mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-13 18:18:29 +00:00
chore: merge main into release/v1.0.0
This commit is contained in:
Greyson LaLonde
@@ -15,15 +15,15 @@ Welcome to the CrewAI AMP API reference. This API allows you to programmatically
|
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<Step title="Get Your API Credentials">
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Navigate to your crew's detail page in the CrewAI AMP dashboard and copy your Bearer Token from the Status tab.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Discover Required Inputs">
|
||||
Use the `GET /inputs` endpoint to see what parameters your crew expects.
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</Step>
|
||||
|
||||
|
||||
<Step title="Start a Crew Execution">
|
||||
Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
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</Step>
|
||||
|
||||
|
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<Step title="Monitor Progress">
|
||||
Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
|
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</Step>
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@@ -62,7 +62,7 @@ Replace `your-crew-name` with your actual crew's URL from the dashboard.
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## Typical Workflow
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|
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1. **Discovery**: Call `GET /inputs` to understand what your crew needs
|
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2. **Execution**: Submit inputs via `POST /kickoff` to start processing
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2. **Execution**: Submit inputs via `POST /kickoff` to start processing
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3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
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4. **Results**: Extract the final output from the completed response
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@@ -87,7 +87,7 @@ The API uses standard HTTP status codes:
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||||
|
||||
Each endpoint page shows you:
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- ✅ **Exact request format** with all parameters
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- ✅ **Response examples** for success and error cases
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- ✅ **Response examples** for success and error cases
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||||
- ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
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- ✅ **Authentication examples** with proper Bearer token format
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@@ -104,7 +104,7 @@ Each endpoint page shows you:
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||||
|
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**Example workflow:**
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1. **Copy this cURL example** from any endpoint page
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2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL
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2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL
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||||
3. **Replace the Bearer token** with your real token from the dashboard
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4. **Run the request** in your terminal or API client
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File diff suppressed because it is too large
Load Diff
@@ -208,7 +208,7 @@ from crewai.tools import BaseTool
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class AsyncCustomTool(BaseTool):
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name: str = "async_custom_tool"
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description: str = "An asynchronous custom tool"
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||||
|
||||
|
||||
async def _run(self, query: str = "") -> str:
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||||
"""Asynchronously run the tool"""
|
||||
# Your async implementation here
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|
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@@ -102,5 +102,3 @@ Once deployed, you can view the automation details and have the **Options** drop
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Stream real-time events and updates to your systems.
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||||
</Card>
|
||||
</CardGroup>
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||||
|
||||
|
||||
|
||||
@@ -86,5 +86,3 @@ Once published, you can view the automation details and have the **Options** dro
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Export a React Component.
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||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
|
||||
|
||||
@@ -28,7 +28,7 @@ The Marketplace provides a curated surface for discovering integrations, interna
|
||||
|
||||
</Frame>
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||||
|
||||
You can also download the templates directly from the marketplace by clicking on the `Download` button so
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You can also download the templates directly from the marketplace by clicking on the `Download` button so
|
||||
you can use them locally or refine them to your needs.
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|
||||
## Related
|
||||
@@ -44,5 +44,3 @@ you can use them locally or refine them to your needs.
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||||
Store, share, and reuse agent definitions across teams and projects.
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||||
</Card>
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||||
</CardGroup>
|
||||
|
||||
|
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|
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@@ -11,12 +11,12 @@ RBAC in CrewAI AMP enables secure, scalable access management through a combinat
|
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|
||||
<Frame>
|
||||
<img src="/images/enterprise/users_and_roles.png" alt="RBAC overview in CrewAI AMP" />
|
||||
|
||||
|
||||
</Frame>
|
||||
|
||||
## Users and Roles
|
||||
|
||||
Each member in your CrewAI workspace is assigned a role, which determines their access across various features.
|
||||
Each member in your CrewAI workspace is assigned a role, which determines their access across various features.
|
||||
|
||||
You can:
|
||||
|
||||
@@ -94,11 +94,9 @@ The organization owner always has access. In private mode, only whitelisted user
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/visibility.png" alt="Automation Visibility settings in CrewAI AMP" />
|
||||
|
||||
|
||||
</Frame>
|
||||
|
||||
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
|
||||
Contact our support team for assistance with RBAC questions.
|
||||
</Card>
|
||||
|
||||
|
||||
|
||||
@@ -188,10 +188,10 @@ 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 AMP 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 AMP account by running this command:
|
||||
```bash
|
||||
crewai login
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||||
```
|
||||
@@ -247,4 +247,3 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma
|
||||
Automate workflows and integrate with external platforms and services.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
|
||||
@@ -30,7 +30,7 @@ Traces in CrewAI AMP are detailed execution records that capture every aspect of
|
||||
<Step title="Navigate to the Traces Tab">
|
||||
Once in your CrewAI AMP dashboard, click on the **Traces** to view all execution records.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Select an Execution">
|
||||
You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.
|
||||
</Step>
|
||||
@@ -112,7 +112,7 @@ Traces are invaluable for troubleshooting issues with your crews:
|
||||
<Steps>
|
||||
<Step title="Identify Failure Points">
|
||||
When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for:
|
||||
|
||||
|
||||
- Failed tasks
|
||||
- Unexpected agent decisions
|
||||
- Tool usage errors
|
||||
@@ -122,19 +122,19 @@ Traces are invaluable for troubleshooting issues with your crews:
|
||||
|
||||
</Frame>
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Optimize Performance">
|
||||
Use execution metrics to identify performance bottlenecks:
|
||||
|
||||
|
||||
- Tasks that took longer than expected
|
||||
- Excessive token usage
|
||||
- Redundant tool operations
|
||||
- Unnecessary API calls
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Improve Cost Efficiency">
|
||||
Analyze token usage and cost estimates to optimize your crew's efficiency:
|
||||
|
||||
|
||||
- Consider using smaller models for simpler tasks
|
||||
- Refine prompts to be more concise
|
||||
- Cache frequently accessed information
|
||||
@@ -154,4 +154,4 @@ This yields more stable tracing under load while preserving detailed task/agent
|
||||
|
||||
<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.
|
||||
</Card>
|
||||
</Card>
|
||||
|
||||
@@ -7,7 +7,7 @@ mode: "wide"
|
||||
|
||||
## Overview
|
||||
|
||||
Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to
|
||||
Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to
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||||
CrewAI AMP, such as model calls, tool usage, and flow steps.
|
||||
|
||||
## Usage
|
||||
@@ -151,7 +151,7 @@ CrewAI supports both system events and custom events in Enterprise Event Streami
|
||||
### Reasoning Events:
|
||||
|
||||
- `agent_reasoning_started`
|
||||
- `agent_reasoning_completed`
|
||||
- `agent_reasoning_completed`
|
||||
- `agent_reasoning_failed`
|
||||
|
||||
Event names match the internal event bus. See GitHub for the full list of events.
|
||||
@@ -165,4 +165,4 @@ You can emit your own custom events, and they will be delivered through the webh
|
||||
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
|
||||
Contact our support team for assistance with webhook integration or troubleshooting.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
</CardGroup>
|
||||
|
||||
@@ -252,4 +252,4 @@ Automation triggers transform your CrewAI deployments into responsive, event-dri
|
||||
|
||||
<Card title="CrewAI AMP Trigger Examples" href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples" icon="github">
|
||||
Check them out on GitHub!
|
||||
</Card>
|
||||
</Card>
|
||||
|
||||
@@ -49,4 +49,4 @@ If you encounter issues:
|
||||
- Verify the Target URI format matches the expected pattern
|
||||
- Check that the API key is correct and has proper permissions
|
||||
- Ensure network access is configured to allow CrewAI connections
|
||||
- Confirm the deployment model matches what you've configured in CrewAI
|
||||
- Confirm the deployment model matches what you've configured in CrewAI
|
||||
|
||||
@@ -6,12 +6,12 @@ mode: "wide"
|
||||
---
|
||||
|
||||
<Tip>
|
||||
Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface.
|
||||
Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface.
|
||||
</Tip>
|
||||
|
||||
## What is Crew Studio?
|
||||
|
||||
Crew Studio is an innovative way to create AI agent crews without writing code.
|
||||
Crew Studio is an innovative way to create AI agent crews without writing code.
|
||||
|
||||
<Frame>
|
||||
|
||||
@@ -37,9 +37,9 @@ Before you can start using Crew Studio, you need to configure your LLM connectio
|
||||
<Note>
|
||||
Feel free to use any LLM provider you want that is supported by CrewAI.
|
||||
</Note>
|
||||
|
||||
|
||||
Configure your LLM connection:
|
||||
|
||||
|
||||
- Enter a `Connection Name` (e.g., `OpenAI`)
|
||||
- Select your model provider: `openai` or `azure`
|
||||
- Select models you'd like to use in your Studio-generated Crews
|
||||
@@ -48,28 +48,28 @@ Before you can start using Crew Studio, you need to configure your LLM connectio
|
||||
- For OpenAI: Add `OPENAI_API_KEY` with your API key
|
||||
- For Azure OpenAI: Refer to [this article](https://blog.crewai.com/configuring-azure-openai-with-crewai-a-comprehensive-guide/) for configuration details
|
||||
- Click `Add Connection` to save your configuration
|
||||
|
||||
|
||||
<Frame>
|
||||
|
||||
</Frame>
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Verify Connection Added">
|
||||
Once you complete the setup, you'll see your new connection added to the list of available connections.
|
||||
|
||||
|
||||
<Frame>
|
||||
|
||||
</Frame>
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Configure LLM Defaults">
|
||||
In the main menu, go to **Settings → Defaults** and configure the LLM Defaults settings:
|
||||
|
||||
|
||||
- Select default models for agents and other components
|
||||
- Set default configurations for Crew Studio
|
||||
|
||||
|
||||
Click `Save Settings` to apply your changes.
|
||||
|
||||
|
||||
<Frame>
|
||||
|
||||
</Frame>
|
||||
@@ -84,36 +84,36 @@ Now that you've configured your LLM connection and default settings, you're read
|
||||
<Step title="Access Studio">
|
||||
Navigate to the **Studio** section in your CrewAI AMP dashboard.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Start a Conversation">
|
||||
Start a conversation with the Crew Assistant by describing the problem you want to solve:
|
||||
|
||||
|
||||
```md
|
||||
I need a crew that can research the latest AI developments and create a summary report.
|
||||
```
|
||||
|
||||
|
||||
The Crew Assistant will ask clarifying questions to better understand your requirements.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Review Generated Crew">
|
||||
Review the generated crew configuration, including:
|
||||
|
||||
|
||||
- Agents and their roles
|
||||
- Tasks to be performed
|
||||
- Required inputs
|
||||
- Tools to be used
|
||||
|
||||
|
||||
This is your opportunity to refine the configuration before proceeding.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Deploy or Download">
|
||||
Once you're satisfied with the configuration, you can:
|
||||
|
||||
|
||||
- Download the generated code for local customization
|
||||
- Deploy the crew directly to the CrewAI AMP platform
|
||||
- Modify the configuration and regenerate the crew
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Test Your Crew">
|
||||
After deployment, test your crew with sample inputs to ensure it performs as expected.
|
||||
</Step>
|
||||
@@ -130,32 +130,32 @@ Here's a typical workflow for creating a crew with Crew Studio:
|
||||
<Steps>
|
||||
<Step title="Describe Your Problem">
|
||||
Start by describing your problem:
|
||||
|
||||
|
||||
```md
|
||||
I need a crew that can analyze financial news and provide investment recommendations
|
||||
```
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Answer Questions">
|
||||
Respond to clarifying questions from the Crew Assistant to refine your requirements.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Review the Plan">
|
||||
Review the generated crew plan, which might include:
|
||||
|
||||
|
||||
- A Research Agent to gather financial news
|
||||
- An Analysis Agent to interpret the data
|
||||
- A Recommendations Agent to provide investment advice
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Approve or Modify">
|
||||
Approve the plan or request changes if necessary.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Download or Deploy">
|
||||
Download the code for customization or deploy directly to the platform.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Test and Refine">
|
||||
Test your crew with sample inputs and refine as needed.
|
||||
</Step>
|
||||
@@ -164,4 +164,3 @@ Here's a typical workflow for creating a crew with Crew Studio:
|
||||
<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.
|
||||
</Card>
|
||||
|
||||
|
||||
@@ -61,4 +61,4 @@ You can jump-start development with the [HubSpot examples in the trigger reposit
|
||||
|
||||
Each crew demonstrates how to parse HubSpot record fields, enrich context, and return structured insights.
|
||||
|
||||
For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows).
|
||||
For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows).
|
||||
|
||||
@@ -184,4 +184,3 @@ If an execution fails:
|
||||
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
|
||||
Contact our support team for assistance with execution issues or questions about the Enterprise platform.
|
||||
</Card>
|
||||
|
||||
|
||||
@@ -38,7 +38,7 @@ To run this React component locally, you'll need to set up a React development e
|
||||
npx create-react-app my-crew-app
|
||||
```
|
||||
- Change into the project directory:
|
||||
|
||||
|
||||
```bash
|
||||
cd my-crew-app
|
||||
```
|
||||
@@ -77,7 +77,7 @@ To run this React component locally, you'll need to set up a React development e
|
||||
|
||||
<Step title="Start the development server">
|
||||
- In your project directory, run:
|
||||
|
||||
|
||||
```bash
|
||||
npm start
|
||||
```
|
||||
@@ -101,4 +101,4 @@ You can then customise the `CrewLead.jsx` to add color, title etc
|
||||
- Customize the component styling to match your application's design
|
||||
- Add additional props for configuration
|
||||
- Integrate with your application's state management
|
||||
- Add error handling and loading states
|
||||
- Add error handling and loading states
|
||||
|
||||
@@ -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 AMP support who can provide tailored guidance for your specific Salesforce environment and business needs.
|
||||
|
||||
@@ -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 AMP organization.
|
||||
|
||||
@@ -142,7 +142,7 @@ Deletion is permanent. Deleted tools cannot be restored or re-installed.
|
||||
|
||||
## Security Checks
|
||||
|
||||
Every published version undergoes automated security checks, and are only available to install after they pass.
|
||||
Every published version undergoes automated security checks, and are only available to install after they pass.
|
||||
|
||||
You can check the security check status of a tool at:
|
||||
|
||||
|
||||
@@ -6,7 +6,7 @@ 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 AMP, you may need to make updates to the code, security settings, or configuration.
|
||||
This guide explains how to perform these common update operations.
|
||||
</Note>
|
||||
|
||||
@@ -87,4 +87,3 @@ If you encounter any issues after updating, you can view deployment logs in the
|
||||
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
|
||||
Contact our support team for assistance with updating your crew or troubleshooting deployment issues.
|
||||
</Card>
|
||||
|
||||
|
||||
@@ -59,7 +59,7 @@ CrewAI AMP allows you to automate your workflow using webhooks. This article wil
|
||||
</Step>
|
||||
|
||||
<Step title="Setting Up the Webhook">
|
||||
1. Create a new flow in ActivePieces and name it
|
||||
1. Create a new flow in ActivePieces and name it
|
||||
<Frame>
|
||||
<img src="/images/enterprise/activepieces-flow.png" alt="ActivePieces Flow" />
|
||||
</Frame>
|
||||
@@ -152,4 +152,4 @@ CrewAI AMP allows you to automate your workflow using webhooks. This article wil
|
||||
}
|
||||
```
|
||||
</Tab>
|
||||
</Tabs>
|
||||
</Tabs>
|
||||
|
||||
@@ -101,4 +101,4 @@ This guide will walk you through the process of setting up Zapier triggers for C
|
||||
- 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 AMP, allowing for automated workflows triggered by Slack messages and resulting in email notifications with CrewAI AMP output.
|
||||
|
||||
@@ -47,4 +47,3 @@ mode: "wide"
|
||||
<Tip>
|
||||
Use Cookbooks to learn a pattern quickly, then jump to Full Examples for production‑grade implementations.
|
||||
</Tip>
|
||||
|
||||
|
||||
@@ -7,12 +7,12 @@ mode: "wide"
|
||||
|
||||
## Human input in agent execution
|
||||
|
||||
Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary.
|
||||
Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary.
|
||||
This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
|
||||
|
||||
## Using human input with CrewAI
|
||||
|
||||
To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer.
|
||||
To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer.
|
||||
This input can provide extra context, clarify ambiguities, or validate the agent's output.
|
||||
|
||||
### Example:
|
||||
@@ -96,4 +96,4 @@ result = crew.kickoff()
|
||||
|
||||
print("######################")
|
||||
print(result)
|
||||
```
|
||||
```
|
||||
|
||||
@@ -44,7 +44,7 @@ The most critical step in LLM selection is understanding what your task actually
|
||||
|
||||
- **Creative Tasks** demand a different type of cognitive capability focused on generating novel, engaging, and contextually appropriate content. This includes storytelling, marketing copy creation, and creative problem-solving. The model needs to understand nuance, tone, and audience while producing content that feels authentic and engaging rather than formulaic.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Output Requirements">
|
||||
- **Structured Data** tasks require precision and consistency in format adherence. When working with JSON, XML, or database formats, the model must reliably produce syntactically correct output that can be programmatically processed. These tasks often have strict validation requirements and little tolerance for format errors, making reliability more important than creativity.
|
||||
|
||||
@@ -52,7 +52,7 @@ The most critical step in LLM selection is understanding what your task actually
|
||||
|
||||
- **Technical Content** sits between structured data and creative content, requiring both precision and clarity. Documentation, code generation, and technical analysis need to be accurate and comprehensive while remaining accessible to the intended audience. The model must understand complex technical concepts and communicate them effectively.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Context Needs">
|
||||
- **Short Context** scenarios involve focused, immediate tasks where the model needs to process limited information quickly. These are often transactional interactions where speed and efficiency matter more than deep understanding. The model doesn't need to maintain extensive conversation history or process large documents.
|
||||
|
||||
@@ -74,7 +74,7 @@ Understanding model capabilities requires looking beyond marketing claims and be
|
||||
|
||||
However, reasoning models often come with trade-offs in terms of speed and cost. They may also be less suitable for creative tasks or simple operations where their sophisticated reasoning capabilities aren't needed. Consider these models when your tasks involve genuine complexity that benefits from systematic, step-by-step analysis.
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="General Purpose Models" icon="microchip">
|
||||
General purpose models offer the most balanced approach to LLM selection, providing solid performance across a wide range of tasks without extreme specialization in any particular area. These models are trained on diverse datasets and optimized for versatility rather than peak performance in specific domains.
|
||||
|
||||
@@ -82,7 +82,7 @@ Understanding model capabilities requires looking beyond marketing claims and be
|
||||
|
||||
While general purpose models may not achieve the peak performance of specialized alternatives in specific domains, they offer operational simplicity and reduced complexity in model management. They're often the best starting point for new projects, allowing teams to understand their specific needs before potentially optimizing with more specialized models.
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Fast & Efficient Models" icon="bolt">
|
||||
Fast and efficient models prioritize speed, cost-effectiveness, and resource efficiency over sophisticated reasoning capabilities. These models are optimized for high-throughput scenarios where quick responses and low operational costs are more important than nuanced understanding or complex reasoning.
|
||||
|
||||
@@ -90,7 +90,7 @@ Understanding model capabilities requires looking beyond marketing claims and be
|
||||
|
||||
The key consideration with efficient models is ensuring that their capabilities align with your task requirements. While they can handle many routine operations effectively, they may struggle with tasks requiring nuanced understanding, complex reasoning, or sophisticated content generation. They're best used for well-defined, routine operations where speed and cost matter more than sophistication.
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Creative Models" icon="pen">
|
||||
Creative models are specifically optimized for content generation, writing quality, and creative thinking tasks. These models typically excel at understanding nuance, tone, and style while producing engaging, contextually appropriate content that feels natural and authentic.
|
||||
|
||||
@@ -98,7 +98,7 @@ Understanding model capabilities requires looking beyond marketing claims and be
|
||||
|
||||
When selecting creative models, consider not just their ability to generate text, but their understanding of audience, context, and purpose. The best creative models can adapt their output to match specific brand voices, target different audience segments, and maintain consistency across extended content pieces.
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Open Source Models" icon="code">
|
||||
Open source models offer unique advantages in terms of cost control, customization potential, data privacy, and deployment flexibility. These models can be run locally or on private infrastructure, providing complete control over data handling and model behavior.
|
||||
|
||||
@@ -151,7 +151,7 @@ content_writer = Agent(
|
||||
)
|
||||
|
||||
data_processor = Agent(
|
||||
role="Data Analysis Specialist",
|
||||
role="Data Analysis Specialist",
|
||||
goal="Extract and organize key data points from research sources",
|
||||
backstory="Detail-oriented analyst focused on accuracy and efficiency",
|
||||
llm=processing_llm, # Fast, cost-effective model for routine tasks
|
||||
@@ -178,7 +178,7 @@ The key to successful multi-model implementation is understanding how different
|
||||
|
||||
Cost considerations are particularly important for manager LLMs since they're involved in every operation. The model needs to provide sufficient capability for effective coordination while remaining cost-effective for frequent use. This often means finding models that offer good reasoning capabilities without the premium pricing of the most sophisticated options.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Function Calling LLM">
|
||||
Function calling LLMs handle tool usage across all agents, making them critical for crews that rely heavily on external tools and APIs. These models need to excel at understanding tool capabilities, extracting parameters accurately, and handling tool responses effectively.
|
||||
|
||||
@@ -186,7 +186,7 @@ The key to successful multi-model implementation is understanding how different
|
||||
|
||||
Many teams find that specialized function calling models or general purpose models with strong tool support work better than creative or reasoning-focused models for this role. The key is ensuring that the model can reliably bridge the gap between natural language instructions and structured tool calls.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Agent-Specific Overrides">
|
||||
Individual agents can override crew-level LLM settings when their specific needs differ significantly from the general crew requirements. This capability allows for fine-tuned optimization while maintaining operational simplicity for most agents.
|
||||
|
||||
@@ -210,7 +210,7 @@ Effective task definition is often more important than model selection in determ
|
||||
|
||||
Common mistakes include being too vague about objectives, failing to provide necessary context, setting unclear success criteria, or combining multiple unrelated tasks into a single description. The goal is to provide enough information for the agent to succeed while maintaining focus on a single, clear objective.
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Expected Output Guidelines" icon="bullseye">
|
||||
Expected output guidelines serve as a contract between the task definition and the agent, clearly specifying what the deliverable should look like and how it will be evaluated. These guidelines should describe both the format and structure needed, as well as the key elements that must be included for the output to be considered complete.
|
||||
|
||||
@@ -230,7 +230,7 @@ Effective task definition is often more important than model selection in determ
|
||||
|
||||
Sequential dependencies work best when there's a clear logical progression from one task to another and when the output of one task genuinely improves the quality or feasibility of subsequent tasks. However, they can create bottlenecks if not managed carefully, so it's important to identify which dependencies are truly necessary versus those that are merely convenient.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Parallel Execution">
|
||||
Parallel execution becomes valuable when tasks are independent of each other, time efficiency is important, or different expertise areas are involved that don't require coordination. This approach can significantly reduce overall execution time while allowing specialized agents to work on their areas of strength simultaneously.
|
||||
|
||||
@@ -286,10 +286,10 @@ domain_expert = Agent(
|
||||
role="B2B SaaS Marketing Strategist",
|
||||
goal="Develop comprehensive go-to-market strategies for enterprise software",
|
||||
backstory="""
|
||||
You have 10+ years of experience scaling B2B SaaS companies from Series A to IPO.
|
||||
You understand the nuances of enterprise sales cycles, the importance of product-market
|
||||
fit in different verticals, and how to balance growth metrics with unit economics.
|
||||
You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving
|
||||
You have 10+ years of experience scaling B2B SaaS companies from Series A to IPO.
|
||||
You understand the nuances of enterprise sales cycles, the importance of product-market
|
||||
fit in different verticals, and how to balance growth metrics with unit economics.
|
||||
You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving
|
||||
you perspective on both established and disruptive go-to-market strategies.
|
||||
""",
|
||||
llm=LLM(model="claude-3-5-sonnet", temperature=0.3) # Balanced creativity with domain knowledge
|
||||
@@ -317,9 +317,9 @@ tech_writer = Agent(
|
||||
role="API Documentation Specialist", # Specific role for clear LLM requirements
|
||||
goal="Create comprehensive, developer-friendly API documentation",
|
||||
backstory="""
|
||||
You're a technical writer with 8+ years documenting REST APIs, GraphQL endpoints,
|
||||
and SDK integration guides. You've worked with developer tools companies and
|
||||
understand what developers need: clear examples, comprehensive error handling,
|
||||
You're a technical writer with 8+ years documenting REST APIs, GraphQL endpoints,
|
||||
and SDK integration guides. You've worked with developer tools companies and
|
||||
understand what developers need: clear examples, comprehensive error handling,
|
||||
and practical use cases. You prioritize accuracy and usability over marketing fluff.
|
||||
""",
|
||||
llm=LLM(
|
||||
@@ -327,13 +327,13 @@ tech_writer = Agent(
|
||||
temperature=0.1 # Low temperature for accuracy
|
||||
),
|
||||
tools=[code_analyzer_tool, api_scanner_tool],
|
||||
verbose=True
|
||||
verbose=True
|
||||
)
|
||||
```
|
||||
|
||||
**Alignment Checklist:**
|
||||
- ✅ **Role Specificity**: Clear domain and responsibilities
|
||||
- ✅ **LLM Match**: Model strengths align with role requirements
|
||||
- ✅ **LLM Match**: Model strengths align with role requirements
|
||||
- ✅ **Backstory Depth**: Provides domain context the LLM can leverage
|
||||
- ✅ **Tool Integration**: Tools support the agent's specialized function
|
||||
- ✅ **Parameter Tuning**: Temperature and settings optimize for role needs
|
||||
@@ -351,26 +351,26 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
- Which agents handle the most complex reasoning tasks?
|
||||
- Which agents primarily do data processing or formatting?
|
||||
- Are any agents heavily tool-dependent?
|
||||
|
||||
|
||||
**Action**: Document current agent roles and identify optimization opportunities.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Implement Crew-Level Strategy" icon="users-gear">
|
||||
**Set Your Baseline:**
|
||||
```python
|
||||
# Start with a reliable default for the crew
|
||||
default_crew_llm = LLM(model="gpt-4o-mini") # Cost-effective baseline
|
||||
|
||||
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
memory=True
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
**Action**: Establish your crew's default LLM before optimizing individual agents.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Optimize High-Impact Agents" icon="star">
|
||||
**Identify and Upgrade Key Agents:**
|
||||
```python
|
||||
@@ -380,25 +380,25 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
llm=LLM(model="gemini-2.5-flash-preview-05-20"), # Premium for coordination
|
||||
# ... rest of config
|
||||
)
|
||||
|
||||
# Creative or customer-facing agents
|
||||
|
||||
# Creative or customer-facing agents
|
||||
content_agent = Agent(
|
||||
role="Content Creator",
|
||||
llm=LLM(model="claude-3-5-sonnet"), # Best for writing
|
||||
# ... rest of config
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
**Action**: Upgrade 20% of your agents that handle 80% of the complexity.
|
||||
</Step>
|
||||
|
||||
|
||||
<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
|
||||
- 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
|
||||
|
||||
|
||||
**Action**: Replace guesswork with data-driven validation using the testing platform.
|
||||
</Step>
|
||||
</Steps>
|
||||
@@ -413,7 +413,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
|
||||
However, reasoning models often come with higher costs and slower response times, so they're best reserved for tasks where their sophisticated capabilities provide genuine value rather than being used for simple operations that don't require complex reasoning.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Creative Models">
|
||||
Creative models become valuable when content generation is the primary output and the quality, style, and engagement level of that content directly impact success. These models excel when writing quality and style matter significantly, creative ideation or brainstorming is needed, or brand voice and tone are important considerations.
|
||||
|
||||
@@ -421,7 +421,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
|
||||
Creative models may be less suitable for technical or analytical tasks where precision and factual accuracy are more important than engagement and style. They're best used when the creative and communicative aspects of the output are primary success factors.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Efficient Models">
|
||||
Efficient models are ideal for high-frequency, routine operations where speed and cost optimization are priorities. These models work best when tasks have clear, well-defined parameters and don't require sophisticated reasoning or creative capabilities.
|
||||
|
||||
@@ -429,7 +429,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
|
||||
The key with efficient models is ensuring that their capabilities align with task requirements. They can handle many routine operations effectively but may struggle with tasks requiring nuanced understanding, complex reasoning, or sophisticated content generation.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Open Source Models">
|
||||
Open source models become attractive when budget constraints are significant, data privacy requirements exist, customization needs are important, or local deployment is required for operational or compliance reasons.
|
||||
|
||||
@@ -451,12 +451,12 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
```python
|
||||
# Strategic agent gets premium model
|
||||
manager = Agent(role="Strategy Manager", llm=LLM(model="gpt-4o"))
|
||||
|
||||
# Processing agent gets efficient model
|
||||
|
||||
# Processing agent gets efficient model
|
||||
processor = Agent(role="Data Processor", llm=LLM(model="gpt-4o-mini"))
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Ignoring Crew-Level vs Agent-Level LLM Hierarchy" icon="shuffle">
|
||||
**The Problem**: Not understanding how CrewAI's LLM hierarchy works - crew LLM, manager LLM, and agent LLM settings can conflict or be poorly coordinated.
|
||||
|
||||
@@ -470,12 +470,12 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
manager_llm=LLM(model="gpt-4o"), # For crew coordination
|
||||
process=Process.hierarchical # When using manager_llm
|
||||
)
|
||||
|
||||
|
||||
# Agents inherit crew LLM unless specifically overridden
|
||||
agent1 = Agent(llm=LLM(model="claude-3-5-sonnet")) # Override for specific needs
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Function Calling Model Mismatch" icon="screwdriver-wrench">
|
||||
**The Problem**: Choosing models based on general capabilities while ignoring function calling performance for tool-heavy CrewAI workflows.
|
||||
|
||||
@@ -493,7 +493,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
)
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Premature Optimization Without Testing" icon="gear">
|
||||
**The Problem**: Making complex model selection decisions based on theoretical performance without validating with actual CrewAI workflows and tasks.
|
||||
|
||||
@@ -503,7 +503,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
|
||||
```python
|
||||
# Start with this
|
||||
crew = Crew(agents=[...], tasks=[...], llm=LLM(model="gpt-4o-mini"))
|
||||
|
||||
|
||||
# Test performance, then optimize specific agents as needed
|
||||
# Use Enterprise platform testing to validate improvements
|
||||
```
|
||||
@@ -571,23 +571,23 @@ The Enterprise platform transforms model selection from guesswork into a data-dr
|
||||
<Card title="Task-Driven Selection" icon="bullseye">
|
||||
Choose models based on what the task actually requires, not theoretical capabilities or general reputation.
|
||||
</Card>
|
||||
|
||||
|
||||
<Card title="Capability Matching" icon="puzzle-piece">
|
||||
Align model strengths with agent roles and responsibilities for optimal performance.
|
||||
</Card>
|
||||
|
||||
|
||||
<Card title="Strategic Consistency" icon="link">
|
||||
Maintain coherent model selection strategy across related components and workflows.
|
||||
</Card>
|
||||
|
||||
|
||||
<Card title="Practical Testing" icon="flask">
|
||||
Validate choices through real-world usage rather than benchmarks alone.
|
||||
</Card>
|
||||
|
||||
|
||||
<Card title="Iterative Improvement" icon="arrow-up">
|
||||
Start simple and optimize based on actual performance and needs.
|
||||
</Card>
|
||||
|
||||
|
||||
<Card title="Operational Balance" icon="scale-balanced">
|
||||
Balance performance requirements with cost and complexity constraints.
|
||||
</Card>
|
||||
@@ -614,7 +614,7 @@ These tables/metrics showcase selected leading models in each category and are n
|
||||
<Tabs>
|
||||
<Tab title="Reasoning & Planning">
|
||||
**Best for Manager LLMs and Complex Analysis**
|
||||
|
||||
|
||||
| Model | Intelligence Score | Cost ($/M tokens) | Speed | Best Use in CrewAI |
|
||||
|:------|:------------------|:------------------|:------|:------------------|
|
||||
| **o3** | 70 | $17.50 | Fast | Manager LLM for complex multi-agent coordination |
|
||||
@@ -625,10 +625,10 @@ These tables/metrics showcase selected leading models in each category and are n
|
||||
|
||||
These models excel at multi-step reasoning and are ideal for agents that need to develop strategies, coordinate other agents, or analyze complex information.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Coding & Technical">
|
||||
**Best for Development and Tool-Heavy Workflows**
|
||||
|
||||
|
||||
| Model | Coding Performance | Tool Use Score | Cost ($/M tokens) | Best Use in CrewAI |
|
||||
|:------|:------------------|:---------------|:------------------|:------------------|
|
||||
| **Claude 4 Sonnet** | Excellent | 72.7% | $6.00 | Primary coding agent, technical documentation |
|
||||
@@ -639,10 +639,10 @@ These tables/metrics showcase selected leading models in each category and are n
|
||||
|
||||
These models are optimized for code generation, debugging, and technical problem-solving, making them ideal for development-focused crews.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Speed & Efficiency">
|
||||
**Best for High-Throughput and Real-Time Applications**
|
||||
|
||||
|
||||
| Model | Speed (tokens/s) | Latency (TTFT) | Cost ($/M tokens) | Best Use in CrewAI |
|
||||
|:------|:-----------------|:---------------|:------------------|:------------------|
|
||||
| **Llama 4 Scout** | 2,600 | 0.33s | $0.27 | High-volume processing agents |
|
||||
@@ -653,10 +653,10 @@ These tables/metrics showcase selected leading models in each category and are n
|
||||
|
||||
These models prioritize speed and efficiency, perfect for agents handling routine operations or requiring quick responses. **Pro tip**: Pairing these models with fast inference providers like Groq can achieve even better performance, especially for open-source models like Llama.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Balanced Performance">
|
||||
**Best All-Around Models for General Crews**
|
||||
|
||||
|
||||
| Model | Overall Score | Versatility | Cost ($/M tokens) | Best Use in CrewAI |
|
||||
|:------|:--------------|:------------|:------------------|:------------------|
|
||||
| **GPT-4.1** | 53 | Excellent | $3.50 | General-purpose crew LLM |
|
||||
@@ -677,19 +677,19 @@ These tables/metrics showcase selected leading models in each category and are n
|
||||
|
||||
**Strategy**: Implement a multi-model approach where premium models handle strategic thinking while efficient models handle routine operations.
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Cost-Conscious Crews" icon="dollar-sign">
|
||||
**When budget is a primary constraint**: Focus on models like **DeepSeek R1**, **Llama 4 Scout**, or **Gemini 2.0 Flash**. These provide strong performance at significantly lower costs.
|
||||
|
||||
**Strategy**: Use cost-effective models for most agents, reserving premium models only for the most critical decision-making roles.
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Specialized Workflows" icon="screwdriver-wrench">
|
||||
**For specific domain expertise**: Choose models optimized for your primary use case. **Claude 4** series for coding, **Gemini 2.5 Pro** for research, **Llama 405B** for function calling.
|
||||
|
||||
**Strategy**: Select models based on your crew's primary function, ensuring the core capability aligns with model strengths.
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Enterprise & Privacy" icon="shield">
|
||||
**For data-sensitive operations**: Consider open-source models like **Llama 4** series, **DeepSeek V3**, or **Qwen3** that can be deployed locally while maintaining competitive performance.
|
||||
|
||||
@@ -715,16 +715,16 @@ These tables/metrics showcase selected leading models in each category and are n
|
||||
<Step title="Start with Proven Models">
|
||||
Begin with well-established models like **GPT-4.1**, **Claude 3.7 Sonnet**, or **Gemini 2.0 Flash** that offer good performance across multiple dimensions and have extensive real-world validation.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Identify Specialized Needs">
|
||||
Determine if your crew has specific requirements (coding, reasoning, speed) that would benefit from specialized models like **Claude 4 Sonnet** for development or **o3** for complex analysis. For speed-critical applications, consider fast inference providers like **Groq** alongside model selection.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Implement Multi-Model Strategy">
|
||||
Use different models for different agents based on their roles. High-capability models for managers and complex tasks, efficient models for routine operations.
|
||||
</Step>
|
||||
|
||||
|
||||
<Step title="Monitor and Optimize">
|
||||
Track performance metrics relevant to your use case and be prepared to adjust model selections as new models are released or pricing changes.
|
||||
</Step>
|
||||
</Steps>
|
||||
</Steps>
|
||||
|
||||
@@ -109,7 +109,7 @@ def crew(self) -> Crew:
|
||||
process=Process.sequential,
|
||||
verbose=True
|
||||
)
|
||||
```
|
||||
```
|
||||
|
||||
The `@crew` annotation is used to decorate the method that creates and returns the `Crew` object. This method assembles all the components (agents and tasks) into a functional crew.
|
||||
|
||||
@@ -148,4 +148,4 @@ Note how the `llm` and `tools` in the YAML file correspond to the methods decora
|
||||
- **Flexibility**: Design your crew to be flexible by allowing easy addition or removal of agents and tasks.
|
||||
- **YAML-Code Correspondence**: Ensure that the names and structures in your YAML files correspond correctly to the decorated methods in your Python code.
|
||||
|
||||
By following these guidelines and properly using annotations, you can create well-structured and maintainable crews using the CrewAI framework.
|
||||
By following these guidelines and properly using annotations, you can create well-structured and maintainable crews using the CrewAI framework.
|
||||
|
||||
213
docs/en/observability/tracing.mdx
Normal file
213
docs/en/observability/tracing.mdx
Normal file
@@ -0,0 +1,213 @@
|
||||
---
|
||||
title: CrewAI Tracing
|
||||
description: Built-in tracing for CrewAI Crews and Flows with the CrewAI AMP platform
|
||||
icon: magnifying-glass-chart
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
# CrewAI Built-in Tracing
|
||||
|
||||
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).
|
||||
|
||||
|
||||
|
||||
## Prerequisites
|
||||
|
||||
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)
|
||||
2. **CLI Authentication**: Use the CrewAI CLI to authenticate your local environment
|
||||
|
||||
```bash
|
||||
crewai login
|
||||
```
|
||||
|
||||
## Setup Instructions
|
||||
|
||||
### Step 1: Create Your CrewAI AMP 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.
|
||||
|
||||
### Step 2: Install CrewAI CLI and Authenticate
|
||||
|
||||
If you haven't already, install CrewAI with the CLI tools:
|
||||
|
||||
```bash
|
||||
uv add crewai[tools]
|
||||
```
|
||||
|
||||
Then authenticate your CLI with your CrewAI AMP account:
|
||||
|
||||
```bash
|
||||
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
|
||||
4. Enable tracing capabilities for your local development
|
||||
|
||||
### Step 3: Enable Tracing in Your Crew
|
||||
|
||||
You can enable tracing for your Crew by setting the `tracing` parameter to `True`:
|
||||
|
||||
```python
|
||||
from crewai import Agent, Crew, Process, Task
|
||||
from crewai_tools import SerperDevTool
|
||||
|
||||
# Define your agents
|
||||
researcher = Agent(
|
||||
role="Senior Research Analyst",
|
||||
goal="Uncover cutting-edge developments in AI and data science",
|
||||
backstory="""You work at a leading tech think tank.
|
||||
Your expertise lies in identifying emerging trends.
|
||||
You have a knack for dissecting complex data and presenting actionable insights.""",
|
||||
verbose=True,
|
||||
tools=[SerperDevTool()],
|
||||
)
|
||||
|
||||
writer = Agent(
|
||||
role="Tech Content Strategist",
|
||||
goal="Craft compelling content on tech advancements",
|
||||
backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
|
||||
You transform complex concepts into compelling narratives.""",
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Create tasks for your agents
|
||||
research_task = Task(
|
||||
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
|
||||
Identify key trends, breakthrough technologies, and potential industry impacts.""",
|
||||
expected_output="Full analysis report in bullet points",
|
||||
agent=researcher,
|
||||
)
|
||||
|
||||
writing_task = Task(
|
||||
description="""Using the insights provided, develop an engaging blog
|
||||
post that highlights the most significant AI advancements.
|
||||
Your post should be informative yet accessible, catering to a tech-savvy audience.""",
|
||||
expected_output="Full blog post of at least 4 paragraphs",
|
||||
agent=writer,
|
||||
)
|
||||
|
||||
# Enable tracing in your crew
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
||||
tasks=[research_task, writing_task],
|
||||
process=Process.sequential,
|
||||
tracing=True, # Enable built-in tracing
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Execute your crew
|
||||
result = crew.kickoff()
|
||||
```
|
||||
|
||||
### Step 4: Enable Tracing in Your Flow
|
||||
|
||||
Similarly, you can enable tracing for CrewAI Flows:
|
||||
|
||||
```python
|
||||
from crewai.flow.flow import Flow, listen, start
|
||||
from pydantic import BaseModel
|
||||
|
||||
class ExampleState(BaseModel):
|
||||
counter: int = 0
|
||||
message: str = ""
|
||||
|
||||
class ExampleFlow(Flow[ExampleState]):
|
||||
def __init__(self):
|
||||
super().__init__(tracing=True) # Enable tracing for the flow
|
||||
|
||||
@start()
|
||||
def first_method(self):
|
||||
print("Starting the flow")
|
||||
self.state.counter = 1
|
||||
self.state.message = "Flow started"
|
||||
return "continue"
|
||||
|
||||
@listen("continue")
|
||||
def second_method(self):
|
||||
print("Continuing the flow")
|
||||
self.state.counter += 1
|
||||
self.state.message = "Flow continued"
|
||||
return "finish"
|
||||
|
||||
@listen("finish")
|
||||
def final_method(self):
|
||||
print("Finishing the flow")
|
||||
self.state.counter += 1
|
||||
self.state.message = "Flow completed"
|
||||
|
||||
# Create and run the flow with tracing enabled
|
||||
flow = ExampleFlow(tracing=True)
|
||||
result = flow.kickoff()
|
||||
```
|
||||
|
||||
### Step 5: View Traces in the CrewAI AMP 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.
|
||||
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)
|
||||
|
||||
|
||||
|
||||
### Alternative: Environment Variable Configuration
|
||||
|
||||
You can also enable tracing globally by setting an environment variable:
|
||||
|
||||
```bash
|
||||
export CREWAI_TRACING_ENABLED=true
|
||||
```
|
||||
|
||||
Or add it to your `.env` file:
|
||||
|
||||
```env
|
||||
CREWAI_TRACING_ENABLED=true
|
||||
```
|
||||
|
||||
When this environment variable is set, all Crews and Flows will automatically have tracing enabled, even without explicitly setting `tracing=True`.
|
||||
|
||||
## Viewing Your Traces
|
||||
|
||||
### Access the CrewAI AMP Dashboard
|
||||
|
||||
1. Visit [app.crewai.com](https://app.crewai.com) and log in to your account
|
||||
2. Navigate to your project dashboard
|
||||
3. Click on the **Traces** tab to view execution details
|
||||
|
||||
### What You'll See in Traces
|
||||
|
||||
CrewAI tracing provides comprehensive visibility into:
|
||||
|
||||
- **Agent Decisions**: See how agents reason through tasks and make decisions
|
||||
- **Task Execution Timeline**: Visual representation of task sequences and dependencies
|
||||
- **Tool Usage**: Monitor which tools are called and their results
|
||||
- **LLM Calls**: Track all language model interactions, including prompts and responses
|
||||
- **Performance Metrics**: Execution times, token usage, and costs
|
||||
- **Error Tracking**: Detailed error information and stack traces
|
||||
|
||||
### Trace Features
|
||||
- **Execution Timeline**: Click through different stages of execution
|
||||
- **Detailed Logs**: Access comprehensive logs for debugging
|
||||
- **Performance Analytics**: Analyze execution patterns and optimize performance
|
||||
- **Export Capabilities**: Download traces for further analysis
|
||||
|
||||
### Authentication Issues
|
||||
|
||||
If you encounter authentication problems:
|
||||
|
||||
1. Ensure you're logged in: `crewai login`
|
||||
2. Check your internet connection
|
||||
3. Verify your account at [app.crewai.com](https://app.crewai.com)
|
||||
|
||||
### Traces Not Appearing
|
||||
|
||||
If traces aren't showing up in the dashboard:
|
||||
|
||||
1. Confirm `tracing=True` is set in your Crew/Flow
|
||||
2. Check that `CREWAI_TRACING_ENABLED=true` if using environment variables
|
||||
3. Ensure you're authenticated with `crewai login`
|
||||
4. Verify your crew/flow is actually executing
|
||||
Reference in New Issue
Block a user