides/salesforce-trigger.mdx
View File

@@ -5,7 +5,7 @@ icon: "salesforce"
mode: "wide"
---
CrewAI AMP can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations.
CrewAI AOP can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations.
## Overview
@@ -31,7 +31,7 @@ Salesforce is a leading customer relationship management (CRM) platform that hel
To set up Salesforce triggers:
1. **Contact Support**: Reach out to CrewAI AMP support for assistance with Salesforce trigger setup
1. **Contact Support**: Reach out to CrewAI AOP 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
@@ -47,4 +47,4 @@ Common Salesforce + CrewAI trigger scenarios include:
## Next Steps
For detailed setup instructions and advanced configuration options, please contact CrewAI AMP support who can provide tailored guidance for your specific Salesforce environment and business needs.
For detailed setup instructions and advanced configuration options, please contact CrewAI AOP support who can provide tailored guidance for your specific Salesforce environment and business needs.

10
docs/en/enterprise/guides/team-management.mdx
View File

@@ -1,17 +1,17 @@
---
title: "Team Management"
description: "Learn how to invite and manage team members in your CrewAI AMP organization"
description: "Learn how to invite and manage team members in your CrewAI AOP organization"
icon: "users"
mode: "wide"
---
As an administrator of a CrewAI AMP account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step.
As an administrator of a CrewAI AOP 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 AMP account
- Log in to your CrewAI AOP 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 caption="Settings page">
@@ -43,7 +43,7 @@ You can add roles to your team members to control their access to different part
<Steps>
<Step title="Access the Settings Page">
- Log in to your CrewAI AMP account
- Log in to your CrewAI AOP 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>
@@ -85,4 +85,4 @@ You can add roles to your team members to control their access to different part
- **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 AMP organization.
By following these steps, you can easily expand your team and collaborate more effectively within your CrewAI AOP organization.

14
docs/en/enterprise/guides/tool-repository.mdx
View File

@@ -20,11 +20,11 @@ The repository is not a version control system. Use Git to track code changes an
Before using the Tool Repository, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account
- A [CrewAI AOP](https://app.crewai.com) account
- [CrewAI CLI](/en/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 AMP organization
- Access permissions to publish or install tools in your CrewAI AOP organization
## Installing Tools
@@ -54,11 +54,11 @@ researcher = Agent(
## Adding other packages after installing a tool
After installing a tool from the CrewAI AMP Tool Repository, you need to use the `crewai uv` command to add other packages to your project.
After installing a tool from the CrewAI AOP Tool Repository, you need to use the `crewai uv` command to add other packages to your project.
Using pure `uv` commands will fail due to authentication to tool repository being handled by the CLI. By using the `crewai uv` command, you can add other packages to your project without having to worry about authentication.
Any `uv` command can be used with the `crewai uv` command, making it a powerful tool for managing your project's dependencies without the hassle of managing authentication through environment variables or other methods.
Say that you have installed a custom tool from the CrewAI AMP Tool Repository called "my-tool":
Say that you have installed a custom tool from the CrewAI AOP Tool Repository called "my-tool":
```bash
crewai tool install my-tool
@@ -131,7 +131,7 @@ crewai tool publish
To delete a tool:
1. Go to [CrewAI AMP](https://app.crewai.com)
1. Go to [CrewAI AOP](https://app.crewai.com)
2. Navigate to **Tools**
3. Select the tool
4. Click **Delete**
@@ -146,10 +146,8 @@ Every published version undergoes automated security checks, and are only availa
You can check the security check status of a tool at:
`CrewAI AMP > Tools > Your Tool > Versions`
`CrewAI AOP > 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>

8
docs/en/enterprise/guides/update-crew.mdx
View File

@@ -1,12 +1,12 @@
---
title: "Update Crew"
description: "Updating a Crew on CrewAI AMP"
description: "Updating a Crew on CrewAI AOP"
icon: "pencil"
mode: "wide"
---
<Note>
After deploying your crew to CrewAI AMP, you may need to make updates to the code, security settings, or configuration.
After deploying your crew to CrewAI AOP, you may need to make updates to the code, security settings, or configuration.
This guide explains how to perform these common update operations.
</Note>
@@ -23,7 +23,7 @@ There are several reasons you might want to update your crew deployment:
When you've pushed new commits to your GitHub repository and want to update your deployment:
1. Navigate to your crew in the CrewAI AMP platform
1. Navigate to your crew in the CrewAI AOP platform
2. Click on the `Re-deploy` button on your crew details page
<Frame>
@@ -36,7 +36,7 @@ This will trigger an update that you can track using the progress bar. The syste
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 AMP platform
1. Navigate to your crew in the CrewAI AOP platform
2. Find the `Bearer Token` section
3. Click the `Reset` button next to your current token

8
docs/en/enterprise/guides/webhook-automation.mdx
View File

@@ -1,17 +1,17 @@
---
title: "Webhook Automation"
description: "Automate CrewAI AMP workflows using webhooks with platforms like ActivePieces, Zapier, and Make.com"
description: "Automate CrewAI AOP workflows using webhooks with platforms like ActivePieces, Zapier, and Make.com"
icon: "webhook"
mode: "wide"
---
CrewAI AMP 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.
CrewAI AOP 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 AMP dashboard
- Navigate to the CrewAI AOP 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" />
@@ -44,7 +44,7 @@ CrewAI AMP allows you to automate your workflow using webhooks. This article wil
3. Add an HTTP action step
- Set the action to `Send HTTP request`
- Use `POST` as the method
- Set the URL to your CrewAI AMP kickoff endpoint
- Set the URL to your CrewAI AOP kickoff endpoint
- Add necessary headers (e.g., `Bearer Token`)
<Frame>
<img src="/images/enterprise/activepieces-headers.png" alt="ActivePieces Headers" />

20
docs/en/enterprise/guides/zapier-trigger.mdx
View File

@@ -5,11 +5,11 @@ icon: "bolt"
mode: "wide"
---
This guide will walk you through the process of setting up Zapier triggers for CrewAI AMP, allowing you to automate workflows between CrewAI AMP and other applications.
This guide will walk you through the process of setting up Zapier triggers for CrewAI AOP, allowing you to automate workflows between CrewAI AOP and other applications.
## Prerequisites
- A CrewAI AMP account
- A CrewAI AOP account
- A Zapier account
- A Slack account (for this specific example)
@@ -32,7 +32,7 @@ This guide will walk you through the process of setting up Zapier triggers for C
- Connect your Slack account if you haven't already.
</Step>
<Step title="Configure the CrewAI AMP Action">
<Step title="Configure the CrewAI AOP Action">
- Add a new action step to your Zap.
- Choose CrewAI+ as your action app and Kickoff as the Action Event
@@ -41,8 +41,8 @@ This guide will walk you through the process of setting up Zapier triggers for C
</Frame>
</Step>
<Step title="Connect your CrewAI AMP account">
- Connect your CrewAI AMP account.
<Step title="Connect your CrewAI AOP account">
- Connect your CrewAI AOP account.
- Select the appropriate Crew for your workflow.
<Frame>
@@ -51,8 +51,8 @@ This guide will walk you through the process of setting up Zapier triggers for C
- Configure the inputs for the Crew using the data from the Slack message.
</Step>
<Step title="Format the CrewAI AMP Output">
- Add another action step to format the text output from CrewAI AMP.
<Step title="Format the CrewAI AOP Output">
- Add another action step to format the text output from CrewAI AOP.
- Use Zapier's formatting tools to convert the Markdown output to HTML.
<Frame>
@@ -67,7 +67,7 @@ This guide will walk you through the process of setting up Zapier triggers for C
- 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 AMP output into the email body.
- Insert the formatted CrewAI AOP output into the email body.
<Frame>
<img src="/images/enterprise/zapier-7.png" alt="Zapier 7" />
@@ -97,8 +97,8 @@ This guide will walk you through the process of setting up Zapier triggers for C
## Tips for Success
- Ensure that your CrewAI AMP inputs are correctly mapped from the Slack message.
- Ensure that your CrewAI AOP 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 AMP, allowing for automated workflows triggered by Slack messages and resulting in email notifications with CrewAI AMP output.
By following these steps, you'll have successfully set up Zapier triggers for CrewAI AOP, allowing for automated workflows triggered by Slack messages and resulting in email notifications with CrewAI AOP output.

4
docs/en/enterprise/integrations/asana.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage tasks, projects, and team coordination through Asan
Before using the Asana integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- An Asana account with appropriate permissions
- Connected your Asana account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Asana integration, ensure you have:
### 1. Connect Your Asana Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Asana** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for task and project management

4
docs/en/enterprise/integrations/box.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage files, folders, and documents through Box. Upload f
Before using the Box integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Box account with appropriate permissions
- Connected your Box account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Box integration, ensure you have:
### 1. Connect Your Box Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Box** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for file and folder management

4
docs/en/enterprise/integrations/clickup.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage tasks, projects, and productivity workflows through
Before using the ClickUp integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A ClickUp account with appropriate permissions
- Connected your ClickUp account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the ClickUp integration, ensure you have:
### 1. Connect Your ClickUp Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **ClickUp** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for task and project management

4
docs/en/enterprise/integrations/github.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage repositories, issues, and releases through GitHub.
Before using the GitHub integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A GitHub account with appropriate repository permissions
- Connected your GitHub account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the GitHub integration, ensure you have:
### 1. Connect Your GitHub Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **GitHub** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for repository and issue management

4
docs/en/enterprise/integrations/gmail.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage emails, contacts, and drafts through Gmail. Send em
Before using the Gmail integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Gmail account with appropriate permissions
- Connected your Gmail account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Gmail integration, ensure you have:
### 1. Connect Your Gmail Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Gmail** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for email and contact management

4
docs/en/enterprise/integrations/google_calendar.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage calendar events, schedules, and availability throug
Before using the Google Calendar integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Google account with Google Calendar access
- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Google Calendar integration, ensure you have:
### 1. Connect Your Google Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Google Calendar** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for calendar access

4
docs/en/enterprise/integrations/google_contacts.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage contacts and directory information through Google C
Before using the Google Contacts integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Google account with Google Contacts access
- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Google Contacts integration, ensure you have:
### 1. Connect Your Google Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Google Contacts** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for contacts and directory access

4
docs/en/enterprise/integrations/google_docs.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to create, edit, and manage Google Docs documents with text m
Before using the Google Docs integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Google account with Google Docs access
- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Google Docs integration, ensure you have:
### 1. Connect Your Google Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Google Docs** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for document access

4
docs/en/enterprise/integrations/google_drive.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage files and folders through Google Drive. Upload, dow
Before using the Google Drive integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Google account with Google Drive access
- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Google Drive integration, ensure you have:
### 1. Connect Your Google Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Google Drive** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for file and folder management

4
docs/en/enterprise/integrations/google_sheets.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage spreadsheet data through Google Sheets. Read rows,
Before using the Google Sheets integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Google account with Google Sheets access
- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
- Spreadsheets with proper column headers for data operations
@@ -22,7 +22,7 @@ Before using the Google Sheets integration, ensure you have:
### 1. Connect Your Google Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Google Sheets** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for spreadsheet access

4
docs/en/enterprise/integrations/google_slides.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to create, edit, and manage Google Slides presentations. Crea
Before using the Google Slides integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Google account with Google Slides access
- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Google Slides integration, ensure you have:
### 1. Connect Your Google Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Google Slides** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for presentations, spreadsheets, and drive access

4
docs/en/enterprise/integrations/hubspot.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage companies and contacts within HubSpot. Create new r
Before using the HubSpot integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription.
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription.
- A HubSpot account with appropriate permissions.
- Connected your HubSpot account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors).
@@ -21,7 +21,7 @@ Before using the HubSpot integration, ensure you have:
### 1. Connect Your HubSpot Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors).
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors).
2. Find **HubSpot** in the Authentication Integrations section.
3. Click **Connect** and complete the OAuth flow.
4. Grant the necessary permissions for company and contact management.

4
docs/en/enterprise/integrations/jira.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage issues, projects, and workflows through Jira. Creat
Before using the Jira integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Jira account with appropriate project permissions
- Connected your Jira account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Jira integration, ensure you have:
### 1. Connect Your Jira Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Jira** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for issue and project management

4
docs/en/enterprise/integrations/linear.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage issues, projects, and development workflows through
Before using the Linear integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Linear account with appropriate workspace permissions
- Connected your Linear account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Linear integration, ensure you have:
### 1. Connect Your Linear Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Linear** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for issue and project management

4
docs/en/enterprise/integrations/microsoft_excel.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to create and manage Excel workbooks, worksheets, tables, and
Before using the Microsoft Excel integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Microsoft 365 account with Excel and OneDrive/SharePoint access
- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Microsoft Excel integration, ensure you have:
### 1. Connect Your Microsoft Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Microsoft Excel** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for files and Excel workbook access

4
docs/en/enterprise/integrations/microsoft_onedrive.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to upload, download, and manage files and folders in Microsof
Before using the Microsoft OneDrive integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Microsoft account with OneDrive access
- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Microsoft OneDrive integration, ensure you have:
### 1. Connect Your Microsoft Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Microsoft OneDrive** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for file access

4
docs/en/enterprise/integrations/microsoft_outlook.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to access and manage Outlook emails, calendar events, and con
Before using the Microsoft Outlook integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Microsoft account with Outlook access
- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Microsoft Outlook integration, ensure you have:
### 1. Connect Your Microsoft Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Microsoft Outlook** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for mail, calendar, and contact access

4
docs/en/enterprise/integrations/microsoft_sharepoint.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to access and manage SharePoint sites, lists, and document li
Before using the Microsoft SharePoint integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Microsoft 365 account with SharePoint access
- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Microsoft SharePoint integration, ensure you have:
### 1. Connect Your Microsoft Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Microsoft SharePoint** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for SharePoint sites and content access

4
docs/en/enterprise/integrations/microsoft_teams.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to access Teams data, send messages, create meetings, and man
Before using the Microsoft Teams integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Microsoft account with Teams access
- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Microsoft Teams integration, ensure you have:
### 1. Connect Your Microsoft Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Microsoft Teams** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for Teams access

4
docs/en/enterprise/integrations/microsoft_word.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to create, read, and manage Word documents and text files in
Before using the Microsoft Word integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Microsoft account with Word and OneDrive/SharePoint access
- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Microsoft Word integration, ensure you have:
### 1. Connect Your Microsoft Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Microsoft Word** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for file access

4
docs/en/enterprise/integrations/notion.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage users and create comments through Notion. Access wo
Before using the Notion integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Notion account with appropriate workspace permissions
- Connected your Notion account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
@@ -21,7 +21,7 @@ Before using the Notion integration, ensure you have:
### 1. Connect Your Notion Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Notion** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for user access and comment creation

4
docs/en/enterprise/integrations/salesforce.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage customer relationships, sales processes, and data t
Before using the Salesforce integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Salesforce account with appropriate permissions
- Connected your Salesforce account through the [Integrations page](https://app.crewai.com/integrations)
@@ -21,7 +21,7 @@ Before using the Salesforce integration, ensure you have:
### 1. Connect Your Salesforce Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Salesforce** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for CRM and sales management

4
docs/en/enterprise/integrations/shopify.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage e-commerce operations through Shopify. Handle custo
Before using the Shopify integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Shopify store with appropriate admin permissions
- Connected your Shopify store through the [Integrations page](https://app.crewai.com/integrations)
@@ -21,7 +21,7 @@ Before using the Shopify integration, ensure you have:
### 1. Connect Your Shopify Store
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Shopify** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for store and product management

4
docs/en/enterprise/integrations/slack.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage team communication through Slack. Send messages, se
Before using the Slack integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Slack workspace with appropriate permissions
- Connected your Slack workspace through the [Integrations page](https://app.crewai.com/integrations)
@@ -21,7 +21,7 @@ Before using the Slack integration, ensure you have:
### 1. Connect Your Slack Workspace
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Slack** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for team communication

4
docs/en/enterprise/integrations/stripe.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage payments, subscriptions, and customer billing throu
Before using the Stripe integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Stripe account with appropriate API permissions
- Connected your Stripe account through the [Integrations page](https://app.crewai.com/integrations)
@@ -21,7 +21,7 @@ Before using the Stripe integration, ensure you have:
### 1. Connect Your Stripe Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Stripe** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for payment processing

4
docs/en/enterprise/integrations/zendesk.mdx
View File

@@ -13,7 +13,7 @@ Enable your agents to manage customer support operations through Zendesk. Create
Before using the Zendesk integration, ensure you have:
- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
- A [CrewAI AOP](https://app.crewai.com) account with an active subscription
- A Zendesk account with appropriate API permissions
- Connected your Zendesk account through the [Integrations page](https://app.crewai.com/integrations)
@@ -21,7 +21,7 @@ Before using the Zendesk integration, ensure you have:
### 1. Connect Your Zendesk Account
1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
1. Navigate to [CrewAI AOP Integrations](https://app.crewai.com/crewai_plus/connectors)
2. Find **Zendesk** in the Authentication Integrations section
3. Click **Connect** and complete the OAuth flow
4. Grant the necessary permissions for ticket and user management

8
docs/en/enterprise/introduction.mdx
View File

@@ -1,5 +1,5 @@
---
title: "CrewAI AMP"
title: "CrewAI AOP"
description: "Deploy, monitor, and scale your AI agent workflows"
icon: "globe"
mode: "wide"
@@ -7,13 +7,13 @@ mode: "wide"
## Introduction
CrewAI AMP(Agent Management Platform) provides a platform for deploying, monitoring, and scaling your crews and agents in a production environment.
CrewAI AOP(Agent Operations Platform) 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 AMP Dashboard" />
<img src="/images/enterprise/crewai-enterprise-dashboard.png" alt="CrewAI AOP Dashboard" />
</Frame>
CrewAI AMP 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.
CrewAI AOP 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

2
docs/en/enterprise/resources/frequently-asked-questions.mdx
View File

@@ -1,6 +1,6 @@
---
title: FAQs
description: "Frequently asked questions about CrewAI AMP"
description: "Frequently asked questions about CrewAI AOP"
icon: "circle-question"
mode: "wide"
---

2
docs/en/installation.mdx
View File

@@ -167,7 +167,7 @@ We recommend using the `YAML` template scaffolding for a structured approach to
<Note type="info">
For teams and organizations, CrewAI offers enterprise deployment options that eliminate setup complexity:
### CrewAI AMP (SaaS)
### CrewAI AOP (SaaS)
- Zero installation required - just sign up for free at [app.crewai.com](https://app.crewai.com)
- Automatic updates and maintenance
- Managed infrastructure and scaling

4
docs/en/learn/llm-selection-guide.mdx
View File

@@ -394,7 +394,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
<Step title="Validate with Enterprise Testing" icon="test-tube">
**Once you deploy your agents to production:**
- Use [CrewAI AMP platform](https://app.crewai.com) to A/B test your model selections
- Use [CrewAI AOP platform](https://app.crewai.com) to A/B test your model selections
- Run multiple iterations with real inputs to measure consistency and performance
- Compare cost vs. performance across your optimized setup
- Share results with your team for collaborative decision-making
@@ -541,7 +541,7 @@ Focus on understanding your requirements first, then select models that best mat
### Enterprise-Grade Model Validation
For teams serious about optimizing their LLM selection, the **CrewAI AMP platform** provides sophisticated testing capabilities that go far beyond basic CLI testing. The platform enables comprehensive model evaluation that helps you make data-driven decisions about your LLM strategy.
For teams serious about optimizing their LLM selection, the **CrewAI AOP platform** provides sophisticated testing capabilities that go far beyond basic CLI testing. The platform enables comprehensive model evaluation that helps you make data-driven decisions about your LLM strategy.
<Frame>
![Enterprise Testing Interface](/images/enterprise/enterprise-testing.png)

320
docs/en/learn/streaming-crew-execution.mdx Normal file
View File

@@ -0,0 +1,320 @@
---
title: Streaming Crew Execution
description: Stream real-time output from your CrewAI crew execution
icon: wave-pulse
mode: "wide"
---
## Introduction
CrewAI provides the ability to stream real-time output during crew execution, allowing you to display results as they're generated rather than waiting for the entire process to complete. This feature is particularly useful for building interactive applications, providing user feedback, and monitoring long-running processes.
## How Streaming Works
When streaming is enabled, CrewAI captures LLM responses and tool calls as they happen, packaging them into structured chunks that include context about which task and agent is executing. You can iterate over these chunks in real-time and access the final result once execution completes.
## Enabling Streaming
To enable streaming, set the `stream` parameter to `True` when creating your crew:
```python Code
from crewai import Agent, Crew, Task
# Create your agents and tasks
researcher = Agent(
role="Research Analyst",
goal="Gather comprehensive information on topics",
backstory="You are an experienced researcher with excellent analytical skills.",
)
task = Task(
description="Research the latest developments in AI",
expected_output="A detailed report on recent AI advancements",
agent=researcher,
)
# Enable streaming
crew = Crew(
agents=[researcher],
tasks=[task],
stream=True # Enable streaming output
)
```
## Synchronous Streaming
When you call `kickoff()` on a crew with streaming enabled, it returns a `CrewStreamingOutput` object that you can iterate over to receive chunks as they arrive:
```python Code
# Start streaming execution
streaming = crew.kickoff(inputs={"topic": "artificial intelligence"})
# Iterate over chunks as they arrive
for chunk in streaming:
print(chunk.content, end="", flush=True)
# Access the final result after streaming completes
result = streaming.result
print(f"\n\nFinal output: {result.raw}")
```
### Stream Chunk Information
Each chunk provides rich context about the execution:
```python Code
streaming = crew.kickoff(inputs={"topic": "AI"})
for chunk in streaming:
print(f"Task: {chunk.task_name} (index {chunk.task_index})")
print(f"Agent: {chunk.agent_role}")
print(f"Content: {chunk.content}")
print(f"Type: {chunk.chunk_type}") # TEXT or TOOL_CALL
if chunk.tool_call:
print(f"Tool: {chunk.tool_call.tool_name}")
print(f"Arguments: {chunk.tool_call.arguments}")
```
### Accessing Streaming Results
The `CrewStreamingOutput` object provides several useful properties:
```python Code
streaming = crew.kickoff(inputs={"topic": "AI"})
# Iterate and collect chunks
for chunk in streaming:
print(chunk.content, end="", flush=True)
# After iteration completes
print(f"\nCompleted: {streaming.is_completed}")
print(f"Full text: {streaming.get_full_text()}")
print(f"All chunks: {len(streaming.chunks)}")
print(f"Final result: {streaming.result.raw}")
```
## Asynchronous Streaming
For async applications, use `kickoff_async()` with async iteration:
```python Code
import asyncio
async def stream_crew():
crew = Crew(
agents=[researcher],
tasks=[task],
stream=True
)
# Start async streaming
streaming = await crew.kickoff_async(inputs={"topic": "AI"})
# Async iteration over chunks
async for chunk in streaming:
print(chunk.content, end="", flush=True)
# Access final result
result = streaming.result
print(f"\n\nFinal output: {result.raw}")
asyncio.run(stream_crew())
```
## Streaming with kickoff_for_each
When executing a crew for multiple inputs with `kickoff_for_each()`, streaming works differently depending on whether you use sync or async:
### Synchronous kickoff_for_each
With synchronous `kickoff_for_each()`, you get a list of `CrewStreamingOutput` objects, one for each input:
```python Code
crew = Crew(
agents=[researcher],
tasks=[task],
stream=True
)
inputs_list = [
{"topic": "AI in healthcare"},
{"topic": "AI in finance"}
]
# Returns list of streaming outputs
streaming_outputs = crew.kickoff_for_each(inputs=inputs_list)
# Iterate over each streaming output
for i, streaming in enumerate(streaming_outputs):
print(f"\n=== Input {i + 1} ===")
for chunk in streaming:
print(chunk.content, end="", flush=True)
result = streaming.result
print(f"\n\nResult {i + 1}: {result.raw}")
```
### Asynchronous kickoff_for_each_async
With async `kickoff_for_each_async()`, you get a single `CrewStreamingOutput` that yields chunks from all crews as they arrive concurrently:
```python Code
import asyncio
async def stream_multiple_crews():
crew = Crew(
agents=[researcher],
tasks=[task],
stream=True
)
inputs_list = [
{"topic": "AI in healthcare"},
{"topic": "AI in finance"}
]
# Returns single streaming output for all crews
streaming = await crew.kickoff_for_each_async(inputs=inputs_list)
# Chunks from all crews arrive as they're generated
async for chunk in streaming:
print(f"[{chunk.task_name}] {chunk.content}", end="", flush=True)
# Access all results
results = streaming.results # List of CrewOutput objects
for i, result in enumerate(results):
print(f"\n\nResult {i + 1}: {result.raw}")
asyncio.run(stream_multiple_crews())
```
## Stream Chunk Types
Chunks can be of different types, indicated by the `chunk_type` field:
### TEXT Chunks
Standard text content from LLM responses:
```python Code
for chunk in streaming:
if chunk.chunk_type == StreamChunkType.TEXT:
print(chunk.content, end="", flush=True)
```
### TOOL_CALL Chunks
Information about tool calls being made:
```python Code
for chunk in streaming:
if chunk.chunk_type == StreamChunkType.TOOL_CALL:
print(f"\nCalling tool: {chunk.tool_call.tool_name}")
print(f"Arguments: {chunk.tool_call.arguments}")
```
## Practical Example: Building a UI with Streaming
Here's a complete example showing how to build an interactive application with streaming:
```python Code
import asyncio
from crewai import Agent, Crew, Task
from crewai.types.streaming import StreamChunkType
async def interactive_research():
# Create crew with streaming enabled
researcher = Agent(
role="Research Analyst",
goal="Provide detailed analysis on any topic",
backstory="You are an expert researcher with broad knowledge.",
)
task = Task(
description="Research and analyze: {topic}",
expected_output="A comprehensive analysis with key insights",
agent=researcher,
)
crew = Crew(
agents=[researcher],
tasks=[task],
stream=True,
verbose=False
)
# Get user input
topic = input("Enter a topic to research: ")
print(f"\n{'='*60}")
print(f"Researching: {topic}")
print(f"{'='*60}\n")
# Start streaming execution
streaming = await crew.kickoff_async(inputs={"topic": topic})
current_task = ""
async for chunk in streaming:
# Show task transitions
if chunk.task_name != current_task:
current_task = chunk.task_name
print(f"\n[{chunk.agent_role}] Working on: {chunk.task_name}")
print("-" * 60)
# Display text chunks
if chunk.chunk_type == StreamChunkType.TEXT:
print(chunk.content, end="", flush=True)
# Display tool calls
elif chunk.chunk_type == StreamChunkType.TOOL_CALL and chunk.tool_call:
print(f"\n🔧 Using tool: {chunk.tool_call.tool_name}")
# Show final result
result = streaming.result
print(f"\n\n{'='*60}")
print("Analysis Complete!")
print(f"{'='*60}")
print(f"\nToken Usage: {result.token_usage}")
asyncio.run(interactive_research())
```
## Use Cases
Streaming is particularly valuable for:
- **Interactive Applications**: Provide real-time feedback to users as agents work
- **Long-Running Tasks**: Show progress for research, analysis, or content generation
- **Debugging and Monitoring**: Observe agent behavior and decision-making in real-time
- **User Experience**: Reduce perceived latency by showing incremental results
- **Live Dashboards**: Build monitoring interfaces that display crew execution status
## Important Notes
- Streaming automatically enables LLM streaming for all agents in the crew
- You must iterate through all chunks before accessing the `.result` property
- For `kickoff_for_each_async()` with streaming, use `.results` (plural) to get all outputs
- Streaming adds minimal overhead and can actually improve perceived performance
- Each chunk includes full context (task, agent, chunk type) for rich UIs
## Error Handling
Handle errors during streaming execution:
```python Code
streaming = crew.kickoff(inputs={"topic": "AI"})
try:
for chunk in streaming:
print(chunk.content, end="", flush=True)
result = streaming.result
print(f"\nSuccess: {result.raw}")
except Exception as e:
print(f"\nError during streaming: {e}")
if streaming.is_completed:
print("Streaming completed but an error occurred")
```
By leveraging streaming, you can build more responsive and interactive applications with CrewAI, providing users with real-time visibility into agent execution and results.

450
docs/en/learn/streaming-flow-execution.mdx Normal file
View File

@@ -0,0 +1,450 @@
---
title: Streaming Flow Execution
description: Stream real-time output from your CrewAI flow execution
icon: wave-pulse
mode: "wide"
---
## Introduction
CrewAI Flows support streaming output, allowing you to receive real-time updates as your flow executes. This feature enables you to build responsive applications that display results incrementally, provide live progress updates, and create better user experiences for long-running workflows.
## How Flow Streaming Works
When streaming is enabled on a Flow, CrewAI captures and streams output from any crews or LLM calls within the flow. The stream delivers structured chunks containing the content, task context, and agent information as execution progresses.
## Enabling Streaming
To enable streaming, set the `stream` attribute to `True` on your Flow class:
```python Code
from crewai.flow.flow import Flow, listen, start
from crewai import Agent, Crew, Task
class ResearchFlow(Flow):
stream = True # Enable streaming for the entire flow
@start()
def initialize(self):
return {"topic": "AI trends"}
@listen(initialize)
def research_topic(self, data):
researcher = Agent(
role="Research Analyst",
goal="Research topics thoroughly",
backstory="Expert researcher with analytical skills",
)
task = Task(
description="Research {topic} and provide insights",
expected_output="Detailed research findings",
agent=researcher,
)
crew = Crew(
agents=[researcher],
tasks=[task],
)
return crew.kickoff(inputs=data)
```
## Synchronous Streaming
When you call `kickoff()` on a flow with streaming enabled, it returns a `FlowStreamingOutput` object that you can iterate over:
```python Code
flow = ResearchFlow()
# Start streaming execution
streaming = flow.kickoff()
# Iterate over chunks as they arrive
for chunk in streaming:
print(chunk.content, end="", flush=True)
# Access the final result after streaming completes
result = streaming.result
print(f"\n\nFinal output: {result}")
```
### Stream Chunk Information
Each chunk provides context about where it originated in the flow:
```python Code
streaming = flow.kickoff()
for chunk in streaming:
print(f"Agent: {chunk.agent_role}")
print(f"Task: {chunk.task_name}")
print(f"Content: {chunk.content}")
print(f"Type: {chunk.chunk_type}") # TEXT or TOOL_CALL
```
### Accessing Streaming Properties
The `FlowStreamingOutput` object provides useful properties and methods:
```python Code
streaming = flow.kickoff()
# Iterate and collect chunks
for chunk in streaming:
print(chunk.content, end="", flush=True)
# After iteration completes
print(f"\nCompleted: {streaming.is_completed}")
print(f"Full text: {streaming.get_full_text()}")
print(f"Total chunks: {len(streaming.chunks)}")
print(f"Final result: {streaming.result}")
```
## Asynchronous Streaming
For async applications, use `kickoff_async()` with async iteration:
```python Code
import asyncio
async def stream_flow():
flow = ResearchFlow()
# Start async streaming
streaming = await flow.kickoff_async()
# Async iteration over chunks
async for chunk in streaming:
print(chunk.content, end="", flush=True)
# Access final result
result = streaming.result
print(f"\n\nFinal output: {result}")
asyncio.run(stream_flow())
```
## Streaming with Multi-Step Flows
Streaming works seamlessly across multiple flow steps, including flows that execute multiple crews:
```python Code
from crewai.flow.flow import Flow, listen, start
from crewai import Agent, Crew, Task
class MultiStepFlow(Flow):
stream = True
@start()
def research_phase(self):
"""First crew: Research the topic."""
researcher = Agent(
role="Research Analyst",
goal="Gather comprehensive information",
backstory="Expert at finding relevant information",
)
task = Task(
description="Research AI developments in healthcare",
expected_output="Research findings on AI in healthcare",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
self.state["research"] = result.raw
return result.raw
@listen(research_phase)
def analysis_phase(self, research_data):
"""Second crew: Analyze the research."""
analyst = Agent(
role="Data Analyst",
goal="Analyze information and extract insights",
backstory="Expert at identifying patterns and trends",
)
task = Task(
description="Analyze this research: {research}",
expected_output="Key insights and trends",
agent=analyst,
)
crew = Crew(agents=[analyst], tasks=[task])
return crew.kickoff(inputs={"research": research_data})
# Stream across both phases
flow = MultiStepFlow()
streaming = flow.kickoff()
current_step = ""
for chunk in streaming:
# Track which flow step is executing
if chunk.task_name != current_step:
current_step = chunk.task_name
print(f"\n\n=== {chunk.task_name} ===\n")
print(chunk.content, end="", flush=True)
result = streaming.result
print(f"\n\nFinal analysis: {result}")
```
## Practical Example: Progress Dashboard
Here's a complete example showing how to build a progress dashboard with streaming:
```python Code
import asyncio
from crewai.flow.flow import Flow, listen, start
from crewai import Agent, Crew, Task
from crewai.types.streaming import StreamChunkType
class ResearchPipeline(Flow):
stream = True
@start()
def gather_data(self):
researcher = Agent(
role="Data Gatherer",
goal="Collect relevant information",
backstory="Skilled at finding quality sources",
)
task = Task(
description="Gather data on renewable energy trends",
expected_output="Collection of relevant data points",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
self.state["data"] = result.raw
return result.raw
@listen(gather_data)
def analyze_data(self, data):
analyst = Agent(
role="Data Analyst",
goal="Extract meaningful insights",
backstory="Expert at data analysis",
)
task = Task(
description="Analyze: {data}",
expected_output="Key insights and trends",
agent=analyst,
)
crew = Crew(agents=[analyst], tasks=[task])
return crew.kickoff(inputs={"data": data})
async def run_with_dashboard():
flow = ResearchPipeline()
print("="*60)
print("RESEARCH PIPELINE DASHBOARD")
print("="*60)
streaming = await flow.kickoff_async()
current_agent = ""
current_task = ""
chunk_count = 0
async for chunk in streaming:
chunk_count += 1
# Display phase transitions
if chunk.task_name != current_task:
current_task = chunk.task_name
current_agent = chunk.agent_role
print(f"\n\n📋 Phase: {current_task}")
print(f"👤 Agent: {current_agent}")
print("-" * 60)
# Display text output
if chunk.chunk_type == StreamChunkType.TEXT:
print(chunk.content, end="", flush=True)
# Display tool usage
elif chunk.chunk_type == StreamChunkType.TOOL_CALL and chunk.tool_call:
print(f"\n🔧 Tool: {chunk.tool_call.tool_name}")
# Show completion summary
result = streaming.result
print(f"\n\n{'='*60}")
print("PIPELINE COMPLETE")
print(f"{'='*60}")
print(f"Total chunks: {chunk_count}")
print(f"Final output length: {len(str(result))} characters")
asyncio.run(run_with_dashboard())
```
## Streaming with State Management
Streaming works naturally with Flow state management:
```python Code
from pydantic import BaseModel
class AnalysisState(BaseModel):
topic: str = ""
research: str = ""
insights: str = ""
class StatefulStreamingFlow(Flow[AnalysisState]):
stream = True
@start()
def research(self):
# State is available during streaming
topic = self.state.topic
print(f"Researching: {topic}")
researcher = Agent(
role="Researcher",
goal="Research topics thoroughly",
backstory="Expert researcher",
)
task = Task(
description=f"Research {topic}",
expected_output="Research findings",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
self.state.research = result.raw
return result.raw
@listen(research)
def analyze(self, research):
# Access updated state
print(f"Analyzing {len(self.state.research)} chars of research")
analyst = Agent(
role="Analyst",
goal="Extract insights",
backstory="Expert analyst",
)
task = Task(
description="Analyze: {research}",
expected_output="Key insights",
agent=analyst,
)
crew = Crew(agents=[analyst], tasks=[task])
result = crew.kickoff(inputs={"research": research})
self.state.insights = result.raw
return result.raw
# Run with streaming
flow = StatefulStreamingFlow()
streaming = flow.kickoff(inputs={"topic": "quantum computing"})
for chunk in streaming:
print(chunk.content, end="", flush=True)
result = streaming.result
print(f"\n\nFinal state:")
print(f"Topic: {flow.state.topic}")
print(f"Research length: {len(flow.state.research)}")
print(f"Insights length: {len(flow.state.insights)}")
```
## Use Cases
Flow streaming is particularly valuable for:
- **Multi-Stage Workflows**: Show progress across research, analysis, and synthesis phases
- **Complex Pipelines**: Provide visibility into long-running data processing flows
- **Interactive Applications**: Build responsive UIs that display intermediate results
- **Monitoring and Debugging**: Observe flow execution and crew interactions in real-time
- **Progress Tracking**: Show users which stage of the workflow is currently executing
- **Live Dashboards**: Create monitoring interfaces for production flows
## Stream Chunk Types
Like crew streaming, flow chunks can be of different types:
### TEXT Chunks
Standard text content from LLM responses:
```python Code
for chunk in streaming:
if chunk.chunk_type == StreamChunkType.TEXT:
print(chunk.content, end="", flush=True)
```
### TOOL_CALL Chunks
Information about tool calls within the flow:
```python Code
for chunk in streaming:
if chunk.chunk_type == StreamChunkType.TOOL_CALL and chunk.tool_call:
print(f"\nTool: {chunk.tool_call.tool_name}")
print(f"Args: {chunk.tool_call.arguments}")
```
## Error Handling
Handle errors gracefully during streaming:
```python Code
flow = ResearchFlow()
streaming = flow.kickoff()
try:
for chunk in streaming:
print(chunk.content, end="", flush=True)
result = streaming.result
print(f"\nSuccess! Result: {result}")
except Exception as e:
print(f"\nError during flow execution: {e}")
if streaming.is_completed:
print("Streaming completed but flow encountered an error")
```
## Important Notes
- Streaming automatically enables LLM streaming for any crews used within the flow
- You must iterate through all chunks before accessing the `.result` property
- Streaming works with both structured and unstructured flow state
- Flow streaming captures output from all crews and LLM calls in the flow
- Each chunk includes context about which agent and task generated it
- Streaming adds minimal overhead to flow execution
## Combining with Flow Visualization
You can combine streaming with flow visualization to provide a complete picture:
```python Code
# Generate flow visualization
flow = ResearchFlow()
flow.plot("research_flow") # Creates HTML visualization
# Run with streaming
streaming = flow.kickoff()
for chunk in streaming:
print(chunk.content, end="", flush=True)
result = streaming.result
print(f"\nFlow complete! View structure at: research_flow.html")
```
By leveraging flow streaming, you can build sophisticated, responsive applications that provide users with real-time visibility into complex multi-stage workflows, making your AI automations more transparent and engaging.

6
docs/en/mcp/dsl-integration.mdx
View File

@@ -60,9 +60,9 @@ Use the `#` syntax to select specific tools from a server:
"https://mcp.exa.ai/mcp?api_key=your_key#web_search_exa"
```
### CrewAI AMP Marketplace
### CrewAI AOP Marketplace
Access tools from the CrewAI AMP marketplace:
Access tools from the CrewAI AOP marketplace:
```python
# Full service with all tools
@@ -97,7 +97,7 @@ multi_source_agent = Agent(
"https://mcp.exa.ai/mcp?api_key=your_exa_key&profile=research",
"https://weather.api.com/mcp#get_current_conditions",
# CrewAI AMP marketplace
# CrewAI AOP marketplace
"crewai-amp:financial-insights",
"crewai-amp:academic-research#pubmed_search",
"crewai-amp:market-intelligence#competitor_analysis"

6
docs/en/mcp/overview.mdx
View File

@@ -17,7 +17,7 @@ Use the `mcps` field directly on agents for seamless MCP tool integration. The D
#### String-Based References (Quick Setup)
Perfect for remote HTTPS servers and CrewAI AMP marketplace:
Perfect for remote HTTPS servers and CrewAI AOP marketplace:
```python
from crewai import Agent
@@ -29,7 +29,7 @@ agent = Agent(
mcps=[
"https://mcp.exa.ai/mcp?api_key=your_key", # External MCP server
"https://api.weather.com/mcp#get_forecast", # Specific tool from server
"crewai-amp:financial-data", # CrewAI AMP marketplace
"crewai-amp:financial-data", # CrewAI AOP marketplace
"crewai-amp:research-tools#pubmed_search" # Specific AMP tool
]
)
@@ -203,7 +203,7 @@ mcps=[
]
```
#### CrewAI AMP Marketplace
#### CrewAI AOP Marketplace
```python
mcps=[

20
docs/en/observability/tracing.mdx
View File

@@ -1,6 +1,6 @@
---
title: CrewAI Tracing
description: Built-in tracing for CrewAI Crews and Flows with the CrewAI AMP platform
description: Built-in tracing for CrewAI Crews and Flows with the CrewAI AOP platform
icon: magnifying-glass-chart
mode: "wide"
---
@@ -9,7 +9,7 @@ mode: "wide"
CrewAI provides built-in tracing capabilities that allow you to monitor and debug your Crews and Flows in real-time. This guide demonstrates how to enable tracing for both **Crews** and **Flows** using CrewAI's integrated observability platform.
> **What is CrewAI Tracing?** CrewAI's built-in tracing provides comprehensive observability for your AI agents, including agent decisions, task execution timelines, tool usage, and LLM calls - all accessible through the [CrewAI AMP platform](https://app.crewai.com).
> **What is CrewAI Tracing?** CrewAI's built-in tracing provides comprehensive observability for your AI agents, including agent decisions, task execution timelines, tool usage, and LLM calls - all accessible through the [CrewAI AOP platform](https://app.crewai.com).
![CrewAI Tracing Interface](/images/crewai-tracing.png)
@@ -17,7 +17,7 @@ CrewAI provides built-in tracing capabilities that allow you to monitor and debu
Before you can use CrewAI tracing, you need:
1. **CrewAI AMP Account**: Sign up for a free account at [app.crewai.com](https://app.crewai.com)
1. **CrewAI AOP Account**: Sign up for a free account at [app.crewai.com](https://app.crewai.com)
2. **CLI Authentication**: Use the CrewAI CLI to authenticate your local environment
```bash
@@ -26,9 +26,9 @@ crewai login
## Setup Instructions
### Step 1: Create Your CrewAI AMP Account
### Step 1: Create Your CrewAI AOP Account
Visit [app.crewai.com](https://app.crewai.com) and create your free account. This will give you access to the CrewAI AMP platform where you can view traces, metrics, and manage your crews.
Visit [app.crewai.com](https://app.crewai.com) and create your free account. This will give you access to the CrewAI AOP platform where you can view traces, metrics, and manage your crews.
### Step 2: Install CrewAI CLI and Authenticate
@@ -38,7 +38,7 @@ If you haven't already, install CrewAI with the CLI tools:
uv add crewai[tools]
```
Then authenticate your CLI with your CrewAI AMP account:
Then authenticate your CLI with your CrewAI AOP account:
```bash
crewai login
@@ -47,7 +47,7 @@ crewai login
This command will:
1. Open your browser to the authentication page
2. Prompt you to enter a device code
3. Authenticate your local environment with your CrewAI AMP account
3. Authenticate your local environment with your CrewAI AOP account
4. Enable tracing capabilities for your local development
### Step 3: Enable Tracing in Your Crew
@@ -147,9 +147,9 @@ flow = ExampleFlow(tracing=True)
result = flow.kickoff()
```
### Step 5: View Traces in the CrewAI AMP Dashboard
### Step 5: View Traces in the CrewAI AOP Dashboard
After running the crew or flow, you can view the traces generated by your CrewAI application in the CrewAI AMP dashboard. You should see detailed steps of the agent interactions, tool usages, and LLM calls.
After running the crew or flow, you can view the traces generated by your CrewAI application in the CrewAI AOP dashboard. You should see detailed steps of the agent interactions, tool usages, and LLM calls.
Just click on the link below to view the traces or head over to the traces tab in the dashboard [here](https://app.crewai.com/crewai_plus/trace_batches)
![CrewAI Tracing Interface](/images/view-traces.png)
@@ -172,7 +172,7 @@ When this environment variable is set, all Crews and Flows will automatically ha
## Viewing Your Traces
### Access the CrewAI AMP Dashboard
### Access the CrewAI AOP Dashboard
1. Visit [app.crewai.com](https://app.crewai.com) and log in to your account
2. Navigate to your project dashboard

14
docs/en/quickstart.mdx
View File

@@ -209,9 +209,9 @@ Follow the steps below to get Crewing! 🚣‍♂️
</Step>
<Step title="Enterprise Alternative: Create in Crew Studio">
For CrewAI AMP users, you can create the same crew without writing code:
For CrewAI AOP users, you can create the same crew without writing code:
1. Log in to your CrewAI AMP account (create a free account at [app.crewai.com](https://app.crewai.com))
1. Log in to your CrewAI AOP account (create a free account at [app.crewai.com](https://app.crewai.com))
2. Open Crew Studio
3. Type what is the automation you're trying to build
4. Create your tasks visually and connect them in sequence
@@ -219,8 +219,8 @@ Follow the steps below to get Crewing! 🚣‍♂️
![Crew Studio Quickstart](/images/enterprise/crew-studio-interface.png)
<Card title="Try CrewAI AMP" icon="rocket" href="https://app.crewai.com">
Start your free account at CrewAI AMP
<Card title="Try CrewAI AOP" icon="rocket" href="https://app.crewai.com">
Start your free account at CrewAI AOP
</Card>
</Step>
<Step title="View your final report">
@@ -340,9 +340,9 @@ email_summarizer_task:
## Deploying Your Crew
The easiest way to deploy your crew to production is through [CrewAI AMP](http://app.crewai.com).
The easiest way to deploy your crew to production is through [CrewAI AOP](http://app.crewai.com).
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI.
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AOP](http://app.crewai.com) using the CLI.
<iframe
className="w-full aspect-video rounded-xl"
@@ -359,7 +359,7 @@ Watch this video tutorial for a step-by-step demonstration of deploying your cre
icon="rocket"
href="http://app.crewai.com"
>
Get started with CrewAI AMP and deploy your crew in a production environment with just a few clicks.
Get started with CrewAI AOP and deploy your crew in a production environment with just a few clicks.
</Card>
<Card
title="Join the Community"

528
docs/en/tools/ai-ml/ragtool.mdx
View File

@@ -77,7 +77,7 @@ The `RagTool` accepts the following parameters:
- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
- **adapter**: Optional. A custom adapter for the knowledge base. If not provided, a CrewAIRagAdapter will be used.
- **config**: Optional. Configuration for the underlying CrewAI RAG system.
- **config**: Optional. Configuration for the underlying CrewAI RAG system. Accepts a `RagToolConfig` TypedDict with optional `embedding_model` (ProviderSpec) and `vectordb` (VectorDbConfig) keys. All configuration values provided programmatically take precedence over environment variables.
## Adding Content
@@ -127,26 +127,528 @@ You can customize the behavior of the `RagTool` by providing a configuration dic
```python Code
from crewai_tools import RagTool
from crewai_tools.tools.rag import RagToolConfig, VectorDbConfig, ProviderSpec
# Create a RAG tool with custom configuration
config = {
"vectordb": {
"provider": "qdrant",
"config": {
"collection_name": "my-collection"
}
},
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small"
}
vectordb: VectorDbConfig = {
"provider": "qdrant",
"config": {
"collection_name": "my-collection"
}
}
embedding_model: ProviderSpec = {
"provider": "openai",
"config": {
"model_name": "text-embedding-3-small"
}
}
config: RagToolConfig = {
"vectordb": vectordb,
"embedding_model": embedding_model
}
rag_tool = RagTool(config=config, summarize=True)
```
## Embedding Model Configuration
The `embedding_model` parameter accepts a `crewai.rag.embeddings.types.ProviderSpec` dictionary with the structure:
```python
{
"provider": "provider-name", # Required
"config": { # Optional
# Provider-specific configuration
}
}
```
### Supported Providers
<AccordionGroup>
<Accordion title="OpenAI">
```python main.py
from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec
embedding_model: OpenAIProviderSpec = {
"provider": "openai",
"config": {
"api_key": "your-api-key",
"model_name": "text-embedding-ada-002",
"dimensions": 1536,
"organization_id": "your-org-id",
"api_base": "https://api.openai.com/v1",
"api_version": "v1",
"default_headers": {"Custom-Header": "value"}
}
}
```
**Config Options:**
- `api_key` (str): OpenAI API key
- `model_name` (str): Model to use. Default: `text-embedding-ada-002`. Options: `text-embedding-3-small`, `text-embedding-3-large`, `text-embedding-ada-002`
- `dimensions` (int): Number of dimensions for the embedding
- `organization_id` (str): OpenAI organization ID
- `api_base` (str): Custom API base URL
- `api_version` (str): API version
- `default_headers` (dict): Custom headers for API requests
**Environment Variables:**
- `OPENAI_API_KEY` or `EMBEDDINGS_OPENAI_API_KEY`: `api_key`
- `OPENAI_ORGANIZATION_ID` or `EMBEDDINGS_OPENAI_ORGANIZATION_ID`: `organization_id`
- `OPENAI_MODEL_NAME` or `EMBEDDINGS_OPENAI_MODEL_NAME`: `model_name`
- `OPENAI_API_BASE` or `EMBEDDINGS_OPENAI_API_BASE`: `api_base`
- `OPENAI_API_VERSION` or `EMBEDDINGS_OPENAI_API_VERSION`: `api_version`
- `OPENAI_DIMENSIONS` or `EMBEDDINGS_OPENAI_DIMENSIONS`: `dimensions`
</Accordion>
<Accordion title="Cohere">
```python main.py
from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec
embedding_model: CohereProviderSpec = {
"provider": "cohere",
"config": {
"api_key": "your-api-key",
"model_name": "embed-english-v3.0"
}
}
```
**Config Options:**
- `api_key` (str): Cohere API key
- `model_name` (str): Model to use. Default: `large`. Options: `embed-english-v3.0`, `embed-multilingual-v3.0`, `large`, `small`
**Environment Variables:**
- `COHERE_API_KEY` or `EMBEDDINGS_COHERE_API_KEY`: `api_key`
- `EMBEDDINGS_COHERE_MODEL_NAME`: `model_name`
</Accordion>
<Accordion title="VoyageAI">
```python main.py
from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec
embedding_model: VoyageAIProviderSpec = {
"provider": "voyageai",
"config": {
"api_key": "your-api-key",
"model": "voyage-3",
"input_type": "document",
"truncation": True,
"output_dtype": "float32",
"output_dimension": 1024,
"max_retries": 3,
"timeout": 60.0
}
}
```
**Config Options:**
- `api_key` (str): VoyageAI API key
- `model` (str): Model to use. Default: `voyage-2`. Options: `voyage-3`, `voyage-3-lite`, `voyage-code-3`, `voyage-large-2`
- `input_type` (str): Type of input. Options: `document` (for storage), `query` (for search)
- `truncation` (bool): Whether to truncate inputs that exceed max length. Default: `True`
- `output_dtype` (str): Output data type
- `output_dimension` (int): Dimension of output embeddings
- `max_retries` (int): Maximum number of retry attempts. Default: `0`
- `timeout` (float): Request timeout in seconds
**Environment Variables:**
- `VOYAGEAI_API_KEY` or `EMBEDDINGS_VOYAGEAI_API_KEY`: `api_key`
- `VOYAGEAI_MODEL` or `EMBEDDINGS_VOYAGEAI_MODEL`: `model`
- `VOYAGEAI_INPUT_TYPE` or `EMBEDDINGS_VOYAGEAI_INPUT_TYPE`: `input_type`
- `VOYAGEAI_TRUNCATION` or `EMBEDDINGS_VOYAGEAI_TRUNCATION`: `truncation`
- `VOYAGEAI_OUTPUT_DTYPE` or `EMBEDDINGS_VOYAGEAI_OUTPUT_DTYPE`: `output_dtype`
- `VOYAGEAI_OUTPUT_DIMENSION` or `EMBEDDINGS_VOYAGEAI_OUTPUT_DIMENSION`: `output_dimension`
- `VOYAGEAI_MAX_RETRIES` or `EMBEDDINGS_VOYAGEAI_MAX_RETRIES`: `max_retries`
- `VOYAGEAI_TIMEOUT` or `EMBEDDINGS_VOYAGEAI_TIMEOUT`: `timeout`
</Accordion>
<Accordion title="Ollama">
```python main.py
from crewai.rag.embeddings.providers.ollama.types import OllamaProviderSpec
embedding_model: OllamaProviderSpec = {
"provider": "ollama",
"config": {
"model_name": "llama2",
"url": "http://localhost:11434/api/embeddings"
}
}
```
**Config Options:**
- `model_name` (str): Ollama model name (e.g., `llama2`, `mistral`, `nomic-embed-text`)
- `url` (str): Ollama API endpoint URL. Default: `http://localhost:11434/api/embeddings`
**Environment Variables:**
- `OLLAMA_MODEL` or `EMBEDDINGS_OLLAMA_MODEL`: `model_name`
- `OLLAMA_URL` or `EMBEDDINGS_OLLAMA_URL`: `url`
</Accordion>
<Accordion title="Amazon Bedrock">
```python main.py
from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
embedding_model: BedrockProviderSpec = {
"provider": "amazon-bedrock",
"config": {
"model_name": "amazon.titan-embed-text-v2:0",
"session": boto3_session
}
}
```
**Config Options:**
- `model_name` (str): Bedrock model ID. Default: `amazon.titan-embed-text-v1`. Options: `amazon.titan-embed-text-v1`, `amazon.titan-embed-text-v2:0`, `cohere.embed-english-v3`, `cohere.embed-multilingual-v3`
- `session` (Any): Boto3 session object for AWS authentication
**Environment Variables:**
- `AWS_ACCESS_KEY_ID`: AWS access key
- `AWS_SECRET_ACCESS_KEY`: AWS secret key
- `AWS_REGION`: AWS region (e.g., `us-east-1`)
</Accordion>
<Accordion title="Azure OpenAI">
```python main.py
from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec
embedding_model: AzureProviderSpec = {
"provider": "azure",
"config": {
"deployment_id": "your-deployment-id",
"api_key": "your-api-key",
"api_base": "https://your-resource.openai.azure.com",
"api_version": "2024-02-01",
"model_name": "text-embedding-ada-002",
"api_type": "azure"
}
}
```
**Config Options:**
- `deployment_id` (str): **Required** - Azure OpenAI deployment ID
- `api_key` (str): Azure OpenAI API key
- `api_base` (str): Azure OpenAI resource endpoint
- `api_version` (str): API version. Example: `2024-02-01`
- `model_name` (str): Model name. Default: `text-embedding-ada-002`
- `api_type` (str): API type. Default: `azure`
- `dimensions` (int): Output dimensions
- `default_headers` (dict): Custom headers
**Environment Variables:**
- `AZURE_OPENAI_API_KEY` or `EMBEDDINGS_AZURE_API_KEY`: `api_key`
- `AZURE_OPENAI_ENDPOINT` or `EMBEDDINGS_AZURE_API_BASE`: `api_base`
- `EMBEDDINGS_AZURE_DEPLOYMENT_ID`: `deployment_id`
- `EMBEDDINGS_AZURE_API_VERSION`: `api_version`
- `EMBEDDINGS_AZURE_MODEL_NAME`: `model_name`
- `EMBEDDINGS_AZURE_API_TYPE`: `api_type`
- `EMBEDDINGS_AZURE_DIMENSIONS`: `dimensions`
</Accordion>
<Accordion title="Google Generative AI">
```python main.py
from crewai.rag.embeddings.providers.google.types import GenerativeAiProviderSpec
embedding_model: GenerativeAiProviderSpec = {
"provider": "google-generativeai",
"config": {
"api_key": "your-api-key",
"model_name": "gemini-embedding-001",
"task_type": "RETRIEVAL_DOCUMENT"
}
}
```
**Config Options:**
- `api_key` (str): Google AI API key
- `model_name` (str): Model name. Default: `gemini-embedding-001`. Options: `gemini-embedding-001`, `text-embedding-005`, `text-multilingual-embedding-002`
- `task_type` (str): Task type for embeddings. Default: `RETRIEVAL_DOCUMENT`. Options: `RETRIEVAL_DOCUMENT`, `RETRIEVAL_QUERY`
**Environment Variables:**
- `GOOGLE_API_KEY`, `GEMINI_API_KEY`, or `EMBEDDINGS_GOOGLE_API_KEY`: `api_key`
- `EMBEDDINGS_GOOGLE_GENERATIVE_AI_MODEL_NAME`: `model_name`
- `EMBEDDINGS_GOOGLE_GENERATIVE_AI_TASK_TYPE`: `task_type`
</Accordion>
<Accordion title="Google Vertex AI">
```python main.py
from crewai.rag.embeddings.providers.google.types import VertexAIProviderSpec
embedding_model: VertexAIProviderSpec = {
"provider": "google-vertex",
"config": {
"model_name": "text-embedding-004",
"project_id": "your-project-id",
"region": "us-central1",
"api_key": "your-api-key"
}
}
```
**Config Options:**
- `model_name` (str): Model name. Default: `textembedding-gecko`. Options: `text-embedding-004`, `textembedding-gecko`, `textembedding-gecko-multilingual`
- `project_id` (str): Google Cloud project ID. Default: `cloud-large-language-models`
- `region` (str): Google Cloud region. Default: `us-central1`
- `api_key` (str): API key for authentication
**Environment Variables:**
- `GOOGLE_APPLICATION_CREDENTIALS`: Path to service account JSON file
- `GOOGLE_CLOUD_PROJECT` or `EMBEDDINGS_GOOGLE_VERTEX_PROJECT_ID`: `project_id`
- `EMBEDDINGS_GOOGLE_VERTEX_MODEL_NAME`: `model_name`
- `EMBEDDINGS_GOOGLE_VERTEX_REGION`: `region`
- `EMBEDDINGS_GOOGLE_VERTEX_API_KEY`: `api_key`
</Accordion>
<Accordion title="Jina AI">
```python main.py
from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec
embedding_model: JinaProviderSpec = {
"provider": "jina",
"config": {
"api_key": "your-api-key",
"model_name": "jina-embeddings-v3"
}
}
```
**Config Options:**
- `api_key` (str): Jina AI API key
- `model_name` (str): Model name. Default: `jina-embeddings-v2-base-en`. Options: `jina-embeddings-v3`, `jina-embeddings-v2-base-en`, `jina-embeddings-v2-small-en`
**Environment Variables:**
- `JINA_API_KEY` or `EMBEDDINGS_JINA_API_KEY`: `api_key`
- `EMBEDDINGS_JINA_MODEL_NAME`: `model_name`
</Accordion>
<Accordion title="HuggingFace">
```python main.py
from crewai.rag.embeddings.providers.huggingface.types import HuggingFaceProviderSpec
embedding_model: HuggingFaceProviderSpec = {
"provider": "huggingface",
"config": {
"url": "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
}
}
```
**Config Options:**
- `url` (str): Full URL to HuggingFace inference API endpoint
**Environment Variables:**
- `HUGGINGFACE_URL` or `EMBEDDINGS_HUGGINGFACE_URL`: `url`
</Accordion>
<Accordion title="Instructor">
```python main.py
from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec
embedding_model: InstructorProviderSpec = {
"provider": "instructor",
"config": {
"model_name": "hkunlp/instructor-xl",
"device": "cuda",
"instruction": "Represent the document"
}
}
```
**Config Options:**
- `model_name` (str): HuggingFace model ID. Default: `hkunlp/instructor-base`. Options: `hkunlp/instructor-xl`, `hkunlp/instructor-large`, `hkunlp/instructor-base`
- `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`, `mps`
- `instruction` (str): Instruction prefix for embeddings
**Environment Variables:**
- `EMBEDDINGS_INSTRUCTOR_MODEL_NAME`: `model_name`
- `EMBEDDINGS_INSTRUCTOR_DEVICE`: `device`
- `EMBEDDINGS_INSTRUCTOR_INSTRUCTION`: `instruction`
</Accordion>
<Accordion title="Sentence Transformer">
```python main.py
from crewai.rag.embeddings.providers.sentence_transformer.types import SentenceTransformerProviderSpec
embedding_model: SentenceTransformerProviderSpec = {
"provider": "sentence-transformer",
"config": {
"model_name": "all-mpnet-base-v2",
"device": "cuda",
"normalize_embeddings": True
}
}
```
**Config Options:**
- `model_name` (str): Sentence Transformers model name. Default: `all-MiniLM-L6-v2`. Options: `all-mpnet-base-v2`, `all-MiniLM-L6-v2`, `paraphrase-multilingual-MiniLM-L12-v2`
- `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`, `mps`
- `normalize_embeddings` (bool): Whether to normalize embeddings. Default: `False`
**Environment Variables:**
- `EMBEDDINGS_SENTENCE_TRANSFORMER_MODEL_NAME`: `model_name`
- `EMBEDDINGS_SENTENCE_TRANSFORMER_DEVICE`: `device`
- `EMBEDDINGS_SENTENCE_TRANSFORMER_NORMALIZE_EMBEDDINGS`: `normalize_embeddings`
</Accordion>
<Accordion title="ONNX">
```python main.py
from crewai.rag.embeddings.providers.onnx.types import ONNXProviderSpec
embedding_model: ONNXProviderSpec = {
"provider": "onnx",
"config": {
"preferred_providers": ["CUDAExecutionProvider", "CPUExecutionProvider"]
}
}
```
**Config Options:**
- `preferred_providers` (list[str]): List of ONNX execution providers in order of preference
**Environment Variables:**
- `EMBEDDINGS_ONNX_PREFERRED_PROVIDERS`: `preferred_providers` (comma-separated list)
</Accordion>
<Accordion title="OpenCLIP">
```python main.py
from crewai.rag.embeddings.providers.openclip.types import OpenCLIPProviderSpec
embedding_model: OpenCLIPProviderSpec = {
"provider": "openclip",
"config": {
"model_name": "ViT-B-32",
"checkpoint": "laion2b_s34b_b79k",
"device": "cuda"
}
}
```
**Config Options:**
- `model_name` (str): OpenCLIP model architecture. Default: `ViT-B-32`. Options: `ViT-B-32`, `ViT-B-16`, `ViT-L-14`
- `checkpoint` (str): Pretrained checkpoint name. Default: `laion2b_s34b_b79k`. Options: `laion2b_s34b_b79k`, `laion400m_e32`, `openai`
- `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`
**Environment Variables:**
- `EMBEDDINGS_OPENCLIP_MODEL_NAME`: `model_name`
- `EMBEDDINGS_OPENCLIP_CHECKPOINT`: `checkpoint`
- `EMBEDDINGS_OPENCLIP_DEVICE`: `device`
</Accordion>
<Accordion title="Text2Vec">
```python main.py
from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec
embedding_model: Text2VecProviderSpec = {
"provider": "text2vec",
"config": {
"model_name": "shibing624/text2vec-base-multilingual"
}
}
```
**Config Options:**
- `model_name` (str): Text2Vec model name from HuggingFace. Default: `shibing624/text2vec-base-chinese`. Options: `shibing624/text2vec-base-multilingual`, `shibing624/text2vec-base-chinese`
**Environment Variables:**
- `EMBEDDINGS_TEXT2VEC_MODEL_NAME`: `model_name`
</Accordion>
<Accordion title="Roboflow">
```python main.py
from crewai.rag.embeddings.providers.roboflow.types import RoboflowProviderSpec
embedding_model: RoboflowProviderSpec = {
"provider": "roboflow",
"config": {
"api_key": "your-api-key",
"api_url": "https://infer.roboflow.com"
}
}
```
**Config Options:**
- `api_key` (str): Roboflow API key. Default: `""` (empty string)
- `api_url` (str): Roboflow inference API URL. Default: `https://infer.roboflow.com`
**Environment Variables:**
- `ROBOFLOW_API_KEY` or `EMBEDDINGS_ROBOFLOW_API_KEY`: `api_key`
- `ROBOFLOW_API_URL` or `EMBEDDINGS_ROBOFLOW_API_URL`: `api_url`
</Accordion>
<Accordion title="WatsonX (IBM)">
```python main.py
from crewai.rag.embeddings.providers.ibm.types import WatsonXProviderSpec
embedding_model: WatsonXProviderSpec = {
"provider": "watsonx",
"config": {
"model_id": "ibm/slate-125m-english-rtrvr",
"url": "https://us-south.ml.cloud.ibm.com",
"api_key": "your-api-key",
"project_id": "your-project-id",
"batch_size": 100,
"concurrency_limit": 10,
"persistent_connection": True
}
}
```
**Config Options:**
- `model_id` (str): WatsonX model identifier
- `url` (str): WatsonX API endpoint
- `api_key` (str): IBM Cloud API key
- `project_id` (str): WatsonX project ID
- `space_id` (str): WatsonX space ID (alternative to project_id)
- `batch_size` (int): Batch size for embeddings. Default: `100`
- `concurrency_limit` (int): Maximum concurrent requests. Default: `10`
- `persistent_connection` (bool): Use persistent connections. Default: `True`
- Plus 20+ additional authentication and configuration options
**Environment Variables:**
- `WATSONX_API_KEY` or `EMBEDDINGS_WATSONX_API_KEY`: `api_key`
- `WATSONX_URL` or `EMBEDDINGS_WATSONX_URL`: `url`
- `WATSONX_PROJECT_ID` or `EMBEDDINGS_WATSONX_PROJECT_ID`: `project_id`
- `EMBEDDINGS_WATSONX_MODEL_ID`: `model_id`
- `EMBEDDINGS_WATSONX_SPACE_ID`: `space_id`
- `EMBEDDINGS_WATSONX_BATCH_SIZE`: `batch_size`
- `EMBEDDINGS_WATSONX_CONCURRENCY_LIMIT`: `concurrency_limit`
- `EMBEDDINGS_WATSONX_PERSISTENT_CONNECTION`: `persistent_connection`
</Accordion>
<Accordion title="Custom">
```python main.py
from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec
class MyEmbeddingFunction(EmbeddingFunction):
def __call__(self, input):
# Your custom embedding logic
return embeddings
embedding_model: CustomProviderSpec = {
"provider": "custom",
"config": {
"embedding_callable": MyEmbeddingFunction
}
}
```
**Config Options:**
- `embedding_callable` (type[EmbeddingFunction]): Custom embedding function class
**Note:** Custom embedding functions must implement the `EmbeddingFunction` protocol defined in `crewai.rag.core.base_embeddings_callable`. The `__call__` method should accept input data and return embeddings as a list of numpy arrays (or compatible format that will be normalized). The returned embeddings are automatically normalized and validated.
</Accordion>
</AccordionGroup>
### Notes
- All config fields are optional unless marked as **Required**
- API keys can typically be provided via environment variables instead of config
- Default values are shown where applicable
## Conclusion
The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.

6
docs/en/tools/database-data/mysqltool.mdx
View File

@@ -58,10 +58,10 @@ tool = MySQLSearchTool(
),
),
embedder=dict(
provider="google",
provider="google-generativeai",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/database-data/pgsearchtool.mdx
View File

@@ -71,10 +71,10 @@ tool = PGSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/file-document/jsonsearchtool.mdx
View File

@@ -64,10 +64,10 @@ tool = JSONSearchTool(
},
},
"embedding_model": {
"provider": "google", # or openai, ollama, ...
"provider": "google-generativeai", # or openai, ollama, ...
"config": {
"model": "models/embedding-001",
"task_type": "retrieval_document",
"model_name": "gemini-embedding-001",
"task_type": "RETRIEVAL_DOCUMENT",
# Further customization options can be added here.
},
},

8
docs/en/tools/file-document/pdfsearchtool.mdx
View File

@@ -63,15 +63,15 @@ tool = PDFSearchTool(
"config": {
# Model identifier for the chosen provider. "model" will be auto-mapped to "model_name" internally.
"model": "text-embedding-3-small",
# Optional: API key. If omitted, the tool will use provider-specific env vars when available
# (e.g., OPENAI_API_KEY for provider="openai").
# Optional: API key. If omitted, the tool will use provider-specific env vars
# (e.g., OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY for OpenAI).
# "api_key": "sk-...",
# Provider-specific examples:
# --- Google Generative AI ---
# (Set provider="google-generativeai" above)
# "model": "models/embedding-001",
# "task_type": "retrieval_document",
# "model_name": "gemini-embedding-001",
# "task_type": "RETRIEVAL_DOCUMENT",
# "title": "Embeddings",
# --- Cohere ---

4
docs/en/tools/file-document/txtsearchtool.mdx
View File

@@ -66,9 +66,9 @@ tool = TXTSearchTool(
"provider": "openai", # or google-generativeai, cohere, ollama, ...
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...", # optional if env var is set
# "api_key": "sk-...", # optional if env var is set (e.g., OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY)
# Provider examples:
# Google → model: "models/embedding-001", task_type: "retrieval_document"
# Google → model_name: "gemini-embedding-001", task_type: "RETRIEVAL_DOCUMENT"
# Cohere → model: "embed-english-v3.0"
# Ollama → model: "nomic-embed-text"
},

6
docs/en/tools/search-research/codedocssearchtool.mdx
View File

@@ -73,10 +73,10 @@ tool = CodeDocsSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/search-research/githubsearchtool.mdx
View File

@@ -75,10 +75,10 @@ tool = GithubSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/search-research/websitesearchtool.mdx
View File

@@ -66,10 +66,10 @@ tool = WebsiteSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/search-research/youtubechannelsearchtool.mdx
View File

@@ -106,10 +106,10 @@ youtube_channel_tool = YoutubeChannelSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

6
docs/en/tools/search-research/youtubevideosearchtool.mdx
View File

@@ -108,10 +108,10 @@ youtube_search_tool = YoutubeVideoSearchTool(
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),

12
docs/enterprise-api.base.yaml
View File

@@ -1,12 +1,12 @@
openapi: 3.0.3
info:
title: CrewAI AMP API
title: CrewAI AOP API
description: |
REST API for interacting with your deployed CrewAI crews on CrewAI AMP.
REST API for interacting with your deployed CrewAI crews on CrewAI AOP.
## Getting Started
1. **Find your crew URL**: Get your unique crew URL from the CrewAI AMP dashboard
1. **Find your crew URL**: Get your unique crew URL from the CrewAI AOP dashboard
2. **Copy examples**: Use the code examples from each endpoint page as templates
3. **Replace placeholders**: Update URLs and tokens with your actual values
4. **Test with your tools**: Use cURL, Postman, or your preferred API client
@@ -18,7 +18,7 @@ info:
- **Bearer Token**: Organization-level token for full crew operations
- **User Bearer Token**: User-scoped token for individual access with limited permissions
You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI AMP dashboard.
You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI AOP dashboard.
## Reference Documentation
@@ -43,7 +43,7 @@ info:
url: https://crewai.com
servers:
- url: https://your-actual-crew-name.crewai.com
description: Replace with your actual deployed crew URL from the CrewAI AMP dashboard
description: Replace with your actual deployed crew URL from the CrewAI AOP dashboard
- url: https://my-travel-crew.crewai.com
description: Example travel planning crew (replace with your URL)
- url: https://content-creation-crew.crewai.com
@@ -412,7 +412,7 @@ components:
description: |
**📋 Reference Documentation** - *The tokens shown in examples are placeholders for reference only.*
Use your actual Bearer Token or User Bearer Token from the CrewAI AMP dashboard for real API calls.
Use your actual Bearer Token or User Bearer Token from the CrewAI AOP dashboard for real API calls.
**Bearer Token**: Organization-level access for full crew operations
**User Bearer Token**: User-scoped access with limited permissions

12
docs/enterprise-api.en.yaml
View File

@@ -1,12 +1,12 @@
openapi: 3.0.3
info:
title: CrewAI AMP API
title: CrewAI AOP API
description: |
REST API for interacting with your deployed CrewAI crews on CrewAI AMP.
REST API for interacting with your deployed CrewAI crews on CrewAI AOP.
## Getting Started
1. **Find your crew URL**: Get your unique crew URL from the CrewAI AMP dashboard
1. **Find your crew URL**: Get your unique crew URL from the CrewAI AOP dashboard
2. **Copy examples**: Use the code examples from each endpoint page as templates
3. **Replace placeholders**: Update URLs and tokens with your actual values
4. **Test with your tools**: Use cURL, Postman, or your preferred API client
@@ -18,7 +18,7 @@ info:
- **Bearer Token**: Organization-level token for full crew operations
- **User Bearer Token**: User-scoped token for individual access with limited permissions
You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI AMP dashboard.
You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI AOP dashboard.
## Reference Documentation
@@ -43,7 +43,7 @@ info:
url: https://crewai.com
servers:
- url: https://your-actual-crew-name.crewai.com
description: Replace with your actual deployed crew URL from the CrewAI AMP dashboard
description: Replace with your actual deployed crew URL from the CrewAI AOP dashboard
- url: https://my-travel-crew.crewai.com
description: Example travel planning crew (replace with your URL)
- url: https://content-creation-crew.crewai.com
@@ -412,7 +412,7 @@ components:
description: |
**📋 Reference Documentation** - *The tokens shown in examples are placeholders for reference only.*
Use your actual Bearer Token or User Bearer Token from the CrewAI AMP dashboard for real API calls.
Use your actual Bearer Token or User Bearer Token from the CrewAI AOP dashboard for real API calls.
**Bearer Token**: Organization-level access for full crew operations
**User Bearer Token**: User-scoped access with limited permissions

2
docs/enterprise-api.ko.yaml
View File

@@ -2,7 +2,7 @@ openapi: 3.0.3
info:
title: CrewAI 엔터프라이즈 API
description: |
CrewAI AMP에 배포된 crew와 상호작용하기 위한 REST API입니다.
CrewAI AOP에 배포된 crew와 상호작용하기 위한 REST API입니다.
## 시작하기
1. **Crew URL 확인**: 대시보드에서 고유한 crew URL을 확인하세요

12
docs/enterprise-api.pt-BR.yaml
View File

@@ -1,12 +1,12 @@
openapi: 3.0.3
info:
title: CrewAI AMP API
title: CrewAI AOP API
description: |
REST API para interagir com suas crews implantadas no CrewAI AMP.
REST API para interagir com suas crews implantadas no CrewAI AOP.
## Introdução
1. **Encontre a URL da sua crew**: Obtenha sua URL única no painel do CrewAI AMP
1. **Encontre a URL da sua crew**: Obtenha sua URL única no painel do CrewAI AOP
2. **Copie os exemplos**: Use os exemplos de cada endpoint como modelo
3. **Substitua os placeholders**: Atualize URLs e tokens com seus valores reais
4. **Teste com suas ferramentas**: Use cURL, Postman ou seu cliente preferido
@@ -18,7 +18,7 @@ info:
- **Bearer Token**: Token em nível de organização para operações completas
- **User Bearer Token**: Token com escopo de usuário com permissões limitadas
Você encontra os tokens na aba Status da sua crew no painel do CrewAI AMP.
Você encontra os tokens na aba Status da sua crew no painel do CrewAI AOP.
## Documentação de Referência
@@ -43,7 +43,7 @@ info:
url: https://crewai.com
servers:
- url: https://your-actual-crew-name.crewai.com
description: Substitua pela URL real da sua crew no painel do CrewAI AMP
description: Substitua pela URL real da sua crew no painel do CrewAI AOP
security:
- BearerAuth: []
paths:
@@ -291,7 +291,7 @@ components:
scheme: bearer
description: |
**📋 Referência** - *Os tokens mostrados são apenas exemplos.*
Use seus tokens reais do painel do CrewAI AMP.
Use seus tokens reais do painel do CrewAI AOP.
schemas:
ExecutionRunning:

8
docs/ko/api-reference/introduction.mdx
View File

@@ -1,6 +1,6 @@
---
title: "소개"
description: "CrewAI AMP REST API에 대한 완벽한 참고 자료"
description: "CrewAI AOP REST API에 대한 완벽한 참고 자료"
icon: "code"
mode: "wide"
---
@@ -13,7 +13,7 @@ CrewAI 엔터프라이즈 API 참고 자료에 오신 것을 환영합니다.
<Steps>
<Step title="API 자격 증명 받기">
CrewAI AMP 대시보드에서 자신의 crew 상세 페이지로 이동하여 Status 탭에서 Bearer Token을 복사하세요.
CrewAI AOP 대시보드에서 자신의 crew 상세 페이지로 이동하여 Status 탭에서 Bearer Token을 복사하세요.
</Step>
<Step title="필수 입력값 확인하기">
@@ -46,7 +46,7 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
| **User Bearer Token** | 사용자 범위 접근 | 제한된 권한, 사용자별 작업에 적합 |
<Tip>
두 토큰 유형 모두 CrewAI AMP 대시보드의 crew 상세 페이지 Status 탭에서 확인할 수 있습니다.
두 토큰 유형 모두 CrewAI AOP 대시보드의 crew 상세 페이지 Status 탭에서 확인할 수 있습니다.
</Tip>
## 기본 URL
@@ -82,7 +82,7 @@ API는 표준 HTTP 상태 코드를 사용합니다:
## 인터랙티브 테스트
<Info>
**왜 "전송" 버튼이 없나요?** 각 CrewAI AMP 사용자는 고유한 crew URL을 가지므로, 혼동을 피하기 위해 인터랙티브 플레이그라운드 대신 **참조 모드**를 사용합니다. 이를 통해 비작동 전송 버튼 없이 요청이 어떻게 생겼는지 정확히 보여줍니다.
**왜 "전송" 버튼이 없나요?** 각 CrewAI AOP 사용자는 고유한 crew URL을 가지므로, 혼동을 피하기 위해 인터랙티브 플레이그라운드 대신 **참조 모드**를 사용합니다. 이를 통해 비작동 전송 버튼 없이 요청이 어떻게 생겼는지 정확히 보여줍니다.
</Info>
각 엔드포인트 페이지에서는 다음을 확인할 수 있습니다:

2
docs/ko/concepts/agents.mdx
View File

@@ -20,7 +20,7 @@ CrewAI 프레임워크에서 `Agent`는 다음과 같은 역할을 수행하는
</Tip>
<Note type="info" title="엔터프라이즈 확장: 시각적 에이전트 빌더">
CrewAI AMP에는 코드를 작성하지 않고도 에이전트 생성 및 구성을 간편하게 할 수 있는 시각적 에이전트 빌더가 포함되어 있습니다. 에이전트를 시각적으로 설계하고 실시간으로 테스트하세요.
CrewAI AOP에는 코드를 작성하지 않고도 에이전트 생성 및 구성을 간편하게 할 수 있는 시각적 에이전트 빌더가 포함되어 있습니다. 에이전트를 시각적으로 설계하고 실시간으로 테스트하세요.
![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)

22
docs/ko/concepts/cli.mdx
View File

@@ -5,7 +5,7 @@ icon: terminal
mode: "wide"
---
<Warning>릴리즈 0.140.0부터 CrewAI AMP는 로그인 제공자 마이그레이션 프로세스를 시작했습니다. 이에 따라 CLI를 통한 인증 흐름이 업데이트되었습니다. Google을 통해 로그인하거나 2025년 7월 3일 이후에 계정을 생성한 사용자는 이전 버전의 `crewai` 라이브러리로는 로그인할 수 없습니다.</Warning>
<Warning>릴리즈 0.140.0부터 CrewAI AOP는 로그인 제공자 마이그레이션 프로세스를 시작했습니다. 이에 따라 CLI를 통한 인증 흐름이 업데이트되었습니다. Google을 통해 로그인하거나 2025년 7월 3일 이후에 계정을 생성한 사용자는 이전 버전의 `crewai` 라이브러리로는 로그인할 수 없습니다.</Warning>
## 개요
@@ -186,9 +186,9 @@ def crew(self) -> Crew:
### 10. 배포
crew 또는 flow를 [CrewAI AMP](https://app.crewai.com)에 배포하세요.
crew 또는 flow를 [CrewAI AOP](https://app.crewai.com)에 배포하세요.
- **인증**: CrewAI AMP에 배포하려면 인증이 필요합니다.
- **인증**: CrewAI AOP에 배포하려면 인증이 필요합니다.
아래 명령어로 로그인하거나 계정을 생성할 수 있습니다:
```shell Terminal
crewai login
@@ -203,7 +203,7 @@ crew 또는 flow를 [CrewAI AMP](https://app.crewai.com)에 배포하세요.
### 11. 조직 관리
CrewAI AMP 조직을 관리합니다.
CrewAI AOP 조직을 관리합니다.
```shell Terminal
crewai org [COMMAND] [OPTIONS]
@@ -227,17 +227,17 @@ crewai org switch <organization_id>
```
<Note>
이러한 조직 관리 명령어를 사용하려면 CrewAI AMP에 인증되어 있어야 합니다.
이러한 조직 관리 명령어를 사용하려면 CrewAI AOP에 인증되어 있어야 합니다.
</Note>
- **배포 생성** (계속):
- 배포를 해당 원격 GitHub 저장소에 연결합니다 (일반적으로 자동으로 감지됩니다).
- **Crew 배포**: 인증이 완료되면 crew 또는 flow를 CrewAI AMP에 배포할 수 있습니다.
- **Crew 배포**: 인증이 완료되면 crew 또는 flow를 CrewAI AOP에 배포할 수 있습니다.
```shell Terminal
crewai deploy push
```
- CrewAI AMP 플랫폼에서 배포 프로세스를 시작합니다.
- CrewAI AOP 플랫폼에서 배포 프로세스를 시작합니다.
- 성공적으로 시작되면, Deployment created successfully! 메시지와 함께 Deployment Name 및 고유한 Deployment ID(UUID)가 출력됩니다.
- **배포 상태**: 배포 상태를 확인하려면 다음을 사용합니다:
@@ -262,7 +262,7 @@ crewai org switch <organization_id>
```shell Terminal
crewai deploy remove
```
이 명령은 CrewAI AMP 플랫폼에서 배포를 삭제합니다.
이 명령은 CrewAI AOP 플랫폼에서 배포를 삭제합니다.
- **도움말 명령어**: CLI에 대한 도움말을 보려면 다음을 사용합니다:
```shell Terminal
@@ -270,7 +270,7 @@ crewai org switch <organization_id>
```
이 명령은 CrewAI Deploy CLI에 대한 도움말 메시지를 표시합니다.
CLI를 사용하여 [CrewAI AMP](http://app.crewai.com)에 crew를 배포하는 단계별 시연은 아래 비디오 튜토리얼을 참조하십시오.
CLI를 사용하여 [CrewAI AOP](http://app.crewai.com)에 crew를 배포하는 단계별 시연은 아래 비디오 튜토리얼을 참조하십시오.
<iframe
className="w-full aspect-video rounded-xl"
@@ -336,7 +336,7 @@ crewai config reset
#### 사용 가능한 구성 파라미터
- `enterprise_base_url`: CrewAI AMP 인스턴스의 기본 URL
- `enterprise_base_url`: CrewAI AOP 인스턴스의 기본 URL
- `oauth2_provider`: 인증에 사용되는 OAuth2 공급자 (예: workos, okta, auth0)
- `oauth2_audience`: OAuth2 audience 값으로, 일반적으로 대상 API 또는 리소스를 식별하는 데 사용됨
- `oauth2_client_id`: 인증 요청 시 사용되는 공급자가 발급한 OAuth2 클라이언트 ID
@@ -353,7 +353,7 @@ crewai config list
| 설정 | 값 | 설명 |
| :------------------- | :---------------------- | :------------------------------------------------------------------- |
| enterprise_base_url | https://app.crewai.com | CrewAI AMP 인스턴스의 기본 URL |
| enterprise_base_url | https://app.crewai.com | CrewAI AOP 인스턴스의 기본 URL |
| org_name | Not set | 현재 활성화된 조직의 이름 |
| org_uuid | Not set | 현재 활성화된 조직의 UUID |
| oauth2_provider | workos | 인증에 사용되는 OAuth2 제공자 (예: workos, okta, auth0) |

2
docs/ko/concepts/event-listener.mdx
View File

@@ -20,7 +20,7 @@ CrewAI는 실행 수명 주기 전반에 걸쳐 이벤트를 발생시키는 이
CrewAI에서 특정 동작(예: Crew가 실행을 시작하거나 Agent가 task를 완료하거나 tool이 사용될 때)이 발생하면, 시스템은 해당 이벤트를 발생시킵니다. 이러한 이벤트에 대한 핸들러를 등록하여 해당 이벤트가 발생할 때 커스텀 코드를 실행할 수 있습니다.
<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
CrewAI AMP는 event 시스템을 활용하여 모든 prompt, completion 및 관련 메타데이터를 추적, 저장 및 시각화하는 내장 Prompt Tracing 기능을 제공합니다. 이 기능을 통해 agent 운영에 대한 강력한 디버깅 기능과 투명성을 얻을 수 있습니다.
CrewAI AOP는 event 시스템을 활용하여 모든 prompt, completion 및 관련 메타데이터를 추적, 저장 및 시각화하는 내장 Prompt Tracing 기능을 제공합니다. 이 기능을 통해 agent 운영에 대한 강력한 디버깅 기능과 투명성을 얻을 수 있습니다.
![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)

6
docs/ko/enterprise/features/rbac.mdx
View File

@@ -7,10 +7,10 @@ mode: "wide"
## 개요
CrewAI AMP의 RBAC는 **조직 수준 역할**과 **자동화(Automation) 수준 가시성**을 결합하여 안전하고 확장 가능한 접근 제어를 제공합니다.
CrewAI AOP의 RBAC는 **조직 수준 역할**과 **자동화(Automation) 수준 가시성**을 결합하여 안전하고 확장 가능한 접근 제어를 제공합니다.
<Frame>
<img src="/images/enterprise/users_and_roles.png" alt="CrewAI AMP RBAC 개요" />
<img src="/images/enterprise/users_and_roles.png" alt="CrewAI AOP RBAC 개요" />
</Frame>
@@ -93,7 +93,7 @@ Owner는 항상 접근 가능하며, Private 모드에서는 화이트리스트
</Tip>
<Frame>
<img src="/images/enterprise/visibility.png" alt="CrewAI AMP 가시성 설정" />
<img src="/images/enterprise/visibility.png" alt="CrewAI AOP 가시성 설정" />
</Frame>

4
docs/ko/enterprise/features/tools-and-integrations.mdx
View File

@@ -179,10 +179,10 @@ mode: "wide"
## 내부 도구
로컬에서 도구를 만들고, CrewAI AMP 도구 저장소에 게시한 후, 에이전트에서 사용하세요.
로컬에서 도구를 만들고, CrewAI AOP 도구 저장소에 게시한 후, 에이전트에서 사용하세요.
<Tip>
아래 명령을 실행하기 전에 CrewAI AMP 계정에 로그인하세요:
아래 명령을 실행하기 전에 CrewAI AOP 계정에 로그인하세요:
```bash
crewai login
```

4
docs/ko/enterprise/features/traces.mdx
View File

@@ -11,7 +11,7 @@ Trace는 crew 실행에 대한 포괄적인 가시성을 제공하여 성능 모
## Traces란 무엇인가요?
CrewAI AMP의 Traces는 crew의 작동 과정을 처음 입력에서 최종 출력까지 모든 측면에서 포착하는 상세 실행 기록입니다. Traces에는 다음 내용이 기록됩니다:
CrewAI AOP의 Traces는 crew의 작동 과정을 처음 입력에서 최종 출력까지 모든 측면에서 포착하는 상세 실행 기록입니다. Traces에는 다음 내용이 기록됩니다:
- Agent의 생각 및 추론
- 작업 실행 세부 정보
@@ -28,7 +28,7 @@ CrewAI AMP의 Traces는 crew의 작동 과정을 처음 입력에서 최종 출
<Steps>
<Step title="트레이스 탭으로 이동">
CrewAI AMP 대시보드에 들어가면, **트레이스**를 클릭하여 모든 실행 기록을 볼 수 있습니다.
CrewAI AOP 대시보드에 들어가면, **트레이스**를 클릭하여 모든 실행 기록을 볼 수 있습니다.
</Step>
<Step title="실행 선택하기">

2
docs/ko/enterprise/features/webhook-streaming.mdx
View File

@@ -7,7 +7,7 @@ mode: "wide"
## 개요
Enterprise Event Streaming을 사용하면 CrewAI AMP에 배포된 crew 및 flow에 대한 실시간 웹훅 업데이트(예: 모델 호출, 도구 사용, flow 단계)를 받을 수 있습니다.
Enterprise Event Streaming을 사용하면 CrewAI AOP에 배포된 crew 및 flow에 대한 실시간 웹훅 업데이트(예: 모델 호출, 도구 사용, flow 단계)를 받을 수 있습니다.
## 사용법

4
docs/ko/enterprise/guides/automation-triggers.mdx
View File

@@ -1,11 +1,11 @@
---
title: "트리거 개요"
description: "CrewAI AMP 트리거의 동작 방식과 관리 방법, 그리고 통합별 플레이북을 한눈에 확인하세요"
description: "CrewAI AOP 트리거의 동작 방식과 관리 방법, 그리고 통합별 플레이북을 한눈에 확인하세요"
icon: "bolt"
mode: "wide"
---
CrewAI AMP 트리거는 팀이 이미 사용하고 있는 도구의 실시간 이벤트와 자동화를 연결합니다. 폴링이나 수동 실행 대신, 트리거는 새로운 이메일, 캘린더 업데이트, CRM 상태 변화 등을 감지하여 지정한 크루 또는 플로우를 즉시 실행합니다.
CrewAI AOP 트리거는 팀이 이미 사용하고 있는 도구의 실시간 이벤트와 자동화를 연결합니다. 폴링이나 수동 실행 대신, 트리거는 새로운 이메일, 캘린더 업데이트, CRM 상태 변화 등을 감지하여 지정한 크루 또는 플로우를 즉시 실행합니다.
<iframe
className="w-full aspect-video rounded-xl"

6
docs/ko/enterprise/guides/azure-openai-setup.mdx
View File

@@ -19,8 +19,8 @@ mode: "wide"
</Frame>
</Step>
<Step title="CrewAI AMP 연결 구성">
4. 다른 탭에서 `CrewAI AMP > LLM Connections`를 엽니다. LLM Connection에 이름을 지정하고, 공급자로 Azure를 선택한 다음, Azure에서 선택한 것과 동일한 모델을 선택하세요.
<Step title="CrewAI AOP 연결 구성">
4. 다른 탭에서 `CrewAI AOP > LLM Connections`를 엽니다. LLM Connection에 이름을 지정하고, 공급자로 Azure를 선택한 다음, Azure에서 선택한 것과 동일한 모델을 선택하세요.
5. 같은 페이지에서 3단계에서 가져온 환경 변수를 추가하세요:
- 하나는 `AZURE_DEPLOYMENT_TARGET_URL` (Target URI 사용)로 명명합니다. URL은 다음과 같이 표시됩니다: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
- 다른 하나는 `AZURE_API_KEY` (Key 사용)로 명명합니다.
@@ -28,7 +28,7 @@ mode: "wide"
</Step>
<Step title="기본 구성 설정">
7. `CrewAI AMP > Settings > Defaults > Crew Studio LLM Settings`에서 새 LLM Connection과 모델을 기본값으로 설정합니다.
7. `CrewAI AOP > Settings > Defaults > Crew Studio LLM Settings`에서 새 LLM Connection과 모델을 기본값으로 설정합니다.
</Step>
<Step title="네트워크 액세스 구성">

2
docs/ko/enterprise/guides/build-crew.mdx
View File

@@ -7,7 +7,7 @@ mode: "wide"
## 개요
[CrewAI AMP](https://app.crewai.com)는 프로덕션 환경에서 AI 에이전트를 **생성**, **배포** 및 **관리**하는 과정을 간소화합니다.
[CrewAI AOP](https://app.crewai.com)는 프로덕션 환경에서 AI 에이전트를 **생성**, **배포** 및 **관리**하는 과정을 간소화합니다.
## 시작하기

14
docs/ko/enterprise/guides/deploy-crew.mdx
View File

@@ -6,7 +6,7 @@ mode: "wide"
---
<Note>
로컬에서 또는 Crew Studio를 통해 crew를 생성한 후, 다음 단계는 이를 CrewAI AMP 플랫폼에 배포하는 것입니다. 본 가이드에서는 다양한 배포 방법을 다루며, 여러분의 워크플로우에 가장 적합한 방식을 선택할 수 있도록 안내합니다.
로컬에서 또는 Crew Studio를 통해 crew를 생성한 후, 다음 단계는 이를 CrewAI AOP 플랫폼에 배포하는 것입니다. 본 가이드에서는 다양한 배포 방법을 다루며, 여러분의 워크플로우에 가장 적합한 방식을 선택할 수 있도록 안내합니다.
</Note>
## 사전 준비 사항
@@ -39,10 +39,10 @@ CLI는 로컬에서 개발된 crew를 Enterprise 플랫폼에 가장 빠르게
</Step>
<Step title="Enterprise 플랫폼에 인증">
먼저, CrewAI AMP 플랫폼에 CLI를 인증해야 합니다:
먼저, CrewAI AOP 플랫폼에 CLI를 인증해야 합니다:
```bash
# 이미 CrewAI AMP 계정이 있거나 새로 생성하고 싶을 때:
# 이미 CrewAI AOP 계정이 있거나 새로 생성하고 싶을 때:
crewai login
```
@@ -124,7 +124,7 @@ crewai deploy remove <deployment_id>
## 옵션 2: 웹 인터페이스를 통한 직접 배포
GitHub 계정을 연결하여 CrewAI AMP 웹 인터페이스를 통해 crews를 직접 배포할 수도 있습니다. 이 방법은 로컬 머신에서 CLI를 사용할 필요가 없습니다.
GitHub 계정을 연결하여 CrewAI AOP 웹 인터페이스를 통해 crews를 직접 배포할 수도 있습니다. 이 방법은 로컬 머신에서 CLI를 사용할 필요가 없습니다.
<Steps>
@@ -134,9 +134,9 @@ GitHub 계정을 연결하여 CrewAI AMP 웹 인터페이스를 통해 crews를
</Step>
<Step title="GitHub를 CrewAI AMP에 연결하기">
<Step title="GitHub를 CrewAI AOP에 연결하기">
1. [CrewAI AMP](https://app.crewai.com)에 로그인합니다.
1. [CrewAI AOP](https://app.crewai.com)에 로그인합니다.
2. "Connect GitHub" 버튼을 클릭합니다.
<Frame>
@@ -190,7 +190,7 @@ GitHub 계정을 연결하여 CrewAI AMP 웹 인터페이스를 통해 crews를
## ⚠️ 환경 변수 보안 요구사항
<Warning>
**중요**: CrewAI AMP는 환경 변수 이름에 대한 보안 제한이 있으며, 이를 따르지 않을 경우 배포가 실패할 수 있습니다.
**중요**: CrewAI AOP는 환경 변수 이름에 대한 보안 제한이 있으며, 이를 따르지 않을 경우 배포가 실패할 수 있습니다.
</Warning>
### 차단된 환경 변수 패턴

Some files were not shown because too many files have changed in this diff Show More