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devin/1769
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devin/1769
| Author | SHA1 | Date | |
|---|---|---|---|
|
9af03058fe |
@@ -370,8 +370,7 @@
|
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"pages": [
|
||||
"en/enterprise/features/traces",
|
||||
"en/enterprise/features/webhook-streaming",
|
||||
"en/enterprise/features/hallucination-guardrail",
|
||||
"en/enterprise/features/flow-hitl-management"
|
||||
"en/enterprise/features/hallucination-guardrail"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -824,8 +823,7 @@
|
||||
"pages": [
|
||||
"pt-BR/enterprise/features/traces",
|
||||
"pt-BR/enterprise/features/webhook-streaming",
|
||||
"pt-BR/enterprise/features/hallucination-guardrail",
|
||||
"pt-BR/enterprise/features/flow-hitl-management"
|
||||
"pt-BR/enterprise/features/hallucination-guardrail"
|
||||
]
|
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},
|
||||
{
|
||||
@@ -1289,8 +1287,7 @@
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||||
"pages": [
|
||||
"ko/enterprise/features/traces",
|
||||
"ko/enterprise/features/webhook-streaming",
|
||||
"ko/enterprise/features/hallucination-guardrail",
|
||||
"ko/enterprise/features/flow-hitl-management"
|
||||
"ko/enterprise/features/hallucination-guardrail"
|
||||
]
|
||||
},
|
||||
{
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||||
|
||||
@@ -1,563 +0,0 @@
|
||||
---
|
||||
title: "Flow HITL Management"
|
||||
description: "Enterprise-grade human review for Flows with email-first notifications, routing rules, and auto-response capabilities"
|
||||
icon: "users-gear"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
<Note>
|
||||
Flow HITL Management features require the `@human_feedback` decorator, available in **CrewAI version 1.8.0 or higher**. These features apply specifically to **Flows**, not Crews.
|
||||
</Note>
|
||||
|
||||
CrewAI Enterprise provides a comprehensive Human-in-the-Loop (HITL) management system for Flows that transforms AI workflows into collaborative human-AI processes. The platform uses an **email-first architecture** that enables anyone with an email address to respond to review requests—no platform account required.
|
||||
|
||||
## Overview
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Email-First Design" icon="envelope">
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||||
Responders can reply directly to notification emails to provide feedback
|
||||
</Card>
|
||||
<Card title="Flexible Routing" icon="route">
|
||||
Route requests to specific emails based on method patterns or flow state
|
||||
</Card>
|
||||
<Card title="Auto-Response" icon="clock">
|
||||
Configure automatic fallback responses when no human replies in time
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### Key Benefits
|
||||
|
||||
- **Simple mental model**: Email addresses are universal; no need to manage platform users or roles
|
||||
- **External responders**: Anyone with an email can respond, even non-platform users
|
||||
- **Dynamic assignment**: Pull assignee email directly from flow state (e.g., `sales_rep_email`)
|
||||
- **Reduced configuration**: Fewer settings to configure, faster time to value
|
||||
- **Email as primary channel**: Most users prefer responding via email over logging into a dashboard
|
||||
|
||||
## Setting Up Human Review Points in Flows
|
||||
|
||||
Configure human review checkpoints within your Flows using the `@human_feedback` decorator. When execution reaches a review point, the system pauses, notifies the assignee via email, and waits for a response.
|
||||
|
||||
```python
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
|
||||
class ContentApprovalFlow(Flow):
|
||||
@start()
|
||||
def generate_content(self):
|
||||
# AI generates content
|
||||
return "Generated marketing copy for Q1 campaign..."
|
||||
|
||||
@listen(generate_content)
|
||||
@human_feedback(
|
||||
message="Please review this content for brand compliance:",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
)
|
||||
def review_content(self, content):
|
||||
return content
|
||||
|
||||
@listen("approved")
|
||||
def publish_content(self, result: HumanFeedbackResult):
|
||||
print(f"Publishing approved content. Reviewer notes: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def archive_content(self, result: HumanFeedbackResult):
|
||||
print(f"Content rejected. Reason: {result.feedback}")
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise_content(self, result: HumanFeedbackResult):
|
||||
print(f"Revision requested: {result.feedback}")
|
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```
|
||||
|
||||
For complete implementation details, see the [Human Feedback in Flows](/en/learn/human-feedback-in-flows) guide.
|
||||
|
||||
### Decorator Parameters
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `message` | `str` | The message displayed to the human reviewer |
|
||||
| `emit` | `list[str]` | Valid response options (displayed as buttons in UI) |
|
||||
|
||||
## Platform Configuration
|
||||
|
||||
Access HITL configuration from: **Deployment → Settings → Human in the Loop Configuration**
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-overview.png" alt="HITL Configuration Settings" />
|
||||
</Frame>
|
||||
|
||||
### Email Notifications
|
||||
|
||||
Toggle to enable or disable email notifications for HITL requests.
|
||||
|
||||
| Setting | Default | Description |
|
||||
|---------|---------|-------------|
|
||||
| Email Notifications | Enabled | Send emails when feedback is requested |
|
||||
|
||||
<Note>
|
||||
When disabled, responders must use the dashboard UI or you must configure webhooks for custom notification systems.
|
||||
</Note>
|
||||
|
||||
### SLA Target
|
||||
|
||||
Set a target response time for tracking and metrics purposes.
|
||||
|
||||
| Setting | Description |
|
||||
|---------|-------------|
|
||||
| SLA Target (minutes) | Target response time. Used for dashboard metrics and SLA tracking |
|
||||
|
||||
Leave empty to disable SLA tracking.
|
||||
|
||||
## Email Notifications & Responses
|
||||
|
||||
The HITL system uses an email-first architecture where responders can reply directly to notification emails.
|
||||
|
||||
### How Email Responses Work
|
||||
|
||||
<Steps>
|
||||
<Step title="Notification Sent">
|
||||
When a HITL request is created, an email is sent to the assigned responder with the review content and context.
|
||||
</Step>
|
||||
<Step title="Reply-To Address">
|
||||
The email includes a special reply-to address with a signed token for authentication.
|
||||
</Step>
|
||||
<Step title="User Replies">
|
||||
The responder simply replies to the email with their feedback—no login required.
|
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</Step>
|
||||
<Step title="Token Validation">
|
||||
The platform receives the reply, verifies the signed token, and matches the sender email.
|
||||
</Step>
|
||||
<Step title="Flow Resumes">
|
||||
The feedback is recorded and the flow continues with the human's input.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
### Response Format
|
||||
|
||||
Responders can reply with:
|
||||
|
||||
- **Emit option**: If the reply matches an `emit` option (e.g., "approved"), it's used directly
|
||||
- **Free-form text**: Any text response is passed to the flow as feedback
|
||||
- **Plain text**: The first line of the reply body is used as feedback
|
||||
|
||||
### Confirmation Emails
|
||||
|
||||
After processing a reply, the responder receives a confirmation email indicating whether the feedback was successfully submitted or if an error occurred.
|
||||
|
||||
### Email Token Security
|
||||
|
||||
- Tokens are cryptographically signed for security
|
||||
- Tokens expire after 7 days
|
||||
- Sender email must match the token's authorized email
|
||||
- Confirmation/error emails are sent after processing
|
||||
|
||||
## Routing Rules
|
||||
|
||||
Route HITL requests to specific email addresses based on method patterns.
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-routing-rules.png" alt="HITL Routing Rules Configuration" />
|
||||
</Frame>
|
||||
|
||||
### Rule Structure
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "Approvals to Finance",
|
||||
"match": {
|
||||
"method_name": "approve_*"
|
||||
},
|
||||
"assign_to_email": "finance@company.com",
|
||||
"assign_from_input": "manager_email"
|
||||
}
|
||||
```
|
||||
|
||||
### Matching Patterns
|
||||
|
||||
| Pattern | Description | Example Match |
|
||||
|---------|-------------|---------------|
|
||||
| `approve_*` | Wildcard (any chars) | `approve_payment`, `approve_vendor` |
|
||||
| `review_?` | Single char | `review_a`, `review_1` |
|
||||
| `validate_payment` | Exact match | `validate_payment` only |
|
||||
|
||||
### Assignment Priority
|
||||
|
||||
1. **Dynamic assignment** (`assign_from_input`): If configured, pulls email from flow state
|
||||
2. **Static email** (`assign_to_email`): Falls back to configured email
|
||||
3. **Deployment creator**: If no rule matches, the deployment creator's email is used
|
||||
|
||||
### Dynamic Assignment Example
|
||||
|
||||
If your flow state contains `{"sales_rep_email": "alice@company.com"}`, configure:
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "Route to Sales Rep",
|
||||
"match": {
|
||||
"method_name": "review_*"
|
||||
},
|
||||
"assign_from_input": "sales_rep_email"
|
||||
}
|
||||
```
|
||||
|
||||
The request will be assigned to `alice@company.com` automatically.
|
||||
|
||||
<Tip>
|
||||
**Use Case**: Pull the assignee from your CRM, database, or previous flow step to dynamically route reviews to the right person.
|
||||
</Tip>
|
||||
|
||||
## Auto-Response
|
||||
|
||||
Automatically respond to HITL requests if no human responds within a timeout. This ensures flows don't hang indefinitely.
|
||||
|
||||
### Configuration
|
||||
|
||||
| Setting | Description |
|
||||
|---------|-------------|
|
||||
| Enabled | Toggle to enable auto-response |
|
||||
| Timeout (minutes) | Time to wait before auto-responding |
|
||||
| Default Outcome | The response value (must match an `emit` option) |
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-auto-respond.png" alt="HITL Auto-Response Configuration" />
|
||||
</Frame>
|
||||
|
||||
### Use Cases
|
||||
|
||||
- **SLA compliance**: Ensure flows don't hang indefinitely
|
||||
- **Default approval**: Auto-approve low-risk requests after timeout
|
||||
- **Graceful degradation**: Continue with a safe default when reviewers are unavailable
|
||||
|
||||
<Warning>
|
||||
Use auto-response carefully. Only enable it for non-critical reviews where a default response is acceptable.
|
||||
</Warning>
|
||||
|
||||
## Review Process
|
||||
|
||||
### Dashboard Interface
|
||||
|
||||
The HITL review interface provides a clean, focused experience for reviewers:
|
||||
|
||||
- **Markdown Rendering**: Rich formatting for review content with syntax highlighting
|
||||
- **Context Panel**: View flow state, execution history, and related information
|
||||
- **Feedback Input**: Provide detailed feedback and comments with your decision
|
||||
- **Quick Actions**: One-click emit option buttons with optional comments
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-list-pending-feedbacks.png" alt="HITL Pending Requests List" />
|
||||
</Frame>
|
||||
|
||||
### Response Methods
|
||||
|
||||
Reviewers can respond via three channels:
|
||||
|
||||
| Method | Description |
|
||||
|--------|-------------|
|
||||
| **Email Reply** | Reply directly to the notification email |
|
||||
| **Dashboard** | Use the Enterprise dashboard UI |
|
||||
| **API/Webhook** | Programmatic response via API |
|
||||
|
||||
### History & Audit Trail
|
||||
|
||||
Every HITL interaction is tracked with a complete timeline:
|
||||
|
||||
- Decision history (approve/reject/revise)
|
||||
- Reviewer identity and timestamp
|
||||
- Feedback and comments provided
|
||||
- Response method (email/dashboard/API)
|
||||
- Response time metrics
|
||||
|
||||
## Analytics & Monitoring
|
||||
|
||||
Track HITL performance with comprehensive analytics.
|
||||
|
||||
### Performance Dashboard
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-metrics.png" alt="HITL Metrics Dashboard" />
|
||||
</Frame>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Response Times" icon="stopwatch">
|
||||
Monitor average and median response times by reviewer or flow.
|
||||
</Card>
|
||||
<Card title="Volume Trends" icon="chart-bar">
|
||||
Analyze review volume patterns to optimize team capacity.
|
||||
</Card>
|
||||
<Card title="Decision Distribution" icon="chart-pie">
|
||||
View approval/rejection rates across different review types.
|
||||
</Card>
|
||||
<Card title="SLA Tracking" icon="chart-line">
|
||||
Track percentage of reviews completed within SLA targets.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### Audit & Compliance
|
||||
|
||||
Enterprise-ready audit capabilities for regulatory requirements:
|
||||
|
||||
- Complete decision history with timestamps
|
||||
- Reviewer identity verification
|
||||
- Immutable audit logs
|
||||
- Export capabilities for compliance reporting
|
||||
|
||||
## Common Use Cases
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Security Reviews" icon="shield-halved">
|
||||
**Use Case**: Internal security questionnaire automation with human validation
|
||||
|
||||
- AI generates responses to security questionnaires
|
||||
- Security team reviews and validates accuracy via email
|
||||
- Approved responses are compiled into final submission
|
||||
- Full audit trail for compliance
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Content Approval" icon="file-lines">
|
||||
**Use Case**: Marketing content requiring legal/brand review
|
||||
|
||||
- AI generates marketing copy or social media content
|
||||
- Route to brand team email for voice/tone review
|
||||
- Automatic publishing upon approval
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Financial Approvals" icon="money-bill">
|
||||
**Use Case**: Expense reports, contract terms, budget allocations
|
||||
|
||||
- AI pre-processes and categorizes financial requests
|
||||
- Route based on amount thresholds using dynamic assignment
|
||||
- Maintain complete audit trail for financial compliance
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Dynamic Assignment from CRM" icon="database">
|
||||
**Use Case**: Route reviews to account owners from your CRM
|
||||
|
||||
- Flow fetches account owner email from CRM
|
||||
- Store email in flow state (e.g., `account_owner_email`)
|
||||
- Use `assign_from_input` to route to the right person automatically
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Quality Assurance" icon="magnifying-glass">
|
||||
**Use Case**: AI output validation before customer delivery
|
||||
|
||||
- AI generates customer-facing content or responses
|
||||
- QA team reviews via email notification
|
||||
- Feedback loops improve AI performance over time
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Webhooks API
|
||||
|
||||
When your Flows pause for human feedback, you can configure webhooks to send request data to your own application. This enables:
|
||||
|
||||
- Building custom approval UIs
|
||||
- Integrating with internal tools (Jira, ServiceNow, custom dashboards)
|
||||
- Routing approvals to third-party systems
|
||||
- Mobile app notifications
|
||||
- Automated decision systems
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-webhook.png" alt="HITL Webhook Configuration" />
|
||||
</Frame>
|
||||
|
||||
### Configuring Webhooks
|
||||
|
||||
<Steps>
|
||||
<Step title="Navigate to Settings">
|
||||
Go to your **Deployment** → **Settings** → **Human in the Loop**
|
||||
</Step>
|
||||
<Step title="Expand Webhooks Section">
|
||||
Click to expand the **Webhooks** configuration
|
||||
</Step>
|
||||
<Step title="Add Your Webhook URL">
|
||||
Enter your webhook URL (must be HTTPS in production)
|
||||
</Step>
|
||||
<Step title="Save Configuration">
|
||||
Click **Save Configuration** to activate
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
You can configure multiple webhooks. Each active webhook receives all HITL events.
|
||||
|
||||
### Webhook Events
|
||||
|
||||
Your endpoint will receive HTTP POST requests for these events:
|
||||
|
||||
| Event Type | When Triggered |
|
||||
|------------|----------------|
|
||||
| `new_request` | A flow pauses and requests human feedback |
|
||||
|
||||
### Webhook Payload
|
||||
|
||||
All webhooks receive a JSON payload with this structure:
|
||||
|
||||
```json
|
||||
{
|
||||
"event": "new_request",
|
||||
"request": {
|
||||
"id": "550e8400-e29b-41d4-a716-446655440000",
|
||||
"flow_id": "flow_abc123",
|
||||
"method_name": "review_article",
|
||||
"message": "Please review this article for publication.",
|
||||
"emit_options": ["approved", "rejected", "request_changes"],
|
||||
"state": {
|
||||
"article_id": 12345,
|
||||
"author": "john@example.com",
|
||||
"category": "technology"
|
||||
},
|
||||
"metadata": {},
|
||||
"created_at": "2026-01-14T12:00:00Z"
|
||||
},
|
||||
"deployment": {
|
||||
"id": 456,
|
||||
"name": "Content Review Flow",
|
||||
"organization_id": 789
|
||||
},
|
||||
"callback_url": "https://api.crewai.com/...",
|
||||
"assigned_to_email": "reviewer@company.com"
|
||||
}
|
||||
```
|
||||
|
||||
### Responding to Requests
|
||||
|
||||
To submit feedback, **POST to the `callback_url`** included in the webhook payload.
|
||||
|
||||
```http
|
||||
POST {callback_url}
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"feedback": "Approved. Great article!",
|
||||
"source": "my_custom_app"
|
||||
}
|
||||
```
|
||||
|
||||
### Security
|
||||
|
||||
<Info>
|
||||
All webhook requests are cryptographically signed using HMAC-SHA256 to ensure authenticity and prevent tampering.
|
||||
</Info>
|
||||
|
||||
#### Webhook Security
|
||||
|
||||
- **HMAC-SHA256 signatures**: Every webhook includes a cryptographic signature
|
||||
- **Per-webhook secrets**: Each webhook has its own unique signing secret
|
||||
- **Encrypted at rest**: Signing secrets are encrypted in our database
|
||||
- **Timestamp verification**: Prevents replay attacks
|
||||
|
||||
#### Signature Headers
|
||||
|
||||
Each webhook request includes these headers:
|
||||
|
||||
| Header | Description |
|
||||
|--------|-------------|
|
||||
| `X-Signature` | HMAC-SHA256 signature: `sha256=<hex_digest>` |
|
||||
| `X-Timestamp` | Unix timestamp when the request was signed |
|
||||
|
||||
#### Verification
|
||||
|
||||
Verify by computing:
|
||||
|
||||
```python
|
||||
import hmac
|
||||
import hashlib
|
||||
|
||||
expected = hmac.new(
|
||||
signing_secret.encode(),
|
||||
f"{timestamp}.{payload}".encode(),
|
||||
hashlib.sha256
|
||||
).hexdigest()
|
||||
|
||||
if hmac.compare_digest(expected, signature):
|
||||
# Valid signature
|
||||
```
|
||||
|
||||
### Error Handling
|
||||
|
||||
Your webhook endpoint should return a 2xx status code to acknowledge receipt:
|
||||
|
||||
| Your Response | Our Behavior |
|
||||
|---------------|--------------|
|
||||
| 2xx | Webhook delivered successfully |
|
||||
| 4xx/5xx | Logged as failed, no retry |
|
||||
| Timeout (30s) | Logged as failed, no retry |
|
||||
|
||||
## Security & RBAC
|
||||
|
||||
### Dashboard Access
|
||||
|
||||
HITL access is controlled at the deployment level:
|
||||
|
||||
| Permission | Capability |
|
||||
|------------|------------|
|
||||
| `manage_human_feedback` | Configure HITL settings, view all requests |
|
||||
| `respond_to_human_feedback` | Respond to requests, view assigned requests |
|
||||
|
||||
### Email Response Authorization
|
||||
|
||||
For email replies:
|
||||
1. The reply-to token encodes the authorized email
|
||||
2. Sender email must match the token's email
|
||||
3. Token must not be expired (7-day default)
|
||||
4. Request must still be pending
|
||||
|
||||
### Audit Trail
|
||||
|
||||
All HITL actions are logged:
|
||||
- Request creation
|
||||
- Assignment changes
|
||||
- Response submission (with source: dashboard/email/API)
|
||||
- Flow resume status
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Emails Not Sending
|
||||
|
||||
1. Check "Email Notifications" is enabled in configuration
|
||||
2. Verify routing rules match the method name
|
||||
3. Verify assignee email is valid
|
||||
4. Check deployment creator fallback if no routing rules match
|
||||
|
||||
### Email Replies Not Processing
|
||||
|
||||
1. Check token hasn't expired (7-day default)
|
||||
2. Verify sender email matches assigned email
|
||||
3. Ensure request is still pending (not already responded)
|
||||
|
||||
### Flow Not Resuming
|
||||
|
||||
1. Check request status in dashboard
|
||||
2. Verify callback URL is accessible
|
||||
3. Ensure deployment is still running
|
||||
|
||||
## Best Practices
|
||||
|
||||
<Tip>
|
||||
**Start Simple**: Begin with email notifications to deployment creator, then add routing rules as your workflows mature.
|
||||
</Tip>
|
||||
|
||||
1. **Use Dynamic Assignment**: Pull assignee emails from your flow state for flexible routing.
|
||||
|
||||
2. **Configure Auto-Response**: Set up a fallback for non-critical reviews to prevent flows from hanging.
|
||||
|
||||
3. **Monitor Response Times**: Use analytics to identify bottlenecks and optimize your review process.
|
||||
|
||||
4. **Keep Review Messages Clear**: Write clear, actionable messages in the `@human_feedback` decorator.
|
||||
|
||||
5. **Test Email Flow**: Send test requests to verify email delivery before going to production.
|
||||
|
||||
## Related Resources
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Human Feedback in Flows" icon="code" href="/en/learn/human-feedback-in-flows">
|
||||
Implementation guide for the `@human_feedback` decorator
|
||||
</Card>
|
||||
<Card title="Flow HITL Workflow Guide" icon="route" href="/en/enterprise/guides/human-in-the-loop">
|
||||
Step-by-step guide for setting up HITL workflows
|
||||
</Card>
|
||||
<Card title="RBAC Configuration" icon="shield-check" href="/en/enterprise/features/rbac">
|
||||
Configure role-based access control for your organization
|
||||
</Card>
|
||||
<Card title="Webhook Streaming" icon="bolt" href="/en/enterprise/features/webhook-streaming">
|
||||
Set up real-time event notifications
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -5,54 +5,9 @@ icon: "user-check"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI Enterprise.
|
||||
Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.
|
||||
|
||||
## HITL Approaches in CrewAI
|
||||
|
||||
CrewAI offers two approaches for implementing human-in-the-loop workflows:
|
||||
|
||||
| Approach | Best For | Version |
|
||||
|----------|----------|---------|
|
||||
| **Flow-based** (`@human_feedback` decorator) | Production with Enterprise UI, email-first workflows, full platform features | **1.8.0+** |
|
||||
| **Webhook-based** | Custom integrations, external systems (Slack, Teams, etc.), legacy setups | All versions |
|
||||
|
||||
## Flow-Based HITL with Enterprise Platform
|
||||
|
||||
<Note>
|
||||
The `@human_feedback` decorator requires **CrewAI version 1.8.0 or higher**.
|
||||
</Note>
|
||||
|
||||
When using the `@human_feedback` decorator in your Flows, CrewAI Enterprise provides an **email-first HITL system** that enables anyone with an email address to respond to review requests:
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Email-First Design" icon="envelope">
|
||||
Responders receive email notifications and can reply directly—no login required.
|
||||
</Card>
|
||||
<Card title="Dashboard Review" icon="desktop">
|
||||
Review and respond to HITL requests in the Enterprise dashboard when preferred.
|
||||
</Card>
|
||||
<Card title="Flexible Routing" icon="route">
|
||||
Route requests to specific emails based on method patterns or pull from flow state.
|
||||
</Card>
|
||||
<Card title="Auto-Response" icon="clock">
|
||||
Configure automatic fallback responses when no human replies within the timeout.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### Key Benefits
|
||||
|
||||
- **External responders**: Anyone with an email can respond, even non-platform users
|
||||
- **Dynamic assignment**: Pull assignee email from flow state (e.g., `account_owner_email`)
|
||||
- **Simple configuration**: Email-based routing is easier to set up than user/role management
|
||||
- **Deployment creator fallback**: If no routing rule matches, the deployment creator is notified
|
||||
|
||||
<Tip>
|
||||
For implementation details on the `@human_feedback` decorator, see the [Human Feedback in Flows](/en/learn/human-feedback-in-flows) guide.
|
||||
</Tip>
|
||||
|
||||
## Setting Up Webhook-Based HITL Workflows
|
||||
|
||||
For custom integrations with external systems like Slack, Microsoft Teams, or your own applications, you can use the webhook-based approach:
|
||||
## Setting Up HITL Workflows
|
||||
|
||||
<Steps>
|
||||
<Step title="Configure Your Task">
|
||||
@@ -144,14 +99,3 @@ HITL workflows are particularly valuable for:
|
||||
- Sensitive or high-stakes operations
|
||||
- Creative tasks requiring human judgment
|
||||
- Compliance and regulatory reviews
|
||||
|
||||
## Learn More
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Flow HITL Management" icon="users-gear" href="/en/enterprise/features/flow-hitl-management">
|
||||
Explore the full Enterprise Flow HITL platform capabilities including email notifications, routing rules, auto-response, and analytics.
|
||||
</Card>
|
||||
<Card title="Human Feedback in Flows" icon="code" href="/en/learn/human-feedback-in-flows">
|
||||
Implementation guide for the `@human_feedback` decorator in your Flows.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -151,9 +151,3 @@ HITL workflows are particularly valuable for:
|
||||
- Sensitive or high-stakes operations
|
||||
- Creative tasks requiring human judgment
|
||||
- Compliance and regulatory reviews
|
||||
|
||||
## Enterprise Features
|
||||
|
||||
<Card title="Flow HITL Management Platform" icon="users-gear" href="/en/enterprise/features/flow-hitl-management">
|
||||
CrewAI Enterprise provides a comprehensive HITL management system for Flows with in-platform review, responder assignment, permissions, escalation policies, SLA management, dynamic routing, and full analytics. [Learn more →](/en/enterprise/features/flow-hitl-management)
|
||||
</Card>
|
||||
|
||||
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|
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|
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@@ -1,563 +0,0 @@
|
||||
---
|
||||
title: "Flow HITL 관리"
|
||||
description: "이메일 우선 알림, 라우팅 규칙 및 자동 응답 기능을 갖춘 Flow용 엔터프라이즈급 인간 검토"
|
||||
icon: "users-gear"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
<Note>
|
||||
Flow HITL 관리 기능은 `@human_feedback` 데코레이터가 필요하며, **CrewAI 버전 1.8.0 이상**에서 사용할 수 있습니다. 이 기능은 Crew가 아닌 **Flow**에만 적용됩니다.
|
||||
</Note>
|
||||
|
||||
CrewAI Enterprise는 AI 워크플로우를 협업적인 인간-AI 프로세스로 전환하는 Flow용 포괄적인 Human-in-the-Loop(HITL) 관리 시스템을 제공합니다. 플랫폼은 **이메일 우선 아키텍처**를 사용하여 이메일 주소가 있는 누구나 플랫폼 계정 없이도 검토 요청에 응답할 수 있습니다.
|
||||
|
||||
## 개요
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="이메일 우선 설계" icon="envelope">
|
||||
응답자가 알림 이메일에 직접 회신하여 피드백 제공 가능
|
||||
</Card>
|
||||
<Card title="유연한 라우팅" icon="route">
|
||||
메서드 패턴 또는 Flow 상태에 따라 특정 이메일로 요청 라우팅
|
||||
</Card>
|
||||
<Card title="자동 응답" icon="clock">
|
||||
시간 내에 인간이 응답하지 않을 경우 자동 대체 응답 구성
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### 주요 이점
|
||||
|
||||
- **간단한 멘탈 모델**: 이메일 주소는 보편적이며 플랫폼 사용자나 역할을 관리할 필요 없음
|
||||
- **외부 응답자**: 플랫폼 사용자가 아니어도 이메일이 있는 누구나 응답 가능
|
||||
- **동적 할당**: Flow 상태에서 직접 담당자 이메일 가져오기 (예: `sales_rep_email`)
|
||||
- **간소화된 구성**: 설정할 항목이 적어 더 빠르게 가치 실현
|
||||
- **이메일이 주요 채널**: 대부분의 사용자는 대시보드 로그인보다 이메일로 응답하는 것을 선호
|
||||
|
||||
## Flow에서 인간 검토 포인트 설정
|
||||
|
||||
`@human_feedback` 데코레이터를 사용하여 Flow 내에 인간 검토 체크포인트를 구성합니다. 실행이 검토 포인트에 도달하면 시스템이 일시 중지되고, 담당자에게 이메일로 알리며, 응답을 기다립니다.
|
||||
|
||||
```python
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
|
||||
class ContentApprovalFlow(Flow):
|
||||
@start()
|
||||
def generate_content(self):
|
||||
# AI가 콘텐츠 생성
|
||||
return "Q1 캠페인용 마케팅 카피 생성..."
|
||||
|
||||
@listen(generate_content)
|
||||
@human_feedback(
|
||||
message="브랜드 준수를 위해 이 콘텐츠를 검토해 주세요:",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
)
|
||||
def review_content(self, content):
|
||||
return content
|
||||
|
||||
@listen("approved")
|
||||
def publish_content(self, result: HumanFeedbackResult):
|
||||
print(f"승인된 콘텐츠 게시 중. 검토자 노트: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def archive_content(self, result: HumanFeedbackResult):
|
||||
print(f"콘텐츠 거부됨. 사유: {result.feedback}")
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise_content(self, result: HumanFeedbackResult):
|
||||
print(f"수정 요청: {result.feedback}")
|
||||
```
|
||||
|
||||
완전한 구현 세부 사항은 [Flow에서 인간 피드백](/ko/learn/human-feedback-in-flows) 가이드를 참조하세요.
|
||||
|
||||
### 데코레이터 파라미터
|
||||
|
||||
| 파라미터 | 유형 | 설명 |
|
||||
|---------|------|------|
|
||||
| `message` | `str` | 인간 검토자에게 표시되는 메시지 |
|
||||
| `emit` | `list[str]` | 유효한 응답 옵션 (UI에서 버튼으로 표시) |
|
||||
|
||||
## 플랫폼 구성
|
||||
|
||||
HITL 구성에 접근: **배포** → **설정** → **Human in the Loop 구성**
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-overview.png" alt="HITL 구성 설정" />
|
||||
</Frame>
|
||||
|
||||
### 이메일 알림
|
||||
|
||||
HITL 요청에 대한 이메일 알림을 활성화하거나 비활성화하는 토글입니다.
|
||||
|
||||
| 설정 | 기본값 | 설명 |
|
||||
|-----|-------|------|
|
||||
| 이메일 알림 | 활성화됨 | 피드백 요청 시 이메일 전송 |
|
||||
|
||||
<Note>
|
||||
비활성화되면 응답자는 대시보드 UI를 사용하거나 커스텀 알림 시스템을 위해 webhook을 구성해야 합니다.
|
||||
</Note>
|
||||
|
||||
### SLA 목표
|
||||
|
||||
추적 및 메트릭 목적으로 목표 응답 시간을 설정합니다.
|
||||
|
||||
| 설정 | 설명 |
|
||||
|-----|------|
|
||||
| SLA 목표 (분) | 목표 응답 시간. 대시보드 메트릭 및 SLA 추적에 사용 |
|
||||
|
||||
SLA 추적을 비활성화하려면 비워 두세요.
|
||||
|
||||
## 이메일 알림 및 응답
|
||||
|
||||
HITL 시스템은 응답자가 알림 이메일에 직접 회신할 수 있는 이메일 우선 아키텍처를 사용합니다.
|
||||
|
||||
### 이메일 응답 작동 방식
|
||||
|
||||
<Steps>
|
||||
<Step title="알림 전송">
|
||||
HITL 요청이 생성되면 검토 콘텐츠와 컨텍스트가 포함된 이메일이 할당된 응답자에게 전송됩니다.
|
||||
</Step>
|
||||
<Step title="Reply-To 주소">
|
||||
이메일에는 인증을 위한 서명된 토큰이 포함된 특별한 reply-to 주소가 있습니다.
|
||||
</Step>
|
||||
<Step title="사용자 회신">
|
||||
응답자는 이메일에 피드백으로 회신하면 됩니다—로그인 필요 없음.
|
||||
</Step>
|
||||
<Step title="토큰 검증">
|
||||
플랫폼이 회신을 받고, 서명된 토큰을 확인하고, 발신자 이메일을 매칭합니다.
|
||||
</Step>
|
||||
<Step title="Flow 재개">
|
||||
피드백이 기록되고 인간의 입력으로 Flow가 계속됩니다.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
### 응답 형식
|
||||
|
||||
응답자는 다음과 같이 회신할 수 있습니다:
|
||||
|
||||
- **Emit 옵션**: 회신이 `emit` 옵션과 일치하면 (예: "approved") 직접 사용됨
|
||||
- **자유 형식 텍스트**: 모든 텍스트 응답이 피드백으로 Flow에 전달됨
|
||||
- **일반 텍스트**: 회신 본문의 첫 번째 줄이 피드백으로 사용됨
|
||||
|
||||
### 확인 이메일
|
||||
|
||||
회신을 처리한 후 응답자는 피드백이 성공적으로 제출되었는지 또는 오류가 발생했는지 나타내는 확인 이메일을 받습니다.
|
||||
|
||||
### 이메일 토큰 보안
|
||||
|
||||
- 토큰은 보안을 위해 암호화 서명됨
|
||||
- 토큰은 7일 후 만료됨
|
||||
- 발신자 이메일은 토큰의 인증된 이메일과 일치해야 함
|
||||
- 처리 후 확인/오류 이메일 전송됨
|
||||
|
||||
## 라우팅 규칙
|
||||
|
||||
메서드 패턴에 따라 HITL 요청을 특정 이메일 주소로 라우팅합니다.
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-routing-rules.png" alt="HITL 라우팅 규칙 구성" />
|
||||
</Frame>
|
||||
|
||||
### 규칙 구조
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "재무팀으로 승인",
|
||||
"match": {
|
||||
"method_name": "approve_*"
|
||||
},
|
||||
"assign_to_email": "finance@company.com",
|
||||
"assign_from_input": "manager_email"
|
||||
}
|
||||
```
|
||||
|
||||
### 매칭 패턴
|
||||
|
||||
| 패턴 | 설명 | 매칭 예시 |
|
||||
|-----|------|----------|
|
||||
| `approve_*` | 와일드카드 (모든 문자) | `approve_payment`, `approve_vendor` |
|
||||
| `review_?` | 단일 문자 | `review_a`, `review_1` |
|
||||
| `validate_payment` | 정확히 일치 | `validate_payment`만 |
|
||||
|
||||
### 할당 우선순위
|
||||
|
||||
1. **동적 할당** (`assign_from_input`): 구성된 경우 Flow 상태에서 이메일 가져옴
|
||||
2. **정적 이메일** (`assign_to_email`): 구성된 이메일로 대체
|
||||
3. **배포 생성자**: 규칙이 일치하지 않으면 배포 생성자의 이메일이 사용됨
|
||||
|
||||
### 동적 할당 예제
|
||||
|
||||
Flow 상태에 `{"sales_rep_email": "alice@company.com"}`이 포함된 경우:
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "영업 담당자에게 라우팅",
|
||||
"match": {
|
||||
"method_name": "review_*"
|
||||
},
|
||||
"assign_from_input": "sales_rep_email"
|
||||
}
|
||||
```
|
||||
|
||||
요청이 자동으로 `alice@company.com`에 할당됩니다.
|
||||
|
||||
<Tip>
|
||||
**사용 사례**: CRM, 데이터베이스 또는 이전 Flow 단계에서 담당자를 가져와 적합한 사람에게 검토를 동적으로 라우팅하세요.
|
||||
</Tip>
|
||||
|
||||
## 자동 응답
|
||||
|
||||
시간 내에 인간이 응답하지 않으면 HITL 요청에 자동으로 응답합니다. 이를 통해 Flow가 무한정 중단되지 않도록 합니다.
|
||||
|
||||
### 구성
|
||||
|
||||
| 설정 | 설명 |
|
||||
|-----|------|
|
||||
| 활성화됨 | 자동 응답 활성화 토글 |
|
||||
| 타임아웃 (분) | 자동 응답 전 대기 시간 |
|
||||
| 기본 결과 | 응답 값 (`emit` 옵션과 일치해야 함) |
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-auto-respond.png" alt="HITL 자동 응답 구성" />
|
||||
</Frame>
|
||||
|
||||
### 사용 사례
|
||||
|
||||
- **SLA 준수**: Flow가 무한정 중단되지 않도록 보장
|
||||
- **기본 승인**: 타임아웃 후 저위험 요청 자동 승인
|
||||
- **우아한 저하**: 검토자가 없을 때 안전한 기본값으로 계속
|
||||
|
||||
<Warning>
|
||||
자동 응답을 신중하게 사용하세요. 기본 응답이 허용되는 중요하지 않은 검토에만 활성화하세요.
|
||||
</Warning>
|
||||
|
||||
## 검토 프로세스
|
||||
|
||||
### 대시보드 인터페이스
|
||||
|
||||
HITL 검토 인터페이스는 검토자에게 깔끔하고 집중된 경험을 제공합니다:
|
||||
|
||||
- **마크다운 렌더링**: 구문 강조가 포함된 풍부한 형식의 검토 콘텐츠
|
||||
- **컨텍스트 패널**: Flow 상태, 실행 기록 및 관련 정보 보기
|
||||
- **피드백 입력**: 결정과 함께 상세한 피드백 및 코멘트 제공
|
||||
- **빠른 작업**: 선택적 코멘트가 있는 원클릭 emit 옵션 버튼
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-list-pending-feedbacks.png" alt="HITL 대기 중인 요청 목록" />
|
||||
</Frame>
|
||||
|
||||
### 응답 방법
|
||||
|
||||
검토자는 세 가지 채널을 통해 응답할 수 있습니다:
|
||||
|
||||
| 방법 | 설명 |
|
||||
|-----|------|
|
||||
| **이메일 회신** | 알림 이메일에 직접 회신 |
|
||||
| **대시보드** | Enterprise 대시보드 UI 사용 |
|
||||
| **API/Webhook** | API를 통한 프로그래밍 방식 응답 |
|
||||
|
||||
### 기록 및 감사 추적
|
||||
|
||||
모든 HITL 상호작용은 완전한 타임라인으로 추적됩니다:
|
||||
|
||||
- 결정 기록 (승인/거부/수정)
|
||||
- 검토자 신원 및 타임스탬프
|
||||
- 제공된 피드백 및 코멘트
|
||||
- 응답 방법 (이메일/대시보드/API)
|
||||
- 응답 시간 메트릭
|
||||
|
||||
## 분석 및 모니터링
|
||||
|
||||
포괄적인 분석으로 HITL 성능을 추적합니다.
|
||||
|
||||
### 성능 대시보드
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-metrics.png" alt="HITL 메트릭 대시보드" />
|
||||
</Frame>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="응답 시간" icon="stopwatch">
|
||||
검토자 또는 Flow별 평균 및 중앙값 응답 시간 모니터링.
|
||||
</Card>
|
||||
<Card title="볼륨 트렌드" icon="chart-bar">
|
||||
팀 용량 최적화를 위한 검토 볼륨 패턴 분석.
|
||||
</Card>
|
||||
<Card title="결정 분포" icon="chart-pie">
|
||||
다양한 검토 유형에 대한 승인/거부 비율 보기.
|
||||
</Card>
|
||||
<Card title="SLA 추적" icon="chart-line">
|
||||
SLA 목표 내에 완료된 검토 비율 추적.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### 감사 및 규정 준수
|
||||
|
||||
규제 요구 사항을 위한 엔터프라이즈급 감사 기능:
|
||||
|
||||
- 타임스탬프가 있는 완전한 결정 기록
|
||||
- 검토자 신원 확인
|
||||
- 불변 감사 로그
|
||||
- 규정 준수 보고를 위한 내보내기 기능
|
||||
|
||||
## 일반적인 사용 사례
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="보안 검토" icon="shield-halved">
|
||||
**사용 사례**: 인간 검증이 포함된 내부 보안 설문지 자동화
|
||||
|
||||
- AI가 보안 설문지에 대한 응답 생성
|
||||
- 보안팀이 이메일로 정확성 검토 및 검증
|
||||
- 승인된 응답이 최종 제출물로 편집
|
||||
- 규정 준수를 위한 완전한 감사 추적
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="콘텐츠 승인" icon="file-lines">
|
||||
**사용 사례**: 법무/브랜드 검토가 필요한 마케팅 콘텐츠
|
||||
|
||||
- AI가 마케팅 카피 또는 소셜 미디어 콘텐츠 생성
|
||||
- 브랜드팀 이메일로 목소리/톤 검토를 위해 라우팅
|
||||
- 승인 시 자동 게시
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="재무 승인" icon="money-bill">
|
||||
**사용 사례**: 경비 보고서, 계약 조건, 예산 배분
|
||||
|
||||
- AI가 재무 요청을 사전 처리하고 분류
|
||||
- 동적 할당을 사용하여 금액 임계값에 따라 라우팅
|
||||
- 재무 규정 준수를 위한 완전한 감사 추적 유지
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="CRM에서 동적 할당" icon="database">
|
||||
**사용 사례**: CRM에서 계정 담당자에게 검토 라우팅
|
||||
|
||||
- Flow가 CRM에서 계정 담당자 이메일 가져옴
|
||||
- 이메일을 Flow 상태에 저장 (예: `account_owner_email`)
|
||||
- `assign_from_input`을 사용하여 적합한 사람에게 자동 라우팅
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="품질 보증" icon="magnifying-glass">
|
||||
**사용 사례**: 고객 전달 전 AI 출력 검증
|
||||
|
||||
- AI가 고객 대면 콘텐츠 또는 응답 생성
|
||||
- QA팀이 이메일 알림을 통해 검토
|
||||
- 피드백 루프가 시간이 지남에 따라 AI 성능 개선
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Webhook API
|
||||
|
||||
Flow가 인간 피드백을 위해 일시 중지되면, 요청 데이터를 자체 애플리케이션으로 보내도록 webhook을 구성할 수 있습니다. 이를 통해 다음이 가능합니다:
|
||||
|
||||
- 커스텀 승인 UI 구축
|
||||
- 내부 도구와 통합 (Jira, ServiceNow, 커스텀 대시보드)
|
||||
- 타사 시스템으로 승인 라우팅
|
||||
- 모바일 앱 알림
|
||||
- 자동화된 결정 시스템
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-webhook.png" alt="HITL Webhook 구성" />
|
||||
</Frame>
|
||||
|
||||
### Webhook 구성
|
||||
|
||||
<Steps>
|
||||
<Step title="설정으로 이동">
|
||||
**배포** → **설정** → **Human in the Loop**으로 이동
|
||||
</Step>
|
||||
<Step title="Webhook 섹션 확장">
|
||||
**Webhooks** 구성을 클릭하여 확장
|
||||
</Step>
|
||||
<Step title="Webhook URL 추가">
|
||||
webhook URL 입력 (프로덕션에서는 HTTPS 필수)
|
||||
</Step>
|
||||
<Step title="구성 저장">
|
||||
**구성 저장**을 클릭하여 활성화
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
여러 webhook을 구성할 수 있습니다. 각 활성 webhook은 모든 HITL 이벤트를 수신합니다.
|
||||
|
||||
### Webhook 이벤트
|
||||
|
||||
엔드포인트는 다음 이벤트에 대해 HTTP POST 요청을 수신합니다:
|
||||
|
||||
| 이벤트 유형 | 트리거 시점 |
|
||||
|------------|------------|
|
||||
| `new_request` | Flow가 일시 중지되고 인간 피드백을 요청할 때 |
|
||||
|
||||
### Webhook 페이로드
|
||||
|
||||
모든 webhook은 다음 구조의 JSON 페이로드를 수신합니다:
|
||||
|
||||
```json
|
||||
{
|
||||
"event": "new_request",
|
||||
"request": {
|
||||
"id": "550e8400-e29b-41d4-a716-446655440000",
|
||||
"flow_id": "flow_abc123",
|
||||
"method_name": "review_article",
|
||||
"message": "이 기사의 게시를 검토해 주세요.",
|
||||
"emit_options": ["approved", "rejected", "request_changes"],
|
||||
"state": {
|
||||
"article_id": 12345,
|
||||
"author": "john@example.com",
|
||||
"category": "technology"
|
||||
},
|
||||
"metadata": {},
|
||||
"created_at": "2026-01-14T12:00:00Z"
|
||||
},
|
||||
"deployment": {
|
||||
"id": 456,
|
||||
"name": "Content Review Flow",
|
||||
"organization_id": 789
|
||||
},
|
||||
"callback_url": "https://api.crewai.com/...",
|
||||
"assigned_to_email": "reviewer@company.com"
|
||||
}
|
||||
```
|
||||
|
||||
### 요청에 응답하기
|
||||
|
||||
피드백을 제출하려면 webhook 페이로드에 포함된 **`callback_url`로 POST**합니다.
|
||||
|
||||
```http
|
||||
POST {callback_url}
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"feedback": "승인됨. 훌륭한 기사입니다!",
|
||||
"source": "my_custom_app"
|
||||
}
|
||||
```
|
||||
|
||||
### 보안
|
||||
|
||||
<Info>
|
||||
모든 webhook 요청은 HMAC-SHA256을 사용하여 암호화 서명되어 진위성을 보장하고 변조를 방지합니다.
|
||||
</Info>
|
||||
|
||||
#### Webhook 보안
|
||||
|
||||
- **HMAC-SHA256 서명**: 모든 webhook에 암호화 서명이 포함됨
|
||||
- **Webhook별 시크릿**: 각 webhook은 고유한 서명 시크릿을 가짐
|
||||
- **저장 시 암호화**: 서명 시크릿은 데이터베이스에서 암호화됨
|
||||
- **타임스탬프 검증**: 리플레이 공격 방지
|
||||
|
||||
#### 서명 헤더
|
||||
|
||||
각 webhook 요청에는 다음 헤더가 포함됩니다:
|
||||
|
||||
| 헤더 | 설명 |
|
||||
|------|------|
|
||||
| `X-Signature` | HMAC-SHA256 서명: `sha256=<hex_digest>` |
|
||||
| `X-Timestamp` | 요청이 서명된 Unix 타임스탬프 |
|
||||
|
||||
#### 검증
|
||||
|
||||
다음과 같이 계산하여 검증합니다:
|
||||
|
||||
```python
|
||||
import hmac
|
||||
import hashlib
|
||||
|
||||
expected = hmac.new(
|
||||
signing_secret.encode(),
|
||||
f"{timestamp}.{payload}".encode(),
|
||||
hashlib.sha256
|
||||
).hexdigest()
|
||||
|
||||
if hmac.compare_digest(expected, signature):
|
||||
# 유효한 서명
|
||||
```
|
||||
|
||||
### 오류 처리
|
||||
|
||||
webhook 엔드포인트는 수신 확인을 위해 2xx 상태 코드를 반환해야 합니다:
|
||||
|
||||
| 응답 | 동작 |
|
||||
|------|------|
|
||||
| 2xx | Webhook 성공적으로 전달됨 |
|
||||
| 4xx/5xx | 실패로 기록됨, 재시도 없음 |
|
||||
| 타임아웃 (30초) | 실패로 기록됨, 재시도 없음 |
|
||||
|
||||
## 보안 및 RBAC
|
||||
|
||||
### 대시보드 접근
|
||||
|
||||
HITL 접근은 배포 수준에서 제어됩니다:
|
||||
|
||||
| 권한 | 기능 |
|
||||
|-----|------|
|
||||
| `manage_human_feedback` | HITL 설정 구성, 모든 요청 보기 |
|
||||
| `respond_to_human_feedback` | 요청에 응답, 할당된 요청 보기 |
|
||||
|
||||
### 이메일 응답 인증
|
||||
|
||||
이메일 회신의 경우:
|
||||
1. reply-to 토큰이 인증된 이메일을 인코딩
|
||||
2. 발신자 이메일이 토큰의 이메일과 일치해야 함
|
||||
3. 토큰이 만료되지 않아야 함 (기본 7일)
|
||||
4. 요청이 여전히 대기 중이어야 함
|
||||
|
||||
### 감사 추적
|
||||
|
||||
모든 HITL 작업이 기록됩니다:
|
||||
- 요청 생성
|
||||
- 할당 변경
|
||||
- 응답 제출 (소스: 대시보드/이메일/API)
|
||||
- Flow 재개 상태
|
||||
|
||||
## 문제 해결
|
||||
|
||||
### 이메일이 전송되지 않음
|
||||
|
||||
1. 구성에서 "이메일 알림"이 활성화되어 있는지 확인
|
||||
2. 라우팅 규칙이 메서드 이름과 일치하는지 확인
|
||||
3. 담당자 이메일이 유효한지 확인
|
||||
4. 라우팅 규칙이 일치하지 않는 경우 배포 생성자 대체 확인
|
||||
|
||||
### 이메일 회신이 처리되지 않음
|
||||
|
||||
1. 토큰이 만료되지 않았는지 확인 (기본 7일)
|
||||
2. 발신자 이메일이 할당된 이메일과 일치하는지 확인
|
||||
3. 요청이 여전히 대기 중인지 확인 (아직 응답되지 않음)
|
||||
|
||||
### Flow가 재개되지 않음
|
||||
|
||||
1. 대시보드에서 요청 상태 확인
|
||||
2. 콜백 URL에 접근 가능한지 확인
|
||||
3. 배포가 여전히 실행 중인지 확인
|
||||
|
||||
## 모범 사례
|
||||
|
||||
<Tip>
|
||||
**간단하게 시작**: 배포 생성자에게 이메일 알림으로 시작한 다음, 워크플로우가 성숙해지면 라우팅 규칙을 추가하세요.
|
||||
</Tip>
|
||||
|
||||
1. **동적 할당 사용**: 유연한 라우팅을 위해 Flow 상태에서 담당자 이메일을 가져오세요.
|
||||
|
||||
2. **자동 응답 구성**: 중요하지 않은 검토에 대해 Flow가 중단되지 않도록 대체를 설정하세요.
|
||||
|
||||
3. **응답 시간 모니터링**: 분석을 사용하여 병목 현상을 식별하고 검토 프로세스를 최적화하세요.
|
||||
|
||||
4. **검토 메시지를 명확하게 유지**: `@human_feedback` 데코레이터에 명확하고 실행 가능한 메시지를 작성하세요.
|
||||
|
||||
5. **이메일 흐름 테스트**: 프로덕션에 가기 전에 테스트 요청을 보내 이메일 전달을 확인하세요.
|
||||
|
||||
## 관련 리소스
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Flow에서 인간 피드백" icon="code" href="/ko/learn/human-feedback-in-flows">
|
||||
`@human_feedback` 데코레이터 구현 가이드
|
||||
</Card>
|
||||
<Card title="Flow HITL 워크플로우 가이드" icon="route" href="/ko/enterprise/guides/human-in-the-loop">
|
||||
HITL 워크플로우 설정을 위한 단계별 가이드
|
||||
</Card>
|
||||
<Card title="RBAC 구성" icon="shield-check" href="/ko/enterprise/features/rbac">
|
||||
조직을 위한 역할 기반 접근 제어 구성
|
||||
</Card>
|
||||
<Card title="Webhook 스트리밍" icon="bolt" href="/ko/enterprise/features/webhook-streaming">
|
||||
실시간 이벤트 알림 설정
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -5,54 +5,9 @@ icon: "user-check"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
인간-중심(Human-In-The-Loop, HITL)은 인공지능과 인간 전문 지식을 결합하여 의사결정을 강화하고 작업 결과를 향상시키는 강력한 접근 방식입니다. 이 가이드는 CrewAI Enterprise 내에서 HITL을 구현하는 방법을 보여줍니다.
|
||||
인간-중심(Human-In-The-Loop, HITL)은 인공지능과 인간 전문 지식을 결합하여 의사결정을 강화하고 작업 결과를 향상시키는 강력한 접근 방식입니다. 이 가이드는 CrewAI 내에서 HITL을 구현하는 방법을 보여줍니다.
|
||||
|
||||
## CrewAI의 HITL 접근 방식
|
||||
|
||||
CrewAI는 human-in-the-loop 워크플로우를 구현하기 위한 두 가지 접근 방식을 제공합니다:
|
||||
|
||||
| 접근 방식 | 적합한 용도 | 버전 |
|
||||
|----------|----------|---------|
|
||||
| **Flow 기반** (`@human_feedback` 데코레이터) | Enterprise UI를 사용한 프로덕션, 이메일 우선 워크플로우, 전체 플랫폼 기능 | **1.8.0+** |
|
||||
| **Webhook 기반** | 커스텀 통합, 외부 시스템 (Slack, Teams 등), 레거시 설정 | 모든 버전 |
|
||||
|
||||
## Enterprise 플랫폼과 Flow 기반 HITL
|
||||
|
||||
<Note>
|
||||
`@human_feedback` 데코레이터는 **CrewAI 버전 1.8.0 이상**이 필요합니다.
|
||||
</Note>
|
||||
|
||||
Flow에서 `@human_feedback` 데코레이터를 사용하면, CrewAI Enterprise는 이메일 주소가 있는 누구나 검토 요청에 응답할 수 있는 **이메일 우선 HITL 시스템**을 제공합니다:
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="이메일 우선 설계" icon="envelope">
|
||||
응답자가 이메일 알림을 받고 직접 회신할 수 있습니다—로그인이 필요 없습니다.
|
||||
</Card>
|
||||
<Card title="대시보드 검토" icon="desktop">
|
||||
원할 때 Enterprise 대시보드에서 HITL 요청을 검토하고 응답하세요.
|
||||
</Card>
|
||||
<Card title="유연한 라우팅" icon="route">
|
||||
메서드 패턴에 따라 특정 이메일로 요청을 라우팅하거나 Flow 상태에서 가져오세요.
|
||||
</Card>
|
||||
<Card title="자동 응답" icon="clock">
|
||||
타임아웃 내에 인간이 응답하지 않을 경우 자동 대체 응답을 구성하세요.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### 주요 이점
|
||||
|
||||
- **외부 응답자**: 플랫폼 사용자가 아니어도 이메일이 있는 누구나 응답 가능
|
||||
- **동적 할당**: Flow 상태에서 담당자 이메일 가져오기 (예: `account_owner_email`)
|
||||
- **간단한 구성**: 이메일 기반 라우팅은 사용자/역할 관리보다 설정이 쉬움
|
||||
- **배포 생성자 대체**: 라우팅 규칙이 일치하지 않으면 배포 생성자에게 알림
|
||||
|
||||
<Tip>
|
||||
`@human_feedback` 데코레이터의 구현 세부 사항은 [Flow에서 인간 피드백](/ko/learn/human-feedback-in-flows) 가이드를 참조하세요.
|
||||
</Tip>
|
||||
|
||||
## Webhook 기반 HITL 워크플로 설정
|
||||
|
||||
Slack, Microsoft Teams 또는 자체 애플리케이션과 같은 외부 시스템과의 커스텀 통합을 위해 webhook 기반 접근 방식을 사용할 수 있습니다:
|
||||
## HITL 워크플로 설정
|
||||
|
||||
<Steps>
|
||||
<Step title="작업 구성">
|
||||
@@ -144,14 +99,3 @@ HITL 워크플로우는 특히 다음과 같은 경우에 유용합니다:
|
||||
- 민감하거나 위험도가 높은 작업
|
||||
- 인간의 판단이 필요한 창의적 작업
|
||||
- 준수 및 규제 검토
|
||||
|
||||
## 자세히 알아보기
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Flow HITL 관리" icon="users-gear" href="/ko/enterprise/features/flow-hitl-management">
|
||||
이메일 알림, 라우팅 규칙, 자동 응답 및 분석을 포함한 전체 Enterprise Flow HITL 플랫폼 기능을 살펴보세요.
|
||||
</Card>
|
||||
<Card title="Flow에서 인간 피드백" icon="code" href="/ko/learn/human-feedback-in-flows">
|
||||
Flow에서 `@human_feedback` 데코레이터 구현 가이드.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -112,9 +112,3 @@ HITL 워크플로우는 다음과 같은 경우에 특히 유용합니다:
|
||||
- 민감하거나 고위험 작업
|
||||
- 인간의 판단이 필요한 창의적 과제
|
||||
- 컴플라이언스 및 규제 검토
|
||||
|
||||
## Enterprise 기능
|
||||
|
||||
<Card title="Flow HITL 관리 플랫폼" icon="users-gear" href="/ko/enterprise/features/flow-hitl-management">
|
||||
CrewAI Enterprise는 플랫폼 내 검토, 응답자 할당, 권한, 에스컬레이션 정책, SLA 관리, 동적 라우팅 및 전체 분석을 갖춘 Flow용 포괄적인 HITL 관리 시스템을 제공합니다. [자세히 알아보기 →](/ko/enterprise/features/flow-hitl-management)
|
||||
</Card>
|
||||
|
||||
@@ -1,563 +0,0 @@
|
||||
---
|
||||
title: "Gerenciamento HITL para Flows"
|
||||
description: "Revisão humana de nível empresarial para Flows com notificações por email, regras de roteamento e capacidades de resposta automática"
|
||||
icon: "users-gear"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
<Note>
|
||||
Os recursos de gerenciamento HITL para Flows requerem o decorador `@human_feedback`, disponível no **CrewAI versão 1.8.0 ou superior**. Estes recursos aplicam-se especificamente a **Flows**, não a Crews.
|
||||
</Note>
|
||||
|
||||
O CrewAI Enterprise oferece um sistema abrangente de gerenciamento Human-in-the-Loop (HITL) para Flows que transforma fluxos de trabalho de IA em processos colaborativos humano-IA. A plataforma usa uma **arquitetura email-first** que permite que qualquer pessoa com um endereço de email responda a solicitações de revisão—sem necessidade de conta na plataforma.
|
||||
|
||||
## Visão Geral
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Design Email-First" icon="envelope">
|
||||
Respondentes podem responder diretamente aos emails de notificação para fornecer feedback
|
||||
</Card>
|
||||
<Card title="Roteamento Flexível" icon="route">
|
||||
Direcione solicitações para emails específicos com base em padrões de método ou estado do flow
|
||||
</Card>
|
||||
<Card title="Resposta Automática" icon="clock">
|
||||
Configure respostas automáticas de fallback quando nenhum humano responder a tempo
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### Principais Benefícios
|
||||
|
||||
- **Modelo mental simples**: Endereços de email são universais; não é necessário gerenciar usuários ou funções da plataforma
|
||||
- **Respondentes externos**: Qualquer pessoa com email pode responder, mesmo não sendo usuário da plataforma
|
||||
- **Atribuição dinâmica**: Obtenha o email do responsável diretamente do estado do flow (ex: `sales_rep_email`)
|
||||
- **Configuração reduzida**: Menos configurações para definir, tempo mais rápido para gerar valor
|
||||
- **Email como canal principal**: A maioria dos usuários prefere responder via email do que fazer login em um dashboard
|
||||
|
||||
## Configurando Pontos de Revisão Humana em Flows
|
||||
|
||||
Configure checkpoints de revisão humana em seus Flows usando o decorador `@human_feedback`. Quando a execução atinge um ponto de revisão, o sistema pausa, notifica o responsável via email e aguarda uma resposta.
|
||||
|
||||
```python
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
|
||||
class ContentApprovalFlow(Flow):
|
||||
@start()
|
||||
def generate_content(self):
|
||||
# IA gera conteúdo
|
||||
return "Texto de marketing gerado para campanha Q1..."
|
||||
|
||||
@listen(generate_content)
|
||||
@human_feedback(
|
||||
message="Por favor, revise este conteúdo para conformidade com a marca:",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
)
|
||||
def review_content(self, content):
|
||||
return content
|
||||
|
||||
@listen("approved")
|
||||
def publish_content(self, result: HumanFeedbackResult):
|
||||
print(f"Publicando conteúdo aprovado. Notas do revisor: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def archive_content(self, result: HumanFeedbackResult):
|
||||
print(f"Conteúdo rejeitado. Motivo: {result.feedback}")
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise_content(self, result: HumanFeedbackResult):
|
||||
print(f"Revisão solicitada: {result.feedback}")
|
||||
```
|
||||
|
||||
Para detalhes completos de implementação, consulte o guia [Feedback Humano em Flows](/pt-BR/learn/human-feedback-in-flows).
|
||||
|
||||
### Parâmetros do Decorador
|
||||
|
||||
| Parâmetro | Tipo | Descrição |
|
||||
|-----------|------|-----------|
|
||||
| `message` | `str` | A mensagem exibida para o revisor humano |
|
||||
| `emit` | `list[str]` | Opções de resposta válidas (exibidas como botões na UI) |
|
||||
|
||||
## Configuração da Plataforma
|
||||
|
||||
Acesse a configuração HITL em: **Deployment** → **Settings** → **Human in the Loop Configuration**
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-overview.png" alt="Configurações HITL" />
|
||||
</Frame>
|
||||
|
||||
### Notificações por Email
|
||||
|
||||
Toggle para ativar ou desativar notificações por email para solicitações HITL.
|
||||
|
||||
| Configuração | Padrão | Descrição |
|
||||
|--------------|--------|-----------|
|
||||
| Notificações por Email | Ativado | Enviar emails quando feedback for solicitado |
|
||||
|
||||
<Note>
|
||||
Quando desativado, os respondentes devem usar a UI do dashboard ou você deve configurar webhooks para sistemas de notificação personalizados.
|
||||
</Note>
|
||||
|
||||
### Meta de SLA
|
||||
|
||||
Defina um tempo de resposta alvo para fins de rastreamento e métricas.
|
||||
|
||||
| Configuração | Descrição |
|
||||
|--------------|-----------|
|
||||
| Meta de SLA (minutos) | Tempo de resposta alvo. Usado para métricas do dashboard e rastreamento de SLA |
|
||||
|
||||
Deixe vazio para desativar o rastreamento de SLA.
|
||||
|
||||
## Notificações e Respostas por Email
|
||||
|
||||
O sistema HITL usa uma arquitetura email-first onde os respondentes podem responder diretamente aos emails de notificação.
|
||||
|
||||
### Como Funcionam as Respostas por Email
|
||||
|
||||
<Steps>
|
||||
<Step title="Notificação Enviada">
|
||||
Quando uma solicitação HITL é criada, um email é enviado ao respondente atribuído com o conteúdo e contexto da revisão.
|
||||
</Step>
|
||||
<Step title="Endereço Reply-To">
|
||||
O email inclui um endereço reply-to especial com um token assinado para autenticação.
|
||||
</Step>
|
||||
<Step title="Usuário Responde">
|
||||
O respondente simplesmente responde ao email com seu feedback—nenhum login necessário.
|
||||
</Step>
|
||||
<Step title="Validação do Token">
|
||||
A plataforma recebe a resposta, verifica o token assinado e corresponde o email do remetente.
|
||||
</Step>
|
||||
<Step title="Flow Continua">
|
||||
O feedback é registrado e o flow continua com a entrada humana.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
### Formato de Resposta
|
||||
|
||||
Respondentes podem responder com:
|
||||
|
||||
- **Opção emit**: Se a resposta corresponder a uma opção `emit` (ex: "approved"), ela é usada diretamente
|
||||
- **Texto livre**: Qualquer resposta de texto é passada para o flow como feedback
|
||||
- **Texto simples**: A primeira linha do corpo da resposta é usada como feedback
|
||||
|
||||
### Emails de Confirmação
|
||||
|
||||
Após processar uma resposta, o respondente recebe um email de confirmação indicando se o feedback foi enviado com sucesso ou se ocorreu um erro.
|
||||
|
||||
### Segurança do Token de Email
|
||||
|
||||
- Tokens são assinados criptograficamente para segurança
|
||||
- Tokens expiram após 7 dias
|
||||
- Email do remetente deve corresponder ao email autorizado do token
|
||||
- Emails de confirmação/erro são enviados após o processamento
|
||||
|
||||
## Regras de Roteamento
|
||||
|
||||
Direcione solicitações HITL para endereços de email específicos com base em padrões de método.
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-routing-rules.png" alt="Configuração de Regras de Roteamento HITL" />
|
||||
</Frame>
|
||||
|
||||
### Estrutura da Regra
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "Aprovações para Financeiro",
|
||||
"match": {
|
||||
"method_name": "approve_*"
|
||||
},
|
||||
"assign_to_email": "financeiro@empresa.com",
|
||||
"assign_from_input": "manager_email"
|
||||
}
|
||||
```
|
||||
|
||||
### Padrões de Correspondência
|
||||
|
||||
| Padrão | Descrição | Exemplo de Correspondência |
|
||||
|--------|-----------|---------------------------|
|
||||
| `approve_*` | Wildcard (qualquer caractere) | `approve_payment`, `approve_vendor` |
|
||||
| `review_?` | Caractere único | `review_a`, `review_1` |
|
||||
| `validate_payment` | Correspondência exata | apenas `validate_payment` |
|
||||
|
||||
### Prioridade de Atribuição
|
||||
|
||||
1. **Atribuição dinâmica** (`assign_from_input`): Se configurado, obtém email do estado do flow
|
||||
2. **Email estático** (`assign_to_email`): Fallback para email configurado
|
||||
3. **Criador do deployment**: Se nenhuma regra corresponder, o email do criador do deployment é usado
|
||||
|
||||
### Exemplo de Atribuição Dinâmica
|
||||
|
||||
Se seu estado do flow contém `{"sales_rep_email": "alice@empresa.com"}`, configure:
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "Direcionar para Representante de Vendas",
|
||||
"match": {
|
||||
"method_name": "review_*"
|
||||
},
|
||||
"assign_from_input": "sales_rep_email"
|
||||
}
|
||||
```
|
||||
|
||||
A solicitação será atribuída automaticamente para `alice@empresa.com`.
|
||||
|
||||
<Tip>
|
||||
**Caso de Uso**: Obtenha o responsável do seu CRM, banco de dados ou etapa anterior do flow para direcionar revisões dinamicamente para a pessoa certa.
|
||||
</Tip>
|
||||
|
||||
## Resposta Automática
|
||||
|
||||
Responda automaticamente a solicitações HITL se nenhum humano responder dentro do timeout. Isso garante que os flows não fiquem travados indefinidamente.
|
||||
|
||||
### Configuração
|
||||
|
||||
| Configuração | Descrição |
|
||||
|--------------|-----------|
|
||||
| Ativado | Toggle para ativar resposta automática |
|
||||
| Timeout (minutos) | Tempo de espera antes de responder automaticamente |
|
||||
| Resultado Padrão | O valor da resposta (deve corresponder a uma opção `emit`) |
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-auto-respond.png" alt="Configuração de Resposta Automática HITL" />
|
||||
</Frame>
|
||||
|
||||
### Casos de Uso
|
||||
|
||||
- **Conformidade com SLA**: Garante que flows não fiquem travados indefinidamente
|
||||
- **Aprovação padrão**: Aprove automaticamente solicitações de baixo risco após timeout
|
||||
- **Degradação graciosa**: Continue com um padrão seguro quando revisores não estiverem disponíveis
|
||||
|
||||
<Warning>
|
||||
Use resposta automática com cuidado. Ative apenas para revisões não críticas onde uma resposta padrão é aceitável.
|
||||
</Warning>
|
||||
|
||||
## Processo de Revisão
|
||||
|
||||
### Interface do Dashboard
|
||||
|
||||
A interface de revisão HITL oferece uma experiência limpa e focada para revisores:
|
||||
|
||||
- **Renderização Markdown**: Formatação rica para conteúdo de revisão com destaque de sintaxe
|
||||
- **Painel de Contexto**: Visualize estado do flow, histórico de execução e informações relacionadas
|
||||
- **Entrada de Feedback**: Forneça feedback detalhado e comentários com sua decisão
|
||||
- **Ações Rápidas**: Botões de opção emit com um clique com comentários opcionais
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-list-pending-feedbacks.png" alt="Lista de Solicitações HITL Pendentes" />
|
||||
</Frame>
|
||||
|
||||
### Métodos de Resposta
|
||||
|
||||
Revisores podem responder por três canais:
|
||||
|
||||
| Método | Descrição |
|
||||
|--------|-----------|
|
||||
| **Resposta por Email** | Responda diretamente ao email de notificação |
|
||||
| **Dashboard** | Use a UI do dashboard Enterprise |
|
||||
| **API/Webhook** | Resposta programática via API |
|
||||
|
||||
### Histórico e Trilha de Auditoria
|
||||
|
||||
Toda interação HITL é rastreada com uma linha do tempo completa:
|
||||
|
||||
- Histórico de decisões (aprovar/rejeitar/revisar)
|
||||
- Identidade do revisor e timestamp
|
||||
- Feedback e comentários fornecidos
|
||||
- Método de resposta (email/dashboard/API)
|
||||
- Métricas de tempo de resposta
|
||||
|
||||
## Análise e Monitoramento
|
||||
|
||||
Acompanhe o desempenho HITL com análises abrangentes.
|
||||
|
||||
### Dashboard de Desempenho
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-metrics.png" alt="Dashboard de Métricas HITL" />
|
||||
</Frame>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Tempos de Resposta" icon="stopwatch">
|
||||
Monitore tempos de resposta médios e medianos por revisor ou flow.
|
||||
</Card>
|
||||
<Card title="Tendências de Volume" icon="chart-bar">
|
||||
Analise padrões de volume de revisão para otimizar capacidade da equipe.
|
||||
</Card>
|
||||
<Card title="Distribuição de Decisões" icon="chart-pie">
|
||||
Visualize taxas de aprovação/rejeição em diferentes tipos de revisão.
|
||||
</Card>
|
||||
<Card title="Rastreamento de SLA" icon="chart-line">
|
||||
Acompanhe a porcentagem de revisões concluídas dentro das metas de SLA.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### Auditoria e Conformidade
|
||||
|
||||
Capacidades de auditoria prontas para empresas para requisitos regulatórios:
|
||||
|
||||
- Histórico completo de decisões com timestamps
|
||||
- Verificação de identidade do revisor
|
||||
- Logs de auditoria imutáveis
|
||||
- Capacidades de exportação para relatórios de conformidade
|
||||
|
||||
## Casos de Uso Comuns
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Revisões de Segurança" icon="shield-halved">
|
||||
**Caso de Uso**: Automação de questionários de segurança internos com validação humana
|
||||
|
||||
- IA gera respostas para questionários de segurança
|
||||
- Equipe de segurança revisa e valida precisão via email
|
||||
- Respostas aprovadas são compiladas na submissão final
|
||||
- Trilha de auditoria completa para conformidade
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Aprovação de Conteúdo" icon="file-lines">
|
||||
**Caso de Uso**: Conteúdo de marketing que requer revisão legal/marca
|
||||
|
||||
- IA gera texto de marketing ou conteúdo de mídia social
|
||||
- Roteie para email da equipe de marca para revisão de voz/tom
|
||||
- Publicação automática após aprovação
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Aprovações Financeiras" icon="money-bill">
|
||||
**Caso de Uso**: Relatórios de despesas, termos de contrato, alocações de orçamento
|
||||
|
||||
- IA pré-processa e categoriza solicitações financeiras
|
||||
- Roteie com base em limites de valor usando atribuição dinâmica
|
||||
- Mantenha trilha de auditoria completa para conformidade financeira
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Atribuição Dinâmica do CRM" icon="database">
|
||||
**Caso de Uso**: Direcione revisões para proprietários de conta do seu CRM
|
||||
|
||||
- Flow obtém email do proprietário da conta do CRM
|
||||
- Armazene email no estado do flow (ex: `account_owner_email`)
|
||||
- Use `assign_from_input` para direcionar automaticamente para a pessoa certa
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Garantia de Qualidade" icon="magnifying-glass">
|
||||
**Caso de Uso**: Validação de saída de IA antes da entrega ao cliente
|
||||
|
||||
- IA gera conteúdo ou respostas voltadas ao cliente
|
||||
- Equipe de QA revisa via notificação por email
|
||||
- Loops de feedback melhoram desempenho da IA ao longo do tempo
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## API de Webhooks
|
||||
|
||||
Quando seus Flows pausam para feedback humano, você pode configurar webhooks para enviar dados da solicitação para sua própria aplicação. Isso permite:
|
||||
|
||||
- Construir UIs de aprovação personalizadas
|
||||
- Integrar com ferramentas internas (Jira, ServiceNow, dashboards personalizados)
|
||||
- Rotear aprovações para sistemas de terceiros
|
||||
- Notificações em apps mobile
|
||||
- Sistemas de decisão automatizados
|
||||
|
||||
<Frame>
|
||||
<img src="/images/enterprise/hitl-settings-webhook.png" alt="Configuração de Webhook HITL" />
|
||||
</Frame>
|
||||
|
||||
### Configurando Webhooks
|
||||
|
||||
<Steps>
|
||||
<Step title="Navegue até Configurações">
|
||||
Vá para **Deployment** → **Settings** → **Human in the Loop**
|
||||
</Step>
|
||||
<Step title="Expanda a Seção Webhooks">
|
||||
Clique para expandir a configuração de **Webhooks**
|
||||
</Step>
|
||||
<Step title="Adicione sua URL de Webhook">
|
||||
Digite sua URL de webhook (deve ser HTTPS em produção)
|
||||
</Step>
|
||||
<Step title="Salve a Configuração">
|
||||
Clique em **Salvar Configuração** para ativar
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
Você pode configurar múltiplos webhooks. Cada webhook ativo recebe todos os eventos HITL.
|
||||
|
||||
### Eventos de Webhook
|
||||
|
||||
Seu endpoint receberá requisições HTTP POST para estes eventos:
|
||||
|
||||
| Tipo de Evento | Quando é Disparado |
|
||||
|----------------|-------------------|
|
||||
| `new_request` | Um flow pausa e solicita feedback humano |
|
||||
|
||||
### Payload do Webhook
|
||||
|
||||
Todos os webhooks recebem um payload JSON com esta estrutura:
|
||||
|
||||
```json
|
||||
{
|
||||
"event": "new_request",
|
||||
"request": {
|
||||
"id": "550e8400-e29b-41d4-a716-446655440000",
|
||||
"flow_id": "flow_abc123",
|
||||
"method_name": "review_article",
|
||||
"message": "Por favor, revise este artigo para publicação.",
|
||||
"emit_options": ["approved", "rejected", "request_changes"],
|
||||
"state": {
|
||||
"article_id": 12345,
|
||||
"author": "john@example.com",
|
||||
"category": "technology"
|
||||
},
|
||||
"metadata": {},
|
||||
"created_at": "2026-01-14T12:00:00Z"
|
||||
},
|
||||
"deployment": {
|
||||
"id": 456,
|
||||
"name": "Content Review Flow",
|
||||
"organization_id": 789
|
||||
},
|
||||
"callback_url": "https://api.crewai.com/...",
|
||||
"assigned_to_email": "reviewer@company.com"
|
||||
}
|
||||
```
|
||||
|
||||
### Respondendo a Solicitações
|
||||
|
||||
Para enviar feedback, **faça POST para a `callback_url`** incluída no payload do webhook.
|
||||
|
||||
```http
|
||||
POST {callback_url}
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"feedback": "Aprovado. Ótimo artigo!",
|
||||
"source": "my_custom_app"
|
||||
}
|
||||
```
|
||||
|
||||
### Segurança
|
||||
|
||||
<Info>
|
||||
Todas as requisições de webhook são assinadas criptograficamente usando HMAC-SHA256 para garantir autenticidade e prevenir adulteração.
|
||||
</Info>
|
||||
|
||||
#### Segurança do Webhook
|
||||
|
||||
- **Assinaturas HMAC-SHA256**: Todo webhook inclui uma assinatura criptográfica
|
||||
- **Secrets por webhook**: Cada webhook tem seu próprio secret de assinatura único
|
||||
- **Criptografado em repouso**: Os secrets de assinatura são criptografados no nosso banco de dados
|
||||
- **Verificação de timestamp**: Previne ataques de replay
|
||||
|
||||
#### Headers de Assinatura
|
||||
|
||||
Cada requisição de webhook inclui estes headers:
|
||||
|
||||
| Header | Descrição |
|
||||
|--------|-----------|
|
||||
| `X-Signature` | Assinatura HMAC-SHA256: `sha256=<hex_digest>` |
|
||||
| `X-Timestamp` | Timestamp Unix de quando a requisição foi assinada |
|
||||
|
||||
#### Verificação
|
||||
|
||||
Verifique computando:
|
||||
|
||||
```python
|
||||
import hmac
|
||||
import hashlib
|
||||
|
||||
expected = hmac.new(
|
||||
signing_secret.encode(),
|
||||
f"{timestamp}.{payload}".encode(),
|
||||
hashlib.sha256
|
||||
).hexdigest()
|
||||
|
||||
if hmac.compare_digest(expected, signature):
|
||||
# Assinatura válida
|
||||
```
|
||||
|
||||
### Tratamento de Erros
|
||||
|
||||
Seu endpoint de webhook deve retornar um código de status 2xx para confirmar o recebimento:
|
||||
|
||||
| Sua Resposta | Nosso Comportamento |
|
||||
|--------------|---------------------|
|
||||
| 2xx | Webhook entregue com sucesso |
|
||||
| 4xx/5xx | Registrado como falha, sem retry |
|
||||
| Timeout (30s) | Registrado como falha, sem retry |
|
||||
|
||||
## Segurança e RBAC
|
||||
|
||||
### Acesso ao Dashboard
|
||||
|
||||
O acesso HITL é controlado no nível do deployment:
|
||||
|
||||
| Permissão | Capacidade |
|
||||
|-----------|------------|
|
||||
| `manage_human_feedback` | Configurar settings HITL, ver todas as solicitações |
|
||||
| `respond_to_human_feedback` | Responder a solicitações, ver solicitações atribuídas |
|
||||
|
||||
### Autorização de Resposta por Email
|
||||
|
||||
Para respostas por email:
|
||||
1. O token reply-to codifica o email autorizado
|
||||
2. Email do remetente deve corresponder ao email do token
|
||||
3. Token não deve estar expirado (padrão 7 dias)
|
||||
4. Solicitação ainda deve estar pendente
|
||||
|
||||
### Trilha de Auditoria
|
||||
|
||||
Todas as ações HITL são registradas:
|
||||
- Criação de solicitação
|
||||
- Mudanças de atribuição
|
||||
- Submissão de resposta (com fonte: dashboard/email/API)
|
||||
- Status de retomada do flow
|
||||
|
||||
## Solução de Problemas
|
||||
|
||||
### Emails Não Enviando
|
||||
|
||||
1. Verifique se "Notificações por Email" está ativado na configuração
|
||||
2. Verifique se as regras de roteamento correspondem ao nome do método
|
||||
3. Verifique se o email do responsável é válido
|
||||
4. Verifique o fallback do criador do deployment se nenhuma regra de roteamento corresponder
|
||||
|
||||
### Respostas de Email Não Processando
|
||||
|
||||
1. Verifique se o token não expirou (padrão 7 dias)
|
||||
2. Verifique se o email do remetente corresponde ao email atribuído
|
||||
3. Garanta que a solicitação ainda está pendente (não respondida ainda)
|
||||
|
||||
### Flow Não Retomando
|
||||
|
||||
1. Verifique o status da solicitação no dashboard
|
||||
2. Verifique se a URL de callback está acessível
|
||||
3. Garanta que o deployment ainda está rodando
|
||||
|
||||
## Melhores Práticas
|
||||
|
||||
<Tip>
|
||||
**Comece Simples**: Comece com notificações por email para o criador do deployment, depois adicione regras de roteamento conforme seus fluxos de trabalho amadurecem.
|
||||
</Tip>
|
||||
|
||||
1. **Use Atribuição Dinâmica**: Obtenha emails de responsáveis do seu estado do flow para roteamento flexível.
|
||||
|
||||
2. **Configure Resposta Automática**: Defina um fallback para revisões não críticas para evitar que flows fiquem travados.
|
||||
|
||||
3. **Monitore Tempos de Resposta**: Use análises para identificar gargalos e otimizar seu processo de revisão.
|
||||
|
||||
4. **Mantenha Mensagens de Revisão Claras**: Escreva mensagens claras e acionáveis no decorador `@human_feedback`.
|
||||
|
||||
5. **Teste o Fluxo de Email**: Envie solicitações de teste para verificar a entrega de email antes de ir para produção.
|
||||
|
||||
## Recursos Relacionados
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Feedback Humano em Flows" icon="code" href="/pt-BR/learn/human-feedback-in-flows">
|
||||
Guia de implementação para o decorador `@human_feedback`
|
||||
</Card>
|
||||
<Card title="Guia de Workflow HITL para Flows" icon="route" href="/pt-BR/enterprise/guides/human-in-the-loop">
|
||||
Guia passo a passo para configurar workflows HITL
|
||||
</Card>
|
||||
<Card title="Configuração RBAC" icon="shield-check" href="/pt-BR/enterprise/features/rbac">
|
||||
Configure controle de acesso baseado em função para sua organização
|
||||
</Card>
|
||||
<Card title="Streaming de Webhook" icon="bolt" href="/pt-BR/enterprise/features/webhook-streaming">
|
||||
Configure notificações de eventos em tempo real
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -5,54 +5,9 @@ icon: "user-check"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
Human-In-The-Loop (HITL) é uma abordagem poderosa que combina inteligência artificial com expertise humana para aprimorar a tomada de decisão e melhorar os resultados das tarefas. Este guia mostra como implementar HITL dentro do CrewAI Enterprise.
|
||||
Human-In-The-Loop (HITL) é uma abordagem poderosa que combina inteligência artificial com expertise humana para aprimorar a tomada de decisão e melhorar os resultados das tarefas. Este guia mostra como implementar HITL dentro do CrewAI.
|
||||
|
||||
## Abordagens HITL no CrewAI
|
||||
|
||||
CrewAI oferece duas abordagens para implementar workflows human-in-the-loop:
|
||||
|
||||
| Abordagem | Melhor Para | Versão |
|
||||
|----------|----------|---------|
|
||||
| **Baseada em Flow** (decorador `@human_feedback`) | Produção com UI Enterprise, workflows email-first, recursos completos da plataforma | **1.8.0+** |
|
||||
| **Baseada em Webhook** | Integrações customizadas, sistemas externos (Slack, Teams, etc.), configurações legadas | Todas as versões |
|
||||
|
||||
## HITL Baseado em Flow com Plataforma Enterprise
|
||||
|
||||
<Note>
|
||||
O decorador `@human_feedback` requer **CrewAI versão 1.8.0 ou superior**.
|
||||
</Note>
|
||||
|
||||
Ao usar o decorador `@human_feedback` em seus Flows, o CrewAI Enterprise oferece um **sistema HITL email-first** que permite que qualquer pessoa com um endereço de email responda a solicitações de revisão:
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Design Email-First" icon="envelope">
|
||||
Respondentes recebem notificações por email e podem responder diretamente—nenhum login necessário.
|
||||
</Card>
|
||||
<Card title="Revisão no Dashboard" icon="desktop">
|
||||
Revise e responda a solicitações HITL no dashboard Enterprise quando preferir.
|
||||
</Card>
|
||||
<Card title="Roteamento Flexível" icon="route">
|
||||
Direcione solicitações para emails específicos com base em padrões de método ou obtenha do estado do flow.
|
||||
</Card>
|
||||
<Card title="Resposta Automática" icon="clock">
|
||||
Configure respostas automáticas de fallback quando nenhum humano responder dentro do timeout.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### Principais Benefícios
|
||||
|
||||
- **Respondentes externos**: Qualquer pessoa com email pode responder, mesmo não sendo usuário da plataforma
|
||||
- **Atribuição dinâmica**: Obtenha o email do responsável do estado do flow (ex: `account_owner_email`)
|
||||
- **Configuração simples**: Roteamento baseado em email é mais fácil de configurar do que gerenciamento de usuários/funções
|
||||
- **Fallback do criador do deployment**: Se nenhuma regra de roteamento corresponder, o criador do deployment é notificado
|
||||
|
||||
<Tip>
|
||||
Para detalhes de implementação do decorador `@human_feedback`, consulte o guia [Feedback Humano em Flows](/pt-BR/learn/human-feedback-in-flows).
|
||||
</Tip>
|
||||
|
||||
## Configurando Workflows HITL Baseados em Webhook
|
||||
|
||||
Para integrações customizadas com sistemas externos como Slack, Microsoft Teams ou suas próprias aplicações, você pode usar a abordagem baseada em webhook:
|
||||
## Configurando Workflows HITL
|
||||
|
||||
<Steps>
|
||||
<Step title="Configure Sua Tarefa">
|
||||
@@ -144,14 +99,3 @@ Workflows HITL são particularmente valiosos para:
|
||||
- Operações sensíveis ou de alto risco
|
||||
- Tarefas criativas que exigem julgamento humano
|
||||
- Revisões de conformidade e regulatórias
|
||||
|
||||
## Saiba Mais
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Gerenciamento HITL para Flows" icon="users-gear" href="/pt-BR/enterprise/features/flow-hitl-management">
|
||||
Explore os recursos completos da plataforma HITL para Flows, incluindo notificações por email, regras de roteamento, resposta automática e análises.
|
||||
</Card>
|
||||
<Card title="Feedback Humano em Flows" icon="code" href="/pt-BR/learn/human-feedback-in-flows">
|
||||
Guia de implementação para o decorador `@human_feedback` em seus Flows.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -112,9 +112,3 @@ Workflows HITL são particularmente valiosos para:
|
||||
- Operações sensíveis ou de alto risco
|
||||
- Tarefas criativas que requerem julgamento humano
|
||||
- Revisões de conformidade e regulamentação
|
||||
|
||||
## Recursos Enterprise
|
||||
|
||||
<Card title="Plataforma de Gerenciamento HITL para Flows" icon="users-gear" href="/pt-BR/enterprise/features/flow-hitl-management">
|
||||
O CrewAI Enterprise oferece um sistema abrangente de gerenciamento HITL para Flows com revisão na plataforma, atribuição de respondentes, permissões, políticas de escalação, gerenciamento de SLA, roteamento dinâmico e análises completas. [Saiba mais →](/pt-BR/enterprise/features/flow-hitl-management)
|
||||
</Card>
|
||||
|
||||
@@ -152,4 +152,4 @@ __all__ = [
|
||||
"wrap_file_source",
|
||||
]
|
||||
|
||||
__version__ = "1.9.2"
|
||||
__version__ = "1.9.0"
|
||||
|
||||
@@ -12,7 +12,7 @@ dependencies = [
|
||||
"pytube~=15.0.0",
|
||||
"requests~=2.32.5",
|
||||
"docker~=7.1.0",
|
||||
"crewai==1.9.2",
|
||||
"crewai==1.9.0",
|
||||
"lancedb~=0.5.4",
|
||||
"tiktoken~=0.8.0",
|
||||
"beautifulsoup4~=4.13.4",
|
||||
|
||||
@@ -291,4 +291,4 @@ __all__ = [
|
||||
"ZapierActionTools",
|
||||
]
|
||||
|
||||
__version__ = "1.9.2"
|
||||
__version__ = "1.9.0"
|
||||
|
||||
@@ -1,11 +1,10 @@
|
||||
"""Crewai Enterprise Tools."""
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Any
|
||||
import json
|
||||
import re
|
||||
from typing import Any, Optional, Union, cast, get_origin
|
||||
|
||||
from crewai.tools import BaseTool
|
||||
from crewai.utilities.pydantic_schema_utils import create_model_from_schema
|
||||
from pydantic import Field, create_model
|
||||
import requests
|
||||
|
||||
@@ -15,6 +14,77 @@ from crewai_tools.tools.crewai_platform_tools.misc import (
|
||||
)
|
||||
|
||||
|
||||
class AllOfSchemaAnalyzer:
|
||||
"""Helper class to analyze and merge allOf schemas."""
|
||||
|
||||
def __init__(self, schemas: list[dict[str, Any]]):
|
||||
self.schemas = schemas
|
||||
self._explicit_types: list[str] = []
|
||||
self._merged_properties: dict[str, Any] = {}
|
||||
self._merged_required: list[str] = []
|
||||
self._analyze_schemas()
|
||||
|
||||
def _analyze_schemas(self) -> None:
|
||||
"""Analyze all schemas and extract relevant information."""
|
||||
for schema in self.schemas:
|
||||
if "type" in schema:
|
||||
self._explicit_types.append(schema["type"])
|
||||
|
||||
# Merge object properties
|
||||
if schema.get("type") == "object" and "properties" in schema:
|
||||
self._merged_properties.update(schema["properties"])
|
||||
if "required" in schema:
|
||||
self._merged_required.extend(schema["required"])
|
||||
|
||||
def has_consistent_type(self) -> bool:
|
||||
"""Check if all schemas have the same explicit type."""
|
||||
return len(set(self._explicit_types)) == 1 if self._explicit_types else False
|
||||
|
||||
def get_consistent_type(self) -> type[Any]:
|
||||
"""Get the consistent type if all schemas agree."""
|
||||
if not self.has_consistent_type():
|
||||
raise ValueError("No consistent type found")
|
||||
|
||||
type_mapping = {
|
||||
"string": str,
|
||||
"integer": int,
|
||||
"number": float,
|
||||
"boolean": bool,
|
||||
"array": list,
|
||||
"object": dict,
|
||||
"null": type(None),
|
||||
}
|
||||
return type_mapping.get(self._explicit_types[0], str)
|
||||
|
||||
def has_object_schemas(self) -> bool:
|
||||
"""Check if any schemas are object types with properties."""
|
||||
return bool(self._merged_properties)
|
||||
|
||||
def get_merged_properties(self) -> dict[str, Any]:
|
||||
"""Get merged properties from all object schemas."""
|
||||
return self._merged_properties
|
||||
|
||||
def get_merged_required_fields(self) -> list[str]:
|
||||
"""Get merged required fields from all object schemas."""
|
||||
return list(set(self._merged_required)) # Remove duplicates
|
||||
|
||||
def get_fallback_type(self) -> type[Any]:
|
||||
"""Get a fallback type when merging fails."""
|
||||
if self._explicit_types:
|
||||
# Use the first explicit type
|
||||
type_mapping = {
|
||||
"string": str,
|
||||
"integer": int,
|
||||
"number": float,
|
||||
"boolean": bool,
|
||||
"array": list,
|
||||
"object": dict,
|
||||
"null": type(None),
|
||||
}
|
||||
return type_mapping.get(self._explicit_types[0], str)
|
||||
return str
|
||||
|
||||
|
||||
class CrewAIPlatformActionTool(BaseTool):
|
||||
action_name: str = Field(default="", description="The name of the action")
|
||||
action_schema: dict[str, Any] = Field(
|
||||
@@ -27,19 +97,42 @@ class CrewAIPlatformActionTool(BaseTool):
|
||||
action_name: str,
|
||||
action_schema: dict[str, Any],
|
||||
):
|
||||
parameters = action_schema.get("function", {}).get("parameters", {})
|
||||
self._model_registry: dict[str, type[Any]] = {}
|
||||
self._base_name = self._sanitize_name(action_name)
|
||||
|
||||
schema_props, required = self._extract_schema_info(action_schema)
|
||||
|
||||
field_definitions: dict[str, Any] = {}
|
||||
for param_name, param_details in schema_props.items():
|
||||
param_desc = param_details.get("description", "")
|
||||
is_required = param_name in required
|
||||
|
||||
if parameters and parameters.get("properties"):
|
||||
try:
|
||||
if "title" not in parameters:
|
||||
parameters = {**parameters, "title": f"{action_name}Schema"}
|
||||
if "type" not in parameters:
|
||||
parameters = {**parameters, "type": "object"}
|
||||
args_schema = create_model_from_schema(parameters)
|
||||
field_type = self._process_schema_type(
|
||||
param_details, self._sanitize_name(param_name).title()
|
||||
)
|
||||
except Exception:
|
||||
args_schema = create_model(f"{action_name}Schema")
|
||||
field_type = str
|
||||
|
||||
field_definitions[param_name] = self._create_field_definition(
|
||||
field_type, is_required, param_desc
|
||||
)
|
||||
|
||||
if field_definitions:
|
||||
try:
|
||||
args_schema = create_model(
|
||||
f"{self._base_name}Schema", **field_definitions
|
||||
)
|
||||
except Exception:
|
||||
args_schema = create_model(
|
||||
f"{self._base_name}Schema",
|
||||
input_text=(str, Field(description="Input for the action")),
|
||||
)
|
||||
else:
|
||||
args_schema = create_model(f"{action_name}Schema")
|
||||
args_schema = create_model(
|
||||
f"{self._base_name}Schema",
|
||||
input_text=(str, Field(description="Input for the action")),
|
||||
)
|
||||
|
||||
super().__init__(
|
||||
name=action_name.lower().replace(" ", "_"),
|
||||
@@ -49,12 +142,285 @@ class CrewAIPlatformActionTool(BaseTool):
|
||||
self.action_name = action_name
|
||||
self.action_schema = action_schema
|
||||
|
||||
def _run(self, **kwargs: Any) -> str:
|
||||
@staticmethod
|
||||
def _sanitize_name(name: str) -> str:
|
||||
name = name.lower().replace(" ", "_")
|
||||
sanitized = re.sub(r"[^a-zA-Z0-9_]", "", name)
|
||||
parts = sanitized.split("_")
|
||||
return "".join(word.capitalize() for word in parts if word)
|
||||
|
||||
@staticmethod
|
||||
def _extract_schema_info(
|
||||
action_schema: dict[str, Any],
|
||||
) -> tuple[dict[str, Any], list[str]]:
|
||||
schema_props = (
|
||||
action_schema.get("function", {})
|
||||
.get("parameters", {})
|
||||
.get("properties", {})
|
||||
)
|
||||
required = (
|
||||
action_schema.get("function", {}).get("parameters", {}).get("required", [])
|
||||
)
|
||||
return schema_props, required
|
||||
|
||||
def _process_schema_type(self, schema: dict[str, Any], type_name: str) -> type[Any]:
|
||||
"""
|
||||
Process a JSON Schema type definition into a Python type.
|
||||
|
||||
Handles complex schema constructs like anyOf, oneOf, allOf, enums, arrays, and objects.
|
||||
"""
|
||||
# Handle composite schema types (anyOf, oneOf, allOf)
|
||||
if composite_type := self._process_composite_schema(schema, type_name):
|
||||
return composite_type
|
||||
|
||||
# Handle primitive types and simple constructs
|
||||
return self._process_primitive_schema(schema, type_name)
|
||||
|
||||
def _process_composite_schema(
|
||||
self, schema: dict[str, Any], type_name: str
|
||||
) -> type[Any] | None:
|
||||
"""Process composite schema types: anyOf, oneOf, allOf."""
|
||||
if "anyOf" in schema:
|
||||
return self._process_any_of_schema(schema["anyOf"], type_name)
|
||||
if "oneOf" in schema:
|
||||
return self._process_one_of_schema(schema["oneOf"], type_name)
|
||||
if "allOf" in schema:
|
||||
return self._process_all_of_schema(schema["allOf"], type_name)
|
||||
return None
|
||||
|
||||
def _process_any_of_schema(
|
||||
self, any_of_types: list[dict[str, Any]], type_name: str
|
||||
) -> type[Any]:
|
||||
"""Process anyOf schema - creates Union of possible types."""
|
||||
is_nullable = any(t.get("type") == "null" for t in any_of_types)
|
||||
non_null_types = [t for t in any_of_types if t.get("type") != "null"]
|
||||
|
||||
if not non_null_types:
|
||||
return cast(
|
||||
type[Any], cast(object, str | None)
|
||||
) # fallback for only-null case
|
||||
|
||||
base_type = (
|
||||
self._process_schema_type(non_null_types[0], type_name)
|
||||
if len(non_null_types) == 1
|
||||
else self._create_union_type(non_null_types, type_name, "AnyOf")
|
||||
)
|
||||
return base_type | None if is_nullable else base_type # type: ignore[return-value]
|
||||
|
||||
def _process_one_of_schema(
|
||||
self, one_of_types: list[dict[str, Any]], type_name: str
|
||||
) -> type[Any]:
|
||||
"""Process oneOf schema - creates Union of mutually exclusive types."""
|
||||
return (
|
||||
self._process_schema_type(one_of_types[0], type_name)
|
||||
if len(one_of_types) == 1
|
||||
else self._create_union_type(one_of_types, type_name, "OneOf")
|
||||
)
|
||||
|
||||
def _process_all_of_schema(
|
||||
self, all_of_schemas: list[dict[str, Any]], type_name: str
|
||||
) -> type[Any]:
|
||||
"""Process allOf schema - merges schemas that must all be satisfied."""
|
||||
if len(all_of_schemas) == 1:
|
||||
return self._process_schema_type(all_of_schemas[0], type_name)
|
||||
return self._merge_all_of_schemas(all_of_schemas, type_name)
|
||||
|
||||
def _create_union_type(
|
||||
self, schemas: list[dict[str, Any]], type_name: str, prefix: str
|
||||
) -> type[Any]:
|
||||
"""Create a Union type from multiple schemas."""
|
||||
return Union[ # type: ignore # noqa: UP007
|
||||
tuple(
|
||||
self._process_schema_type(schema, f"{type_name}{prefix}{i}")
|
||||
for i, schema in enumerate(schemas)
|
||||
)
|
||||
]
|
||||
|
||||
def _process_primitive_schema(
|
||||
self, schema: dict[str, Any], type_name: str
|
||||
) -> type[Any]:
|
||||
"""Process primitive schema types: string, number, array, object, etc."""
|
||||
json_type = schema.get("type", "string")
|
||||
|
||||
if "enum" in schema:
|
||||
return self._process_enum_schema(schema, json_type)
|
||||
|
||||
if json_type == "array":
|
||||
return self._process_array_schema(schema, type_name)
|
||||
|
||||
if json_type == "object":
|
||||
return self._create_nested_model(schema, type_name)
|
||||
|
||||
return self._map_json_type_to_python(json_type)
|
||||
|
||||
def _process_enum_schema(self, schema: dict[str, Any], json_type: str) -> type[Any]:
|
||||
"""Process enum schema - currently falls back to base type."""
|
||||
enum_values = schema["enum"]
|
||||
if not enum_values:
|
||||
return self._map_json_type_to_python(json_type)
|
||||
|
||||
# For Literal types, we need to pass the values directly, not as a tuple
|
||||
# This is a workaround since we can't dynamically create Literal types easily
|
||||
# Fall back to the base JSON type for now
|
||||
return self._map_json_type_to_python(json_type)
|
||||
|
||||
def _process_array_schema(
|
||||
self, schema: dict[str, Any], type_name: str
|
||||
) -> type[Any]:
|
||||
items_schema = schema.get("items", {"type": "string"})
|
||||
item_type = self._process_schema_type(items_schema, f"{type_name}Item")
|
||||
return list[item_type] # type: ignore
|
||||
|
||||
def _merge_all_of_schemas(
|
||||
self, schemas: list[dict[str, Any]], type_name: str
|
||||
) -> type[Any]:
|
||||
schema_analyzer = AllOfSchemaAnalyzer(schemas)
|
||||
|
||||
if schema_analyzer.has_consistent_type():
|
||||
return schema_analyzer.get_consistent_type()
|
||||
|
||||
if schema_analyzer.has_object_schemas():
|
||||
return self._create_merged_object_model(
|
||||
schema_analyzer.get_merged_properties(),
|
||||
schema_analyzer.get_merged_required_fields(),
|
||||
type_name,
|
||||
)
|
||||
|
||||
return schema_analyzer.get_fallback_type()
|
||||
|
||||
def _create_merged_object_model(
|
||||
self, properties: dict[str, Any], required: list[str], model_name: str
|
||||
) -> type[Any]:
|
||||
full_model_name = f"{self._base_name}{model_name}AllOf"
|
||||
|
||||
if full_model_name in self._model_registry:
|
||||
return self._model_registry[full_model_name]
|
||||
|
||||
if not properties:
|
||||
return dict
|
||||
|
||||
field_definitions = self._build_field_definitions(
|
||||
properties, required, model_name
|
||||
)
|
||||
|
||||
try:
|
||||
merged_model = create_model(full_model_name, **field_definitions)
|
||||
self._model_registry[full_model_name] = merged_model
|
||||
return merged_model
|
||||
except Exception:
|
||||
return dict
|
||||
|
||||
def _build_field_definitions(
|
||||
self, properties: dict[str, Any], required: list[str], model_name: str
|
||||
) -> dict[str, Any]:
|
||||
field_definitions = {}
|
||||
|
||||
for prop_name, prop_schema in properties.items():
|
||||
prop_desc = prop_schema.get("description", "")
|
||||
is_required = prop_name in required
|
||||
|
||||
try:
|
||||
prop_type = self._process_schema_type(
|
||||
prop_schema, f"{model_name}{self._sanitize_name(prop_name).title()}"
|
||||
)
|
||||
except Exception:
|
||||
prop_type = str
|
||||
|
||||
field_definitions[prop_name] = self._create_field_definition(
|
||||
prop_type, is_required, prop_desc
|
||||
)
|
||||
|
||||
return field_definitions
|
||||
|
||||
def _create_nested_model(
|
||||
self, schema: dict[str, Any], model_name: str
|
||||
) -> type[Any]:
|
||||
full_model_name = f"{self._base_name}{model_name}"
|
||||
|
||||
if full_model_name in self._model_registry:
|
||||
return self._model_registry[full_model_name]
|
||||
|
||||
properties = schema.get("properties", {})
|
||||
required_fields = schema.get("required", [])
|
||||
|
||||
if not properties:
|
||||
return dict
|
||||
|
||||
field_definitions = {}
|
||||
for prop_name, prop_schema in properties.items():
|
||||
prop_desc = prop_schema.get("description", "")
|
||||
is_required = prop_name in required_fields
|
||||
|
||||
try:
|
||||
prop_type = self._process_schema_type(
|
||||
prop_schema, f"{model_name}{self._sanitize_name(prop_name).title()}"
|
||||
)
|
||||
except Exception:
|
||||
prop_type = str
|
||||
|
||||
field_definitions[prop_name] = self._create_field_definition(
|
||||
prop_type, is_required, prop_desc
|
||||
)
|
||||
|
||||
try:
|
||||
nested_model = create_model(full_model_name, **field_definitions) # type: ignore
|
||||
self._model_registry[full_model_name] = nested_model
|
||||
return nested_model
|
||||
except Exception:
|
||||
return dict
|
||||
|
||||
def _create_field_definition(
|
||||
self, field_type: type[Any], is_required: bool, description: str
|
||||
) -> tuple:
|
||||
if is_required:
|
||||
return (field_type, Field(description=description))
|
||||
if get_origin(field_type) is Union:
|
||||
return (field_type, Field(default=None, description=description))
|
||||
return (
|
||||
Optional[field_type], # noqa: UP045
|
||||
Field(default=None, description=description),
|
||||
)
|
||||
|
||||
def _map_json_type_to_python(self, json_type: str) -> type[Any]:
|
||||
type_mapping = {
|
||||
"string": str,
|
||||
"integer": int,
|
||||
"number": float,
|
||||
"boolean": bool,
|
||||
"array": list,
|
||||
"object": dict,
|
||||
"null": type(None),
|
||||
}
|
||||
return type_mapping.get(json_type, str)
|
||||
|
||||
def _get_required_nullable_fields(self) -> list[str]:
|
||||
schema_props, required = self._extract_schema_info(self.action_schema)
|
||||
|
||||
required_nullable_fields = []
|
||||
for param_name in required:
|
||||
param_details = schema_props.get(param_name, {})
|
||||
if self._is_nullable_type(param_details):
|
||||
required_nullable_fields.append(param_name)
|
||||
|
||||
return required_nullable_fields
|
||||
|
||||
def _is_nullable_type(self, schema: dict[str, Any]) -> bool:
|
||||
if "anyOf" in schema:
|
||||
return any(t.get("type") == "null" for t in schema["anyOf"])
|
||||
return schema.get("type") == "null"
|
||||
|
||||
def _run(self, **kwargs) -> str:
|
||||
try:
|
||||
cleaned_kwargs = {
|
||||
key: value for key, value in kwargs.items() if value is not None
|
||||
}
|
||||
|
||||
required_nullable_fields = self._get_required_nullable_fields()
|
||||
|
||||
for field_name in required_nullable_fields:
|
||||
if field_name not in cleaned_kwargs:
|
||||
cleaned_kwargs[field_name] = None
|
||||
|
||||
api_url = (
|
||||
f"{get_platform_api_base_url()}/actions/{self.action_name}/execute"
|
||||
)
|
||||
@@ -63,9 +429,7 @@ class CrewAIPlatformActionTool(BaseTool):
|
||||
"Authorization": f"Bearer {token}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
payload = {
|
||||
"integration": cleaned_kwargs if cleaned_kwargs else {"_noop": True}
|
||||
}
|
||||
payload = cleaned_kwargs
|
||||
|
||||
response = requests.post(
|
||||
url=api_url,
|
||||
@@ -77,14 +441,7 @@ class CrewAIPlatformActionTool(BaseTool):
|
||||
|
||||
data = response.json()
|
||||
if not response.ok:
|
||||
if isinstance(data, dict):
|
||||
error_info = data.get("error", {})
|
||||
if isinstance(error_info, dict):
|
||||
error_message = error_info.get("message", json.dumps(data))
|
||||
else:
|
||||
error_message = str(error_info)
|
||||
else:
|
||||
error_message = str(data)
|
||||
error_message = data.get("error", {}).get("message", json.dumps(data))
|
||||
return f"API request failed: {error_message}"
|
||||
|
||||
return json.dumps(data, indent=2)
|
||||
|
||||
@@ -1,10 +1,5 @@
|
||||
"""CrewAI platform tool builder for fetching and creating action tools."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
from types import TracebackType
|
||||
from typing import Any
|
||||
|
||||
import os
|
||||
from crewai.tools import BaseTool
|
||||
import requests
|
||||
|
||||
@@ -17,29 +12,22 @@ from crewai_tools.tools.crewai_platform_tools.misc import (
|
||||
)
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CrewaiPlatformToolBuilder:
|
||||
"""Builds platform tools from remote action schemas."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
apps: list[str],
|
||||
) -> None:
|
||||
):
|
||||
self._apps = apps
|
||||
self._actions_schema: dict[str, dict[str, Any]] = {}
|
||||
self._tools: list[BaseTool] | None = None
|
||||
self._actions_schema = {} # type: ignore[var-annotated]
|
||||
self._tools = None
|
||||
|
||||
def tools(self) -> list[BaseTool]:
|
||||
"""Fetch actions and return built tools."""
|
||||
if self._tools is None:
|
||||
self._fetch_actions()
|
||||
self._create_tools()
|
||||
return self._tools if self._tools is not None else []
|
||||
|
||||
def _fetch_actions(self) -> None:
|
||||
"""Fetch action schemas from the platform API."""
|
||||
def _fetch_actions(self):
|
||||
actions_url = f"{get_platform_api_base_url()}/actions"
|
||||
headers = {"Authorization": f"Bearer {get_platform_integration_token()}"}
|
||||
|
||||
@@ -52,8 +40,7 @@ class CrewaiPlatformToolBuilder:
|
||||
verify=os.environ.get("CREWAI_FACTORY", "false").lower() != "true",
|
||||
)
|
||||
response.raise_for_status()
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to fetch platform tools for apps {self._apps}: {e}")
|
||||
except Exception:
|
||||
return
|
||||
|
||||
raw_data = response.json()
|
||||
@@ -64,8 +51,6 @@ class CrewaiPlatformToolBuilder:
|
||||
for app, action_list in action_categories.items():
|
||||
if isinstance(action_list, list):
|
||||
for action in action_list:
|
||||
if not isinstance(action, dict):
|
||||
continue
|
||||
if action_name := action.get("name"):
|
||||
action_schema = {
|
||||
"function": {
|
||||
@@ -79,16 +64,72 @@ class CrewaiPlatformToolBuilder:
|
||||
}
|
||||
self._actions_schema[action_name] = action_schema
|
||||
|
||||
def _create_tools(self) -> None:
|
||||
"""Create tool instances from fetched action schemas."""
|
||||
tools: list[BaseTool] = []
|
||||
def _generate_detailed_description(
|
||||
self, schema: dict[str, Any], indent: int = 0
|
||||
) -> list[str]:
|
||||
descriptions = []
|
||||
indent_str = " " * indent
|
||||
|
||||
schema_type = schema.get("type", "string")
|
||||
|
||||
if schema_type == "object":
|
||||
properties = schema.get("properties", {})
|
||||
required_fields = schema.get("required", [])
|
||||
|
||||
if properties:
|
||||
descriptions.append(f"{indent_str}Object with properties:")
|
||||
for prop_name, prop_schema in properties.items():
|
||||
prop_desc = prop_schema.get("description", "")
|
||||
is_required = prop_name in required_fields
|
||||
req_str = " (required)" if is_required else " (optional)"
|
||||
descriptions.append(
|
||||
f"{indent_str} - {prop_name}: {prop_desc}{req_str}"
|
||||
)
|
||||
|
||||
if prop_schema.get("type") == "object":
|
||||
descriptions.extend(
|
||||
self._generate_detailed_description(prop_schema, indent + 2)
|
||||
)
|
||||
elif prop_schema.get("type") == "array":
|
||||
items_schema = prop_schema.get("items", {})
|
||||
if items_schema.get("type") == "object":
|
||||
descriptions.append(f"{indent_str} Array of objects:")
|
||||
descriptions.extend(
|
||||
self._generate_detailed_description(
|
||||
items_schema, indent + 3
|
||||
)
|
||||
)
|
||||
elif "enum" in items_schema:
|
||||
descriptions.append(
|
||||
f"{indent_str} Array of enum values: {items_schema['enum']}"
|
||||
)
|
||||
elif "enum" in prop_schema:
|
||||
descriptions.append(
|
||||
f"{indent_str} Enum values: {prop_schema['enum']}"
|
||||
)
|
||||
|
||||
return descriptions
|
||||
|
||||
def _create_tools(self):
|
||||
tools = []
|
||||
|
||||
for action_name, action_schema in self._actions_schema.items():
|
||||
function_details = action_schema.get("function", {})
|
||||
description = function_details.get("description", f"Execute {action_name}")
|
||||
|
||||
parameters = function_details.get("parameters", {})
|
||||
param_descriptions = []
|
||||
|
||||
if parameters.get("properties"):
|
||||
param_descriptions.append("\nDetailed Parameter Structure:")
|
||||
param_descriptions.extend(
|
||||
self._generate_detailed_description(parameters)
|
||||
)
|
||||
|
||||
full_description = description + "\n".join(param_descriptions)
|
||||
|
||||
tool = CrewAIPlatformActionTool(
|
||||
description=description,
|
||||
description=full_description,
|
||||
action_name=action_name,
|
||||
action_schema=action_schema,
|
||||
)
|
||||
@@ -97,14 +138,8 @@ class CrewaiPlatformToolBuilder:
|
||||
|
||||
self._tools = tools
|
||||
|
||||
def __enter__(self) -> list[BaseTool]:
|
||||
"""Enter context manager and return tools."""
|
||||
def __enter__(self):
|
||||
return self.tools()
|
||||
|
||||
def __exit__(
|
||||
self,
|
||||
exc_type: type[BaseException] | None,
|
||||
exc_val: BaseException | None,
|
||||
exc_tb: TracebackType | None,
|
||||
) -> None:
|
||||
"""Exit context manager."""
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
pass
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
from typing import Union, get_args, get_origin
|
||||
from unittest.mock import patch, Mock
|
||||
import os
|
||||
|
||||
@@ -6,6 +7,251 @@ from crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool import
|
||||
)
|
||||
|
||||
|
||||
class TestSchemaProcessing:
|
||||
|
||||
def setup_method(self):
|
||||
self.base_action_schema = {
|
||||
"function": {
|
||||
"parameters": {
|
||||
"properties": {},
|
||||
"required": []
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
def create_test_tool(self, action_name="test_action"):
|
||||
return CrewAIPlatformActionTool(
|
||||
description="Test tool",
|
||||
action_name=action_name,
|
||||
action_schema=self.base_action_schema
|
||||
)
|
||||
|
||||
def test_anyof_multiple_types(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"anyOf": [
|
||||
{"type": "string"},
|
||||
{"type": "number"},
|
||||
{"type": "integer"}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestField")
|
||||
|
||||
assert get_origin(result_type) is Union
|
||||
|
||||
args = get_args(result_type)
|
||||
expected_types = (str, float, int)
|
||||
|
||||
for expected_type in expected_types:
|
||||
assert expected_type in args
|
||||
|
||||
def test_anyof_with_null(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"anyOf": [
|
||||
{"type": "string"},
|
||||
{"type": "number"},
|
||||
{"type": "null"}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldNullable")
|
||||
|
||||
assert get_origin(result_type) is Union
|
||||
|
||||
args = get_args(result_type)
|
||||
assert type(None) in args
|
||||
assert str in args
|
||||
assert float in args
|
||||
|
||||
def test_anyof_single_type(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"anyOf": [
|
||||
{"type": "string"}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldSingle")
|
||||
|
||||
assert result_type is str
|
||||
|
||||
def test_oneof_multiple_types(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"oneOf": [
|
||||
{"type": "string"},
|
||||
{"type": "boolean"}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldOneOf")
|
||||
|
||||
assert get_origin(result_type) is Union
|
||||
|
||||
args = get_args(result_type)
|
||||
expected_types = (str, bool)
|
||||
|
||||
for expected_type in expected_types:
|
||||
assert expected_type in args
|
||||
|
||||
def test_oneof_single_type(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"oneOf": [
|
||||
{"type": "integer"}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldOneOfSingle")
|
||||
|
||||
assert result_type is int
|
||||
|
||||
def test_basic_types(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_cases = [
|
||||
({"type": "string"}, str),
|
||||
({"type": "integer"}, int),
|
||||
({"type": "number"}, float),
|
||||
({"type": "boolean"}, bool),
|
||||
({"type": "array", "items": {"type": "string"}}, list),
|
||||
]
|
||||
|
||||
for schema, expected_type in test_cases:
|
||||
result_type = tool._process_schema_type(schema, "TestField")
|
||||
if schema["type"] == "array":
|
||||
assert get_origin(result_type) is list
|
||||
else:
|
||||
assert result_type is expected_type
|
||||
|
||||
def test_enum_handling(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"type": "string",
|
||||
"enum": ["option1", "option2", "option3"]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldEnum")
|
||||
|
||||
assert result_type is str
|
||||
|
||||
def test_nested_anyof(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"anyOf": [
|
||||
{"type": "string"},
|
||||
{
|
||||
"anyOf": [
|
||||
{"type": "integer"},
|
||||
{"type": "boolean"}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldNested")
|
||||
|
||||
assert get_origin(result_type) is Union
|
||||
args = get_args(result_type)
|
||||
|
||||
assert str in args
|
||||
|
||||
if len(args) == 3:
|
||||
assert int in args
|
||||
assert bool in args
|
||||
else:
|
||||
nested_union = next(arg for arg in args if get_origin(arg) is Union)
|
||||
nested_args = get_args(nested_union)
|
||||
assert int in nested_args
|
||||
assert bool in nested_args
|
||||
|
||||
def test_allof_same_types(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"allOf": [
|
||||
{"type": "string"},
|
||||
{"type": "string", "maxLength": 100}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldAllOfSame")
|
||||
|
||||
assert result_type is str
|
||||
|
||||
def test_allof_object_merge(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"allOf": [
|
||||
{
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {"type": "string"},
|
||||
"age": {"type": "integer"}
|
||||
},
|
||||
"required": ["name"]
|
||||
},
|
||||
{
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"email": {"type": "string"},
|
||||
"age": {"type": "integer"}
|
||||
},
|
||||
"required": ["email"]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldAllOfMerged")
|
||||
|
||||
# Should create a merged model with all properties
|
||||
# The implementation might fall back to dict if model creation fails
|
||||
# Let's just verify it's not a basic scalar type
|
||||
assert result_type is not str
|
||||
assert result_type is not int
|
||||
assert result_type is not bool
|
||||
# It could be dict (fallback) or a proper model class
|
||||
assert result_type in (dict, type) or hasattr(result_type, '__name__')
|
||||
|
||||
def test_allof_single_schema(self):
|
||||
"""Test that allOf with single schema works correctly."""
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"allOf": [
|
||||
{"type": "boolean"}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldAllOfSingle")
|
||||
|
||||
# Should be just bool
|
||||
assert result_type is bool
|
||||
|
||||
def test_allof_mixed_types(self):
|
||||
tool = self.create_test_tool()
|
||||
|
||||
test_schema = {
|
||||
"allOf": [
|
||||
{"type": "string"},
|
||||
{"type": "integer"}
|
||||
]
|
||||
}
|
||||
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldAllOfMixed")
|
||||
|
||||
assert result_type is str
|
||||
|
||||
class TestCrewAIPlatformActionToolVerify:
|
||||
"""Test suite for SSL verification behavior based on CREWAI_FACTORY environment variable"""
|
||||
|
||||
|
||||
@@ -224,6 +224,43 @@ class TestCrewaiPlatformToolBuilder(unittest.TestCase):
|
||||
_, kwargs = mock_get.call_args
|
||||
assert kwargs["params"]["apps"] == ""
|
||||
|
||||
def test_detailed_description_generation(self):
|
||||
builder = CrewaiPlatformToolBuilder(apps=["test"])
|
||||
|
||||
complex_schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"simple_string": {"type": "string", "description": "A simple string"},
|
||||
"nested_object": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"inner_prop": {
|
||||
"type": "integer",
|
||||
"description": "Inner property",
|
||||
}
|
||||
},
|
||||
"description": "Nested object",
|
||||
},
|
||||
"array_prop": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"},
|
||||
"description": "Array of strings",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
descriptions = builder._generate_detailed_description(complex_schema)
|
||||
|
||||
assert isinstance(descriptions, list)
|
||||
assert len(descriptions) > 0
|
||||
|
||||
description_text = "\n".join(descriptions)
|
||||
assert "simple_string" in description_text
|
||||
assert "nested_object" in description_text
|
||||
assert "array_prop" in description_text
|
||||
|
||||
|
||||
|
||||
class TestCrewaiPlatformToolBuilderVerify(unittest.TestCase):
|
||||
"""Test suite for SSL verification behavior in CrewaiPlatformToolBuilder"""
|
||||
|
||||
|
||||
@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
|
||||
|
||||
[project.optional-dependencies]
|
||||
tools = [
|
||||
"crewai-tools==1.9.2",
|
||||
"crewai-tools==1.9.0",
|
||||
]
|
||||
embeddings = [
|
||||
"tiktoken~=0.8.0"
|
||||
|
||||
@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
|
||||
|
||||
_suppress_pydantic_deprecation_warnings()
|
||||
|
||||
__version__ = "1.9.2"
|
||||
__version__ = "1.9.0"
|
||||
_telemetry_submitted = False
|
||||
|
||||
|
||||
|
||||
@@ -37,8 +37,7 @@ class CrewAgentExecutorMixin:
|
||||
self.crew
|
||||
and self.agent
|
||||
and self.task
|
||||
and f"Action: {sanitize_tool_name('Delegate work to coworker')}"
|
||||
not in output.text
|
||||
and f"Action: {sanitize_tool_name('Delegate work to coworker')}" not in output.text
|
||||
):
|
||||
try:
|
||||
if (
|
||||
@@ -133,11 +132,10 @@ class CrewAgentExecutorMixin:
|
||||
and self.crew._long_term_memory
|
||||
and self.crew._entity_memory is None
|
||||
):
|
||||
if self.agent and self.agent.verbose:
|
||||
self._printer.print(
|
||||
content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.",
|
||||
color="bold_yellow",
|
||||
)
|
||||
self._printer.print(
|
||||
content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.",
|
||||
color="bold_yellow",
|
||||
)
|
||||
|
||||
def _ask_human_input(self, final_answer: str) -> str:
|
||||
"""Prompt human input with mode-appropriate messaging.
|
||||
|
||||
@@ -28,11 +28,6 @@ from crewai.hooks.llm_hooks import (
|
||||
get_after_llm_call_hooks,
|
||||
get_before_llm_call_hooks,
|
||||
)
|
||||
from crewai.hooks.tool_hooks import (
|
||||
ToolCallHookContext,
|
||||
get_after_tool_call_hooks,
|
||||
get_before_tool_call_hooks,
|
||||
)
|
||||
from crewai.utilities.agent_utils import (
|
||||
aget_llm_response,
|
||||
convert_tools_to_openai_schema,
|
||||
@@ -206,14 +201,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
try:
|
||||
formatted_answer = self._invoke_loop()
|
||||
except AssertionError:
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content="Agent failed to reach a final answer. This is likely a bug - please report it.",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content="Agent failed to reach a final answer. This is likely a bug - please report it.",
|
||||
color="red",
|
||||
)
|
||||
raise
|
||||
except Exception as e:
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise
|
||||
|
||||
if self.ask_for_human_input:
|
||||
@@ -328,7 +322,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
messages=self.messages,
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
break
|
||||
|
||||
@@ -343,41 +336,22 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
from_agent=self.agent,
|
||||
response_model=self.response_model,
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
# breakpoint()
|
||||
if self.response_model is not None:
|
||||
try:
|
||||
if isinstance(answer, BaseModel):
|
||||
output_json = answer.model_dump_json()
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=output_json,
|
||||
)
|
||||
else:
|
||||
self.response_model.model_validate_json(answer)
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=answer,
|
||||
)
|
||||
except ValidationError:
|
||||
# If validation fails, convert BaseModel to JSON string for parsing
|
||||
answer_str = (
|
||||
answer.model_dump_json()
|
||||
if isinstance(answer, BaseModel)
|
||||
else str(answer)
|
||||
self.response_model.model_validate_json(answer)
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=answer,
|
||||
)
|
||||
except ValidationError:
|
||||
formatted_answer = process_llm_response(
|
||||
answer_str, self.use_stop_words
|
||||
answer, self.use_stop_words
|
||||
) # type: ignore[assignment]
|
||||
else:
|
||||
# When no response_model, answer should be a string
|
||||
answer_str = str(answer) if not isinstance(answer, str) else answer
|
||||
formatted_answer = process_llm_response(
|
||||
answer_str, self.use_stop_words
|
||||
) # type: ignore[assignment]
|
||||
formatted_answer = process_llm_response(answer, self.use_stop_words) # type: ignore[assignment]
|
||||
|
||||
if isinstance(formatted_answer, AgentAction):
|
||||
# Extract agent fingerprint if available
|
||||
@@ -420,7 +394,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
iterations=self.iterations,
|
||||
log_error_after=self.log_error_after,
|
||||
printer=self._printer,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
@@ -435,10 +408,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
i18n=self._i18n,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
continue
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise e
|
||||
finally:
|
||||
self.iterations += 1
|
||||
@@ -484,7 +456,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
messages=self.messages,
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
@@ -506,7 +477,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
from_agent=self.agent,
|
||||
response_model=self.response_model,
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
# Check if the response is a list of tool calls
|
||||
@@ -538,18 +508,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
if isinstance(answer, BaseModel):
|
||||
output_json = answer.model_dump_json()
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=output_json,
|
||||
)
|
||||
self._invoke_step_callback(formatted_answer)
|
||||
self._append_message(output_json)
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
# Unexpected response type, treat as final answer
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
@@ -572,10 +530,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
i18n=self._i18n,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
continue
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise e
|
||||
finally:
|
||||
self.iterations += 1
|
||||
@@ -597,23 +554,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
from_agent=self.agent,
|
||||
response_model=self.response_model,
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
if isinstance(answer, BaseModel):
|
||||
output_json = answer.model_dump_json()
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=output_json,
|
||||
)
|
||||
else:
|
||||
answer_str = answer if isinstance(answer, str) else str(answer)
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer_str,
|
||||
text=answer_str,
|
||||
)
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=str(answer),
|
||||
text=str(answer),
|
||||
)
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
@@ -802,42 +749,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
|
||||
track_delegation_if_needed(func_name, args_dict, self.task)
|
||||
|
||||
# Find the structured tool for hook context
|
||||
structured_tool: CrewStructuredTool | None = None
|
||||
for structured in self.tools or []:
|
||||
if sanitize_tool_name(structured.name) == func_name:
|
||||
structured_tool = structured
|
||||
break
|
||||
|
||||
# Execute before_tool_call hooks
|
||||
hook_blocked = False
|
||||
before_hook_context = ToolCallHookContext(
|
||||
tool_name=func_name,
|
||||
tool_input=args_dict,
|
||||
tool=structured_tool, # type: ignore[arg-type]
|
||||
agent=self.agent,
|
||||
task=self.task,
|
||||
crew=self.crew,
|
||||
)
|
||||
before_hooks = get_before_tool_call_hooks()
|
||||
try:
|
||||
for hook in before_hooks:
|
||||
hook_result = hook(before_hook_context)
|
||||
if hook_result is False:
|
||||
hook_blocked = True
|
||||
break
|
||||
except Exception as hook_error:
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content=f"Error in before_tool_call hook: {hook_error}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
# If hook blocked execution, set result and skip tool execution
|
||||
if hook_blocked:
|
||||
result = f"Tool execution blocked by hook. Tool: {func_name}"
|
||||
# Execute the tool (only if not cached, not at max usage, and not blocked by hook)
|
||||
elif not from_cache and not max_usage_reached:
|
||||
# Execute the tool (only if not cached and not at max usage)
|
||||
if not from_cache and not max_usage_reached:
|
||||
result = "Tool not found"
|
||||
if func_name in available_functions:
|
||||
try:
|
||||
@@ -885,29 +798,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
# Return error message when max usage limit is reached
|
||||
result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
|
||||
|
||||
after_hook_context = ToolCallHookContext(
|
||||
tool_name=func_name,
|
||||
tool_input=args_dict,
|
||||
tool=structured_tool, # type: ignore[arg-type]
|
||||
agent=self.agent,
|
||||
task=self.task,
|
||||
crew=self.crew,
|
||||
tool_result=result,
|
||||
)
|
||||
after_hooks = get_after_tool_call_hooks()
|
||||
try:
|
||||
for after_hook in after_hooks:
|
||||
after_hook_result = after_hook(after_hook_context)
|
||||
if after_hook_result is not None:
|
||||
result = after_hook_result
|
||||
after_hook_context.tool_result = result
|
||||
except Exception as hook_error:
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content=f"Error in after_tool_call hook: {hook_error}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
# Emit tool usage finished event
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
@@ -992,14 +882,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
try:
|
||||
formatted_answer = await self._ainvoke_loop()
|
||||
except AssertionError:
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content="Agent failed to reach a final answer. This is likely a bug - please report it.",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content="Agent failed to reach a final answer. This is likely a bug - please report it.",
|
||||
color="red",
|
||||
)
|
||||
raise
|
||||
except Exception as e:
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise
|
||||
|
||||
if self.ask_for_human_input:
|
||||
@@ -1050,7 +939,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
messages=self.messages,
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
break
|
||||
|
||||
@@ -1065,41 +953,22 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
from_agent=self.agent,
|
||||
response_model=self.response_model,
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
if self.response_model is not None:
|
||||
try:
|
||||
if isinstance(answer, BaseModel):
|
||||
output_json = answer.model_dump_json()
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=output_json,
|
||||
)
|
||||
else:
|
||||
self.response_model.model_validate_json(answer)
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=answer,
|
||||
)
|
||||
except ValidationError:
|
||||
# If validation fails, convert BaseModel to JSON string for parsing
|
||||
answer_str = (
|
||||
answer.model_dump_json()
|
||||
if isinstance(answer, BaseModel)
|
||||
else str(answer)
|
||||
self.response_model.model_validate_json(answer)
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=answer,
|
||||
)
|
||||
except ValidationError:
|
||||
formatted_answer = process_llm_response(
|
||||
answer_str, self.use_stop_words
|
||||
answer, self.use_stop_words
|
||||
) # type: ignore[assignment]
|
||||
else:
|
||||
# When no response_model, answer should be a string
|
||||
answer_str = str(answer) if not isinstance(answer, str) else answer
|
||||
formatted_answer = process_llm_response(
|
||||
answer_str, self.use_stop_words
|
||||
) # type: ignore[assignment]
|
||||
formatted_answer = process_llm_response(answer, self.use_stop_words) # type: ignore[assignment]
|
||||
|
||||
if isinstance(formatted_answer, AgentAction):
|
||||
fingerprint_context = {}
|
||||
@@ -1141,7 +1010,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
iterations=self.iterations,
|
||||
log_error_after=self.log_error_after,
|
||||
printer=self._printer,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
@@ -1155,10 +1023,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
i18n=self._i18n,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
continue
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise e
|
||||
finally:
|
||||
self.iterations += 1
|
||||
@@ -1198,7 +1065,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
messages=self.messages,
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
@@ -1220,7 +1086,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
from_agent=self.agent,
|
||||
response_model=self.response_model,
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
# Check if the response is a list of tool calls
|
||||
if (
|
||||
@@ -1251,18 +1116,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
if isinstance(answer, BaseModel):
|
||||
output_json = answer.model_dump_json()
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=output_json,
|
||||
)
|
||||
self._invoke_step_callback(formatted_answer)
|
||||
self._append_message(output_json)
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
# Unexpected response type, treat as final answer
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
@@ -1285,10 +1138,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
i18n=self._i18n,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
continue
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise e
|
||||
finally:
|
||||
self.iterations += 1
|
||||
@@ -1310,23 +1162,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
from_agent=self.agent,
|
||||
response_model=self.response_model,
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
if isinstance(answer, BaseModel):
|
||||
output_json = answer.model_dump_json()
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=output_json,
|
||||
)
|
||||
else:
|
||||
answer_str = answer if isinstance(answer, str) else str(answer)
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer_str,
|
||||
text=answer_str,
|
||||
)
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=str(answer),
|
||||
text=str(answer),
|
||||
)
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
@@ -1437,11 +1279,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
)
|
||||
|
||||
if train_iteration is None or not isinstance(train_iteration, int):
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content="Invalid or missing train iteration. Cannot save training data.",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content="Invalid or missing train iteration. Cannot save training data.",
|
||||
color="red",
|
||||
)
|
||||
return
|
||||
|
||||
training_handler = CrewTrainingHandler(TRAINING_DATA_FILE)
|
||||
@@ -1461,14 +1302,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
if train_iteration in agent_training_data:
|
||||
agent_training_data[train_iteration]["improved_output"] = result.output
|
||||
else:
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content=(
|
||||
f"No existing training data for agent {agent_id} and iteration "
|
||||
f"{train_iteration}. Cannot save improved output."
|
||||
),
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content=(
|
||||
f"No existing training data for agent {agent_id} and iteration "
|
||||
f"{train_iteration}. Cannot save improved output."
|
||||
),
|
||||
color="red",
|
||||
)
|
||||
return
|
||||
|
||||
# Update the training data and save
|
||||
@@ -1499,12 +1339,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
Returns:
|
||||
Final answer after feedback.
|
||||
"""
|
||||
output_str = (
|
||||
formatted_answer.output
|
||||
if isinstance(formatted_answer.output, str)
|
||||
else formatted_answer.output.model_dump_json()
|
||||
)
|
||||
human_feedback = self._ask_human_input(output_str)
|
||||
human_feedback = self._ask_human_input(formatted_answer.output)
|
||||
|
||||
if self._is_training_mode():
|
||||
return self._handle_training_feedback(formatted_answer, human_feedback)
|
||||
@@ -1563,12 +1398,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
self.ask_for_human_input = False
|
||||
else:
|
||||
answer = self._process_feedback_iteration(feedback)
|
||||
output_str = (
|
||||
answer.output
|
||||
if isinstance(answer.output, str)
|
||||
else answer.output.model_dump_json()
|
||||
)
|
||||
feedback = self._ask_human_input(output_str)
|
||||
feedback = self._ask_human_input(answer.output)
|
||||
|
||||
return answer
|
||||
|
||||
|
||||
@@ -8,7 +8,6 @@ AgentAction or AgentFinish objects.
|
||||
from dataclasses import dataclass
|
||||
|
||||
from json_repair import repair_json # type: ignore[import-untyped]
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.agents.constants import (
|
||||
ACTION_INPUT_ONLY_REGEX,
|
||||
@@ -41,7 +40,7 @@ class AgentFinish:
|
||||
"""Represents the final answer from an agent."""
|
||||
|
||||
thought: str
|
||||
output: str | BaseModel
|
||||
output: str
|
||||
text: str
|
||||
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.9.2"
|
||||
"crewai[tools]==1.9.0"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.9.2"
|
||||
"crewai[tools]==1.9.0"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -193,13 +193,7 @@ class Crew(FlowTrackable, BaseModel):
|
||||
tasks: list[Task] = Field(default_factory=list)
|
||||
agents: list[BaseAgent] = Field(default_factory=list)
|
||||
process: Process = Field(default=Process.sequential)
|
||||
verbose: bool | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Whether to enable verbose logging output. True=always enable, "
|
||||
"False=always disable, None=check CREWAI_VERBOSE env var (defaults to True if not set)."
|
||||
),
|
||||
)
|
||||
verbose: bool = Field(default=False)
|
||||
memory: bool = Field(
|
||||
default=False,
|
||||
description="If crew should use memory to store memories of it's execution",
|
||||
@@ -356,8 +350,6 @@ class Crew(FlowTrackable, BaseModel):
|
||||
@model_validator(mode="after")
|
||||
def set_private_attrs(self) -> Crew:
|
||||
"""set private attributes."""
|
||||
from crewai.utilities.logger_utils import should_enable_verbose
|
||||
|
||||
self._cache_handler = CacheHandler()
|
||||
event_listener = EventListener()
|
||||
|
||||
@@ -365,11 +357,6 @@ class Crew(FlowTrackable, BaseModel):
|
||||
tracing_enabled = should_enable_tracing(override=self.tracing)
|
||||
set_tracing_enabled(tracing_enabled)
|
||||
|
||||
# Determine verbose setting (respects CREWAI_VERBOSE env var)
|
||||
# Update self.verbose to the resolved boolean value so it can be used
|
||||
# consistently throughout the class (e.g., in _create_manager_agent)
|
||||
self.verbose = should_enable_verbose(override=self.verbose)
|
||||
|
||||
# Always setup trace listener - actual execution control is via contextvar
|
||||
trace_listener = TraceCollectionListener()
|
||||
trace_listener.setup_listeners(crewai_event_bus)
|
||||
|
||||
@@ -119,11 +119,14 @@ To enable tracing, do any one of these:
|
||||
self, content: Text, title: str, style: str = "blue", is_flow: bool = False
|
||||
) -> None:
|
||||
"""Print a panel with consistent formatting if verbose is enabled."""
|
||||
if not self.verbose:
|
||||
return
|
||||
panel = self.create_panel(content, title, style)
|
||||
self.print(panel)
|
||||
self.print()
|
||||
if is_flow:
|
||||
self.print(panel)
|
||||
self.print()
|
||||
else:
|
||||
if self.verbose:
|
||||
self.print(panel)
|
||||
self.print()
|
||||
|
||||
def handle_crew_status(
|
||||
self,
|
||||
|
||||
@@ -36,12 +36,6 @@ from crewai.hooks.llm_hooks import (
|
||||
get_after_llm_call_hooks,
|
||||
get_before_llm_call_hooks,
|
||||
)
|
||||
from crewai.hooks.tool_hooks import (
|
||||
ToolCallHookContext,
|
||||
get_after_tool_call_hooks,
|
||||
get_before_tool_call_hooks,
|
||||
)
|
||||
from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
|
||||
from crewai.utilities.agent_utils import (
|
||||
convert_tools_to_openai_schema,
|
||||
enforce_rpm_limit,
|
||||
@@ -191,8 +185,8 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
|
||||
self._instance_id = str(uuid4())[:8]
|
||||
|
||||
self.before_llm_call_hooks: list[BeforeLLMCallHookType] = []
|
||||
self.after_llm_call_hooks: list[AfterLLMCallHookType] = []
|
||||
self.before_llm_call_hooks: list[Callable] = []
|
||||
self.after_llm_call_hooks: list[Callable] = []
|
||||
self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
|
||||
self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
|
||||
|
||||
@@ -305,21 +299,11 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
"""Compatibility property for mixin - returns state messages."""
|
||||
return self._state.messages
|
||||
|
||||
@messages.setter
|
||||
def messages(self, value: list[LLMMessage]) -> None:
|
||||
"""Set state messages."""
|
||||
self._state.messages = value
|
||||
|
||||
@property
|
||||
def iterations(self) -> int:
|
||||
"""Compatibility property for mixin - returns state iterations."""
|
||||
return self._state.iterations
|
||||
|
||||
@iterations.setter
|
||||
def iterations(self, value: int) -> None:
|
||||
"""Set state iterations."""
|
||||
self._state.iterations = value
|
||||
|
||||
@start()
|
||||
def initialize_reasoning(self) -> Literal["initialized"]:
|
||||
"""Initialize the reasoning flow and emit agent start logs."""
|
||||
@@ -341,7 +325,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
messages=list(self.state.messages),
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
self.state.current_answer = formatted_answer
|
||||
@@ -367,7 +350,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
from_agent=self.agent,
|
||||
response_model=None,
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
# Parse the LLM response
|
||||
@@ -403,7 +385,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
return "context_error"
|
||||
if e.__class__.__module__.startswith("litellm"):
|
||||
raise e
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise
|
||||
|
||||
@listen("continue_reasoning_native")
|
||||
@@ -438,7 +420,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
from_agent=self.agent,
|
||||
response_model=None,
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
# Check if the response is a list of tool calls
|
||||
@@ -477,7 +458,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
return "context_error"
|
||||
if e.__class__.__module__.startswith("litellm"):
|
||||
raise e
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise
|
||||
|
||||
@router(call_llm_and_parse)
|
||||
@@ -596,12 +577,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
"content": None,
|
||||
"tool_calls": tool_calls_to_report,
|
||||
}
|
||||
if all(
|
||||
type(tc).__qualname__ == "Part" for tc in self.state.pending_tool_calls
|
||||
):
|
||||
assistant_message["raw_tool_call_parts"] = list(
|
||||
self.state.pending_tool_calls
|
||||
)
|
||||
self.state.messages.append(assistant_message)
|
||||
|
||||
# Now execute each tool
|
||||
@@ -636,12 +611,14 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
|
||||
# Check if tool has reached max usage count
|
||||
max_usage_reached = False
|
||||
if (
|
||||
original_tool
|
||||
and original_tool.max_usage_count is not None
|
||||
and original_tool.current_usage_count >= original_tool.max_usage_count
|
||||
):
|
||||
max_usage_reached = True
|
||||
if original_tool:
|
||||
if (
|
||||
hasattr(original_tool, "max_usage_count")
|
||||
and original_tool.max_usage_count is not None
|
||||
and original_tool.current_usage_count
|
||||
>= original_tool.max_usage_count
|
||||
):
|
||||
max_usage_reached = True
|
||||
|
||||
# Check cache before executing
|
||||
from_cache = False
|
||||
@@ -673,38 +650,8 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
|
||||
track_delegation_if_needed(func_name, args_dict, self.task)
|
||||
|
||||
structured_tool: CrewStructuredTool | None = None
|
||||
for structured in self.tools or []:
|
||||
if sanitize_tool_name(structured.name) == func_name:
|
||||
structured_tool = structured
|
||||
break
|
||||
|
||||
hook_blocked = False
|
||||
before_hook_context = ToolCallHookContext(
|
||||
tool_name=func_name,
|
||||
tool_input=args_dict,
|
||||
tool=structured_tool, # type: ignore[arg-type]
|
||||
agent=self.agent,
|
||||
task=self.task,
|
||||
crew=self.crew,
|
||||
)
|
||||
before_hooks = get_before_tool_call_hooks()
|
||||
try:
|
||||
for hook in before_hooks:
|
||||
hook_result = hook(before_hook_context)
|
||||
if hook_result is False:
|
||||
hook_blocked = True
|
||||
break
|
||||
except Exception as hook_error:
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content=f"Error in before_tool_call hook: {hook_error}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
if hook_blocked:
|
||||
result = f"Tool execution blocked by hook. Tool: {func_name}"
|
||||
elif not from_cache and not max_usage_reached:
|
||||
# Execute the tool (only if not cached and not at max usage)
|
||||
if not from_cache and not max_usage_reached:
|
||||
result = "Tool not found"
|
||||
if func_name in self._available_functions:
|
||||
try:
|
||||
@@ -714,7 +661,11 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
# Add to cache after successful execution (before string conversion)
|
||||
if self.tools_handler and self.tools_handler.cache:
|
||||
should_cache = True
|
||||
if original_tool:
|
||||
if (
|
||||
original_tool
|
||||
and hasattr(original_tool, "cache_function")
|
||||
and original_tool.cache_function
|
||||
):
|
||||
should_cache = original_tool.cache_function(
|
||||
args_dict, raw_result
|
||||
)
|
||||
@@ -745,34 +696,10 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
error=e,
|
||||
),
|
||||
)
|
||||
elif max_usage_reached and original_tool:
|
||||
elif max_usage_reached:
|
||||
# Return error message when max usage limit is reached
|
||||
result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
|
||||
|
||||
# Execute after_tool_call hooks (even if blocked, to allow logging/monitoring)
|
||||
after_hook_context = ToolCallHookContext(
|
||||
tool_name=func_name,
|
||||
tool_input=args_dict,
|
||||
tool=structured_tool, # type: ignore[arg-type]
|
||||
agent=self.agent,
|
||||
task=self.task,
|
||||
crew=self.crew,
|
||||
tool_result=result,
|
||||
)
|
||||
after_hooks = get_after_tool_call_hooks()
|
||||
try:
|
||||
for after_hook in after_hooks:
|
||||
after_hook_result = after_hook(after_hook_context)
|
||||
if after_hook_result is not None:
|
||||
result = after_hook_result
|
||||
after_hook_context.tool_result = result
|
||||
except Exception as hook_error:
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content=f"Error in after_tool_call hook: {hook_error}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
# Emit tool usage finished event
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
@@ -819,6 +746,15 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
self.state.is_finished = True
|
||||
return "tool_result_is_final"
|
||||
|
||||
# Add reflection prompt once after all tools in the batch
|
||||
reasoning_prompt = self._i18n.slice("post_tool_reasoning")
|
||||
|
||||
reasoning_message: LLMMessage = {
|
||||
"role": "user",
|
||||
"content": reasoning_prompt,
|
||||
}
|
||||
self.state.messages.append(reasoning_message)
|
||||
|
||||
return "native_tool_completed"
|
||||
|
||||
def _extract_tool_name(self, tool_call: Any) -> str:
|
||||
@@ -897,17 +833,12 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
@listen("parser_error")
|
||||
def recover_from_parser_error(self) -> Literal["initialized"]:
|
||||
"""Recover from output parser errors and retry."""
|
||||
if not self._last_parser_error:
|
||||
self.state.iterations += 1
|
||||
return "initialized"
|
||||
|
||||
formatted_answer = handle_output_parser_exception(
|
||||
e=self._last_parser_error,
|
||||
messages=list(self.state.messages),
|
||||
iterations=self.state.iterations,
|
||||
log_error_after=self.log_error_after,
|
||||
printer=self._printer,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
if formatted_answer:
|
||||
@@ -927,7 +858,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
llm=self.llm,
|
||||
callbacks=self.callbacks,
|
||||
i18n=self._i18n,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
self.state.iterations += 1
|
||||
@@ -1019,7 +949,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
self._console.print(fail_text)
|
||||
raise
|
||||
except Exception as e:
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise
|
||||
finally:
|
||||
self._is_executing = False
|
||||
@@ -1104,7 +1034,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
self._console.print(fail_text)
|
||||
raise
|
||||
except Exception as e:
|
||||
handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise
|
||||
finally:
|
||||
self._is_executing = False
|
||||
|
||||
@@ -559,7 +559,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
name: str | None = None
|
||||
tracing: bool | None = None
|
||||
stream: bool = False
|
||||
verbose: bool = True
|
||||
|
||||
def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]:
|
||||
class _FlowGeneric(cls): # type: ignore
|
||||
@@ -573,7 +572,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
persistence: FlowPersistence | None = None,
|
||||
tracing: bool | None = None,
|
||||
suppress_flow_events: bool = False,
|
||||
verbose: bool | None = None,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Initialize a new Flow instance.
|
||||
@@ -582,12 +580,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
persistence: Optional persistence backend for storing flow states
|
||||
tracing: Whether to enable tracing. True=always enable, False=always disable, None=check environment/user settings
|
||||
suppress_flow_events: Whether to suppress flow event emissions (internal use)
|
||||
verbose: Whether to enable verbose logging output. True=always enable, False=always disable,
|
||||
None=check CREWAI_VERBOSE environment variable (defaults to True if not set).
|
||||
**kwargs: Additional state values to initialize or override
|
||||
"""
|
||||
from crewai.events.event_listener import EventListener
|
||||
|
||||
# Initialize basic instance attributes
|
||||
self._methods: dict[FlowMethodName, FlowMethod[Any, Any]] = {}
|
||||
self._method_execution_counts: dict[FlowMethodName, int] = {}
|
||||
@@ -611,14 +605,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
self._pending_feedback_context: PendingFeedbackContext | None = None
|
||||
self.suppress_flow_events: bool = suppress_flow_events
|
||||
|
||||
# Set verbose and configure event listener
|
||||
from crewai.utilities.logger_utils import should_enable_verbose
|
||||
|
||||
self.verbose = should_enable_verbose(override=verbose)
|
||||
event_listener = EventListener()
|
||||
event_listener.verbose = self.verbose
|
||||
event_listener.formatter.verbose = self.verbose
|
||||
|
||||
# Initialize state with initial values
|
||||
self._state = self._create_initial_state()
|
||||
self.tracing = tracing
|
||||
|
||||
@@ -118,20 +118,17 @@ class PersistenceDecorator:
|
||||
)
|
||||
except Exception as e:
|
||||
error_msg = LOG_MESSAGES["save_error"].format(method_name, str(e))
|
||||
if verbose:
|
||||
cls._printer.print(error_msg, color="red")
|
||||
cls._printer.print(error_msg, color="red")
|
||||
logger.error(error_msg)
|
||||
raise RuntimeError(f"State persistence failed: {e!s}") from e
|
||||
except AttributeError as e:
|
||||
error_msg = LOG_MESSAGES["state_missing"]
|
||||
if verbose:
|
||||
cls._printer.print(error_msg, color="red")
|
||||
cls._printer.print(error_msg, color="red")
|
||||
logger.error(error_msg)
|
||||
raise ValueError(error_msg) from e
|
||||
except (TypeError, ValueError) as e:
|
||||
error_msg = LOG_MESSAGES["id_missing"]
|
||||
if verbose:
|
||||
cls._printer.print(error_msg, color="red")
|
||||
cls._printer.print(error_msg, color="red")
|
||||
logger.error(error_msg)
|
||||
raise ValueError(error_msg) from e
|
||||
|
||||
|
||||
@@ -151,9 +151,7 @@ def _unwrap_function(function: Any) -> Any:
|
||||
return function
|
||||
|
||||
|
||||
def get_possible_return_constants(
|
||||
function: Any, verbose: bool = True
|
||||
) -> list[str] | None:
|
||||
def get_possible_return_constants(function: Any) -> list[str] | None:
|
||||
"""Extract possible string return values from a function using AST parsing.
|
||||
|
||||
This function analyzes the source code of a router method to identify
|
||||
@@ -180,11 +178,10 @@ def get_possible_return_constants(
|
||||
# Can't get source code
|
||||
return None
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
_printer.print(
|
||||
f"Error retrieving source code for function {function.__name__}: {e}",
|
||||
color="red",
|
||||
)
|
||||
_printer.print(
|
||||
f"Error retrieving source code for function {function.__name__}: {e}",
|
||||
color="red",
|
||||
)
|
||||
return None
|
||||
|
||||
try:
|
||||
@@ -193,28 +190,25 @@ def get_possible_return_constants(
|
||||
# Parse the source code into an AST
|
||||
code_ast = ast.parse(source)
|
||||
except IndentationError as e:
|
||||
if verbose:
|
||||
_printer.print(
|
||||
f"IndentationError while parsing source code of {function.__name__}: {e}",
|
||||
color="red",
|
||||
)
|
||||
_printer.print(f"Source code:\n{source}", color="yellow")
|
||||
_printer.print(
|
||||
f"IndentationError while parsing source code of {function.__name__}: {e}",
|
||||
color="red",
|
||||
)
|
||||
_printer.print(f"Source code:\n{source}", color="yellow")
|
||||
return None
|
||||
except SyntaxError as e:
|
||||
if verbose:
|
||||
_printer.print(
|
||||
f"SyntaxError while parsing source code of {function.__name__}: {e}",
|
||||
color="red",
|
||||
)
|
||||
_printer.print(f"Source code:\n{source}", color="yellow")
|
||||
_printer.print(
|
||||
f"SyntaxError while parsing source code of {function.__name__}: {e}",
|
||||
color="red",
|
||||
)
|
||||
_printer.print(f"Source code:\n{source}", color="yellow")
|
||||
return None
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
_printer.print(
|
||||
f"Unexpected error while parsing source code of {function.__name__}: {e}",
|
||||
color="red",
|
||||
)
|
||||
_printer.print(f"Source code:\n{source}", color="yellow")
|
||||
_printer.print(
|
||||
f"Unexpected error while parsing source code of {function.__name__}: {e}",
|
||||
color="red",
|
||||
)
|
||||
_printer.print(f"Source code:\n{source}", color="yellow")
|
||||
return None
|
||||
|
||||
return_values: set[str] = set()
|
||||
@@ -394,17 +388,15 @@ def get_possible_return_constants(
|
||||
|
||||
StateAttributeVisitor().visit(class_ast)
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
_printer.print(
|
||||
f"Could not analyze class context for {function.__name__}: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
_printer.print(
|
||||
f"Could not analyze class context for {function.__name__}: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
_printer.print(
|
||||
f"Could not introspect class for {function.__name__}: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
_printer.print(
|
||||
f"Could not introspect class for {function.__name__}: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
VariableAssignmentVisitor().visit(code_ast)
|
||||
ReturnVisitor().visit(code_ast)
|
||||
|
||||
@@ -9,7 +9,6 @@ from crewai.utilities.printer import Printer
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.agents.crew_agent_executor import CrewAgentExecutor
|
||||
from crewai.experimental.agent_executor import AgentExecutor
|
||||
from crewai.lite_agent import LiteAgent
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
from crewai.utilities.types import LLMMessage
|
||||
@@ -42,7 +41,7 @@ class LLMCallHookContext:
|
||||
Can be modified by returning a new string from after_llm_call hook.
|
||||
"""
|
||||
|
||||
executor: CrewAgentExecutor | AgentExecutor | LiteAgent | None
|
||||
executor: CrewAgentExecutor | LiteAgent | None
|
||||
messages: list[LLMMessage]
|
||||
agent: Any
|
||||
task: Any
|
||||
@@ -53,7 +52,7 @@ class LLMCallHookContext:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
executor: CrewAgentExecutor | AgentExecutor | LiteAgent | None = None,
|
||||
executor: CrewAgentExecutor | LiteAgent | None = None,
|
||||
response: str | None = None,
|
||||
messages: list[LLMMessage] | None = None,
|
||||
llm: BaseLLM | str | Any | None = None, # TODO: look into
|
||||
|
||||
@@ -72,13 +72,13 @@ from crewai.utilities.agent_utils import (
|
||||
from crewai.utilities.converter import (
|
||||
Converter,
|
||||
ConverterError,
|
||||
generate_model_description,
|
||||
)
|
||||
from crewai.utilities.guardrail import process_guardrail
|
||||
from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
|
||||
from crewai.utilities.i18n import I18N, get_i18n
|
||||
from crewai.utilities.llm_utils import create_llm
|
||||
from crewai.utilities.printer import Printer
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.token_counter_callback import TokenCalcHandler
|
||||
from crewai.utilities.tool_utils import execute_tool_and_check_finality
|
||||
from crewai.utilities.types import LLMMessage
|
||||
@@ -344,12 +344,11 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
if self.verbose:
|
||||
self._printer.print(
|
||||
content="Agent failed to reach a final answer. This is likely a bug - please report it.",
|
||||
color="red",
|
||||
)
|
||||
handle_unknown_error(self._printer, e, verbose=self.verbose)
|
||||
self._printer.print(
|
||||
content="Agent failed to reach a final answer. This is likely a bug - please report it.",
|
||||
color="red",
|
||||
)
|
||||
handle_unknown_error(self._printer, e)
|
||||
# Emit error event
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
@@ -397,11 +396,10 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
if isinstance(result, BaseModel):
|
||||
formatted_result = result
|
||||
except ConverterError as e:
|
||||
if self.verbose:
|
||||
self._printer.print(
|
||||
content=f"Failed to parse output into response format after retries: {e.message}",
|
||||
color="yellow",
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"Failed to parse output into response format after retries: {e.message}",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
# Calculate token usage metrics
|
||||
if isinstance(self.llm, BaseLLM):
|
||||
@@ -607,7 +605,6 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
messages=self._messages,
|
||||
llm=cast(LLM, self.llm),
|
||||
callbacks=self._callbacks,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
|
||||
enforce_rpm_limit(self.request_within_rpm_limit)
|
||||
@@ -620,7 +617,6 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
printer=self._printer,
|
||||
from_agent=self,
|
||||
executor_context=self,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
@@ -650,18 +646,16 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
|
||||
self._append_message(formatted_answer.text, role="assistant")
|
||||
except OutputParserError as e: # noqa: PERF203
|
||||
if self.verbose:
|
||||
self._printer.print(
|
||||
content="Failed to parse LLM output. Retrying...",
|
||||
color="yellow",
|
||||
)
|
||||
self._printer.print(
|
||||
content="Failed to parse LLM output. Retrying...",
|
||||
color="yellow",
|
||||
)
|
||||
formatted_answer = handle_output_parser_exception(
|
||||
e=e,
|
||||
messages=self._messages,
|
||||
iterations=self._iterations,
|
||||
log_error_after=3,
|
||||
printer=self._printer,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
@@ -676,10 +670,9 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
llm=cast(LLM, self.llm),
|
||||
callbacks=self._callbacks,
|
||||
i18n=self.i18n,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
continue
|
||||
handle_unknown_error(self._printer, e, verbose=self.verbose)
|
||||
handle_unknown_error(self._printer, e)
|
||||
raise e
|
||||
|
||||
finally:
|
||||
|
||||
@@ -404,7 +404,7 @@ class BaseLLM(ABC):
|
||||
from_agent: Agent | None = None,
|
||||
tool_call: dict[str, Any] | None = None,
|
||||
call_type: LLMCallType | None = None,
|
||||
response_id: str | None = None,
|
||||
response_id: str | None = None
|
||||
) -> None:
|
||||
"""Emit stream chunk event.
|
||||
|
||||
@@ -427,7 +427,7 @@ class BaseLLM(ABC):
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
call_type=call_type,
|
||||
response_id=response_id,
|
||||
response_id=response_id
|
||||
),
|
||||
)
|
||||
|
||||
@@ -497,7 +497,7 @@ class BaseLLM(ABC):
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
return str(result) if not isinstance(result, str) else result
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"Error executing function '{function_name}': {e!s}"
|
||||
@@ -737,25 +737,22 @@ class BaseLLM(ABC):
|
||||
task=None,
|
||||
crew=None,
|
||||
)
|
||||
verbose = getattr(from_agent, "verbose", True) if from_agent else True
|
||||
printer = Printer()
|
||||
|
||||
try:
|
||||
for hook in before_hooks:
|
||||
result = hook(hook_context)
|
||||
if result is False:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="LLM call blocked by before_llm_call hook",
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content="LLM call blocked by before_llm_call hook",
|
||||
color="yellow",
|
||||
)
|
||||
return False
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content=f"Error in before_llm_call hook: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content=f"Error in before_llm_call hook: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
return True
|
||||
|
||||
@@ -808,7 +805,6 @@ class BaseLLM(ABC):
|
||||
crew=None,
|
||||
response=response,
|
||||
)
|
||||
verbose = getattr(from_agent, "verbose", True) if from_agent else True
|
||||
printer = Printer()
|
||||
modified_response = response
|
||||
|
||||
@@ -819,10 +815,9 @@ class BaseLLM(ABC):
|
||||
modified_response = result
|
||||
hook_context.response = modified_response
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content=f"Error in after_llm_call hook: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content=f"Error in after_llm_call hook: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
return modified_response
|
||||
|
||||
@@ -23,7 +23,7 @@ if TYPE_CHECKING:
|
||||
try:
|
||||
from anthropic import Anthropic, AsyncAnthropic, transform_schema
|
||||
from anthropic.types import Message, TextBlock, ThinkingBlock, ToolUseBlock
|
||||
from anthropic.types.beta import BetaMessage, BetaTextBlock
|
||||
from anthropic.types.beta import BetaMessage
|
||||
import httpx
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
@@ -337,7 +337,6 @@ class AnthropicCompletion(BaseLLM):
|
||||
available_functions: Available functions for tool calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
response_model: Optional response model.
|
||||
|
||||
Returns:
|
||||
Chat completion response or tool call result
|
||||
@@ -678,31 +677,31 @@ class AnthropicCompletion(BaseLLM):
|
||||
if _is_pydantic_model_class(response_model) and response.content:
|
||||
if use_native_structured_output:
|
||||
for block in response.content:
|
||||
if isinstance(block, (TextBlock, BetaTextBlock)):
|
||||
structured_data = response_model.model_validate_json(block.text)
|
||||
if isinstance(block, TextBlock):
|
||||
structured_json = block.text
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_data
|
||||
return structured_json
|
||||
else:
|
||||
for block in response.content:
|
||||
if (
|
||||
isinstance(block, ToolUseBlock)
|
||||
and block.name == "structured_output"
|
||||
):
|
||||
structured_data = response_model.model_validate(block.input)
|
||||
structured_json = json.dumps(block.input)
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_data
|
||||
return structured_json
|
||||
|
||||
# Check if Claude wants to use tools
|
||||
if response.content:
|
||||
@@ -898,29 +897,28 @@ class AnthropicCompletion(BaseLLM):
|
||||
|
||||
if _is_pydantic_model_class(response_model):
|
||||
if use_native_structured_output:
|
||||
structured_data = response_model.model_validate_json(full_response)
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=full_response,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_data
|
||||
return full_response
|
||||
for block in final_message.content:
|
||||
if (
|
||||
isinstance(block, ToolUseBlock)
|
||||
and block.name == "structured_output"
|
||||
):
|
||||
structured_data = response_model.model_validate(block.input)
|
||||
structured_json = json.dumps(block.input)
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_data
|
||||
return structured_json
|
||||
|
||||
if final_message.content:
|
||||
tool_uses = [
|
||||
@@ -1168,31 +1166,31 @@ class AnthropicCompletion(BaseLLM):
|
||||
if _is_pydantic_model_class(response_model) and response.content:
|
||||
if use_native_structured_output:
|
||||
for block in response.content:
|
||||
if isinstance(block, (TextBlock, BetaTextBlock)):
|
||||
structured_data = response_model.model_validate_json(block.text)
|
||||
if isinstance(block, TextBlock):
|
||||
structured_json = block.text
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_data
|
||||
return structured_json
|
||||
else:
|
||||
for block in response.content:
|
||||
if (
|
||||
isinstance(block, ToolUseBlock)
|
||||
and block.name == "structured_output"
|
||||
):
|
||||
structured_data = response_model.model_validate(block.input)
|
||||
structured_json = json.dumps(block.input)
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_data
|
||||
return structured_json
|
||||
|
||||
if response.content:
|
||||
tool_uses = [
|
||||
@@ -1364,29 +1362,28 @@ class AnthropicCompletion(BaseLLM):
|
||||
|
||||
if _is_pydantic_model_class(response_model):
|
||||
if use_native_structured_output:
|
||||
structured_data = response_model.model_validate_json(full_response)
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=full_response,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_data
|
||||
return full_response
|
||||
for block in final_message.content:
|
||||
if (
|
||||
isinstance(block, ToolUseBlock)
|
||||
and block.name == "structured_output"
|
||||
):
|
||||
structured_data = response_model.model_validate(block.input)
|
||||
structured_json = json.dumps(block.input)
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_data
|
||||
return structured_json
|
||||
|
||||
if final_message.content:
|
||||
tool_uses = [
|
||||
|
||||
@@ -557,7 +557,7 @@ class AzureCompletion(BaseLLM):
|
||||
params: AzureCompletionParams,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> BaseModel:
|
||||
) -> str:
|
||||
"""Validate content against response model and emit completion event.
|
||||
|
||||
Args:
|
||||
@@ -568,23 +568,24 @@ class AzureCompletion(BaseLLM):
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Returns:
|
||||
Validated Pydantic model instance
|
||||
Validated and serialized JSON string
|
||||
|
||||
Raises:
|
||||
ValueError: If validation fails
|
||||
"""
|
||||
try:
|
||||
structured_data = response_model.model_validate_json(content)
|
||||
structured_json = structured_data.model_dump_json()
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
|
||||
return structured_data
|
||||
return structured_json
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
|
||||
logging.error(error_msg)
|
||||
|
||||
@@ -16,7 +16,6 @@ from crewai.utilities.agent_utils import is_context_length_exceeded
|
||||
from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
LLMContextLengthExceededError,
|
||||
)
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
@@ -549,11 +548,7 @@ class BedrockCompletion(BaseLLM):
|
||||
"toolSpec": {
|
||||
"name": "structured_output",
|
||||
"description": "Returns structured data according to the schema",
|
||||
"inputSchema": {
|
||||
"json": generate_model_description(response_model)
|
||||
.get("json_schema", {})
|
||||
.get("schema", {})
|
||||
},
|
||||
"inputSchema": {"json": response_model.model_json_schema()},
|
||||
}
|
||||
}
|
||||
body["toolConfig"] = cast(
|
||||
@@ -784,11 +779,7 @@ class BedrockCompletion(BaseLLM):
|
||||
"toolSpec": {
|
||||
"name": "structured_output",
|
||||
"description": "Returns structured data according to the schema",
|
||||
"inputSchema": {
|
||||
"json": generate_model_description(response_model)
|
||||
.get("json_schema", {})
|
||||
.get("schema", {})
|
||||
},
|
||||
"inputSchema": {"json": response_model.model_json_schema()},
|
||||
}
|
||||
}
|
||||
body["toolConfig"] = cast(
|
||||
@@ -1020,11 +1011,7 @@ class BedrockCompletion(BaseLLM):
|
||||
"toolSpec": {
|
||||
"name": "structured_output",
|
||||
"description": "Returns structured data according to the schema",
|
||||
"inputSchema": {
|
||||
"json": generate_model_description(response_model)
|
||||
.get("json_schema", {})
|
||||
.get("schema", {})
|
||||
},
|
||||
"inputSchema": {"json": response_model.model_json_schema()},
|
||||
}
|
||||
}
|
||||
body["toolConfig"] = cast(
|
||||
@@ -1236,11 +1223,7 @@ class BedrockCompletion(BaseLLM):
|
||||
"toolSpec": {
|
||||
"name": "structured_output",
|
||||
"description": "Returns structured data according to the schema",
|
||||
"inputSchema": {
|
||||
"json": generate_model_description(response_model)
|
||||
.get("json_schema", {})
|
||||
.get("schema", {})
|
||||
},
|
||||
"inputSchema": {"json": response_model.model_json_schema()},
|
||||
}
|
||||
}
|
||||
body["toolConfig"] = cast(
|
||||
|
||||
@@ -15,7 +15,6 @@ from crewai.utilities.agent_utils import is_context_length_exceeded
|
||||
from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
LLMContextLengthExceededError,
|
||||
)
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
@@ -132,9 +131,6 @@ class GeminiCompletion(BaseLLM):
|
||||
self.supports_tools = bool(
|
||||
version_match and float(version_match.group(1)) >= 1.5
|
||||
)
|
||||
self.is_gemini_2_0 = bool(
|
||||
version_match and float(version_match.group(1)) >= 2.0
|
||||
)
|
||||
|
||||
@property
|
||||
def stop(self) -> list[str]:
|
||||
@@ -442,11 +438,6 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
Returns:
|
||||
GenerateContentConfig object for Gemini API
|
||||
|
||||
Note:
|
||||
Structured output support varies by model version:
|
||||
- Gemini 1.5 and earlier: Uses response_schema (Pydantic model)
|
||||
- Gemini 2.0+: Uses response_json_schema (JSON Schema) with propertyOrdering
|
||||
"""
|
||||
self.tools = tools
|
||||
config_params: dict[str, Any] = {}
|
||||
@@ -473,14 +464,7 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
if response_model:
|
||||
config_params["response_mime_type"] = "application/json"
|
||||
schema_output = generate_model_description(response_model)
|
||||
schema = schema_output.get("json_schema", {}).get("schema", {})
|
||||
|
||||
if self.is_gemini_2_0:
|
||||
schema = self._add_property_ordering(schema)
|
||||
config_params["response_json_schema"] = schema
|
||||
else:
|
||||
config_params["response_schema"] = response_model
|
||||
config_params["response_schema"] = response_model.model_json_schema()
|
||||
|
||||
# Handle tools for supported models
|
||||
if tools and self.supports_tools:
|
||||
@@ -505,7 +489,7 @@ class GeminiCompletion(BaseLLM):
|
||||
function_declaration = types.FunctionDeclaration(
|
||||
name=name,
|
||||
description=description,
|
||||
parameters_json_schema=parameters if parameters else None,
|
||||
parameters=parameters if parameters else None,
|
||||
)
|
||||
|
||||
gemini_tool = types.Tool(function_declarations=[function_declaration])
|
||||
@@ -559,10 +543,11 @@ class GeminiCompletion(BaseLLM):
|
||||
else:
|
||||
parts.append(types.Part.from_text(text=str(content) if content else ""))
|
||||
|
||||
text_content: str = " ".join(p.text for p in parts if p.text is not None)
|
||||
|
||||
if role == "system":
|
||||
# Extract system instruction - Gemini handles it separately
|
||||
text_content = " ".join(
|
||||
p.text for p in parts if hasattr(p, "text") and p.text
|
||||
)
|
||||
if system_instruction:
|
||||
system_instruction += f"\n\n{text_content}"
|
||||
else:
|
||||
@@ -591,40 +576,31 @@ class GeminiCompletion(BaseLLM):
|
||||
types.Content(role="user", parts=[function_response_part])
|
||||
)
|
||||
elif role == "assistant" and message.get("tool_calls"):
|
||||
raw_parts: list[Any] | None = message.get("raw_tool_call_parts")
|
||||
if raw_parts and all(isinstance(p, types.Part) for p in raw_parts):
|
||||
tool_parts: list[types.Part] = list(raw_parts)
|
||||
if text_content:
|
||||
tool_parts.insert(0, types.Part.from_text(text=text_content))
|
||||
else:
|
||||
tool_parts = []
|
||||
if text_content:
|
||||
tool_parts.append(types.Part.from_text(text=text_content))
|
||||
tool_parts: list[types.Part] = []
|
||||
|
||||
tool_calls: list[dict[str, Any]] = message.get("tool_calls") or []
|
||||
for tool_call in tool_calls:
|
||||
func: dict[str, Any] = tool_call.get("function") or {}
|
||||
func_name: str = str(func.get("name") or "")
|
||||
func_args_raw: str | dict[str, Any] = (
|
||||
func.get("arguments") or {}
|
||||
)
|
||||
if text_content:
|
||||
tool_parts.append(types.Part.from_text(text=text_content))
|
||||
|
||||
func_args: dict[str, Any]
|
||||
if isinstance(func_args_raw, str):
|
||||
try:
|
||||
func_args = (
|
||||
json.loads(func_args_raw) if func_args_raw else {}
|
||||
)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
func_args = {}
|
||||
else:
|
||||
func_args = func_args_raw
|
||||
tool_calls: list[dict[str, Any]] = message.get("tool_calls") or []
|
||||
for tool_call in tool_calls:
|
||||
func: dict[str, Any] = tool_call.get("function") or {}
|
||||
func_name: str = str(func.get("name") or "")
|
||||
func_args_raw: str | dict[str, Any] = func.get("arguments") or {}
|
||||
|
||||
tool_parts.append(
|
||||
types.Part.from_function_call(
|
||||
name=func_name, args=func_args
|
||||
func_args: dict[str, Any]
|
||||
if isinstance(func_args_raw, str):
|
||||
try:
|
||||
func_args = (
|
||||
json.loads(func_args_raw) if func_args_raw else {}
|
||||
)
|
||||
)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
func_args = {}
|
||||
else:
|
||||
func_args = func_args_raw
|
||||
|
||||
tool_parts.append(
|
||||
types.Part.from_function_call(name=func_name, args=func_args)
|
||||
)
|
||||
|
||||
contents.append(types.Content(role="model", parts=tool_parts))
|
||||
else:
|
||||
@@ -644,7 +620,7 @@ class GeminiCompletion(BaseLLM):
|
||||
messages_for_event: list[LLMMessage],
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> BaseModel:
|
||||
) -> str:
|
||||
"""Validate content against response model and emit completion event.
|
||||
|
||||
Args:
|
||||
@@ -655,23 +631,24 @@ class GeminiCompletion(BaseLLM):
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Returns:
|
||||
Validated Pydantic model instance
|
||||
Validated and serialized JSON string
|
||||
|
||||
Raises:
|
||||
ValueError: If validation fails
|
||||
"""
|
||||
try:
|
||||
structured_data = response_model.model_validate_json(content)
|
||||
structured_json = structured_data.model_dump_json()
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=structured_data.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
)
|
||||
|
||||
return structured_data
|
||||
return structured_json
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
|
||||
logging.error(error_msg)
|
||||
@@ -684,7 +661,7 @@ class GeminiCompletion(BaseLLM):
|
||||
response_model: type[BaseModel] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> str | BaseModel:
|
||||
) -> str:
|
||||
"""Finalize completion response with validation and event emission.
|
||||
|
||||
Args:
|
||||
@@ -695,7 +672,7 @@ class GeminiCompletion(BaseLLM):
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Returns:
|
||||
Final response content after processing (str or Pydantic model if response_model provided)
|
||||
Final response content after processing
|
||||
"""
|
||||
messages_for_event = self._convert_contents_to_dict(contents)
|
||||
|
||||
@@ -881,7 +858,7 @@ class GeminiCompletion(BaseLLM):
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | BaseModel | list[dict[str, Any]]:
|
||||
) -> str | list[dict[str, Any]]:
|
||||
"""Finalize streaming response with usage tracking, function execution, and events.
|
||||
|
||||
Args:
|
||||
@@ -1001,7 +978,7 @@ class GeminiCompletion(BaseLLM):
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | BaseModel | list[dict[str, Any]] | Any:
|
||||
) -> str | Any:
|
||||
"""Handle streaming content generation."""
|
||||
full_response = ""
|
||||
function_calls: dict[int, dict[str, Any]] = {}
|
||||
@@ -1201,36 +1178,6 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
return "".join(text_parts)
|
||||
|
||||
@staticmethod
|
||||
def _add_property_ordering(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Add propertyOrdering to JSON schema for Gemini 2.0 compatibility.
|
||||
|
||||
Gemini 2.0 models require an explicit propertyOrdering list to define
|
||||
the preferred structure of JSON objects. This recursively adds
|
||||
propertyOrdering to all objects in the schema.
|
||||
|
||||
Args:
|
||||
schema: JSON schema dictionary.
|
||||
|
||||
Returns:
|
||||
Modified schema with propertyOrdering added to all objects.
|
||||
"""
|
||||
if isinstance(schema, dict):
|
||||
if schema.get("type") == "object" and "properties" in schema:
|
||||
properties = schema["properties"]
|
||||
if properties and "propertyOrdering" not in schema:
|
||||
schema["propertyOrdering"] = list(properties.keys())
|
||||
|
||||
for value in schema.values():
|
||||
if isinstance(value, dict):
|
||||
GeminiCompletion._add_property_ordering(value)
|
||||
elif isinstance(value, list):
|
||||
for item in value:
|
||||
if isinstance(item, dict):
|
||||
GeminiCompletion._add_property_ordering(item)
|
||||
|
||||
return schema
|
||||
|
||||
@staticmethod
|
||||
def _convert_contents_to_dict(
|
||||
contents: list[types.Content],
|
||||
|
||||
@@ -693,14 +693,14 @@ class OpenAICompletion(BaseLLM):
|
||||
if response_model or self.response_format:
|
||||
format_model = response_model or self.response_format
|
||||
if isinstance(format_model, type) and issubclass(format_model, BaseModel):
|
||||
schema_output = generate_model_description(format_model)
|
||||
json_schema = schema_output.get("json_schema", {})
|
||||
schema = format_model.model_json_schema()
|
||||
schema["additionalProperties"] = False
|
||||
params["text"] = {
|
||||
"format": {
|
||||
"type": "json_schema",
|
||||
"name": json_schema.get("name", format_model.__name__),
|
||||
"strict": json_schema.get("strict", True),
|
||||
"schema": json_schema.get("schema", {}),
|
||||
"name": format_model.__name__,
|
||||
"strict": True,
|
||||
"schema": schema,
|
||||
}
|
||||
}
|
||||
elif isinstance(format_model, dict):
|
||||
@@ -1060,7 +1060,7 @@ class OpenAICompletion(BaseLLM):
|
||||
chunk=delta_text,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_id=response_id_stream,
|
||||
response_id=response_id_stream
|
||||
)
|
||||
|
||||
elif event.type == "response.function_call_arguments.delta":
|
||||
@@ -1570,14 +1570,15 @@ class OpenAICompletion(BaseLLM):
|
||||
|
||||
parsed_object = parsed_response.choices[0].message.parsed
|
||||
if parsed_object:
|
||||
structured_json = parsed_object.model_dump_json()
|
||||
self._emit_call_completed_event(
|
||||
response=parsed_object.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return parsed_object
|
||||
return structured_json
|
||||
|
||||
response: ChatCompletion = self.client.chat.completions.create(**params)
|
||||
|
||||
@@ -1691,7 +1692,7 @@ class OpenAICompletion(BaseLLM):
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | BaseModel:
|
||||
) -> str:
|
||||
"""Handle streaming chat completion."""
|
||||
full_response = ""
|
||||
tool_calls: dict[int, dict[str, Any]] = {}
|
||||
@@ -1708,7 +1709,7 @@ class OpenAICompletion(BaseLLM):
|
||||
**parse_params, response_format=response_model
|
||||
) as stream:
|
||||
for chunk in stream:
|
||||
response_id_stream = chunk.id if hasattr(chunk, "id") else None
|
||||
response_id_stream=chunk.id if hasattr(chunk,"id") else None
|
||||
|
||||
if chunk.type == "content.delta":
|
||||
delta_content = chunk.delta
|
||||
@@ -1717,7 +1718,7 @@ class OpenAICompletion(BaseLLM):
|
||||
chunk=delta_content,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_id=response_id_stream,
|
||||
response_id=response_id_stream
|
||||
)
|
||||
|
||||
final_completion = stream.get_final_completion()
|
||||
@@ -1727,14 +1728,15 @@ class OpenAICompletion(BaseLLM):
|
||||
if final_completion.choices:
|
||||
parsed_result = final_completion.choices[0].message.parsed
|
||||
if parsed_result:
|
||||
structured_json = parsed_result.model_dump_json()
|
||||
self._emit_call_completed_event(
|
||||
response=parsed_result.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return parsed_result
|
||||
return structured_json
|
||||
|
||||
logging.error("Failed to get parsed result from stream")
|
||||
return ""
|
||||
@@ -1746,9 +1748,7 @@ class OpenAICompletion(BaseLLM):
|
||||
usage_data = {"total_tokens": 0}
|
||||
|
||||
for completion_chunk in completion_stream:
|
||||
response_id_stream = (
|
||||
completion_chunk.id if hasattr(completion_chunk, "id") else None
|
||||
)
|
||||
response_id_stream=completion_chunk.id if hasattr(completion_chunk,"id") else None
|
||||
|
||||
if hasattr(completion_chunk, "usage") and completion_chunk.usage:
|
||||
usage_data = self._extract_openai_token_usage(completion_chunk)
|
||||
@@ -1766,7 +1766,7 @@ class OpenAICompletion(BaseLLM):
|
||||
chunk=chunk_delta.content,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_id=response_id_stream,
|
||||
response_id=response_id_stream
|
||||
)
|
||||
|
||||
if chunk_delta.tool_calls:
|
||||
@@ -1805,7 +1805,7 @@ class OpenAICompletion(BaseLLM):
|
||||
"index": tool_calls[tool_index]["index"],
|
||||
},
|
||||
call_type=LLMCallType.TOOL_CALL,
|
||||
response_id=response_id_stream,
|
||||
response_id=response_id_stream
|
||||
)
|
||||
|
||||
self._track_token_usage_internal(usage_data)
|
||||
@@ -1885,14 +1885,15 @@ class OpenAICompletion(BaseLLM):
|
||||
|
||||
parsed_object = parsed_response.choices[0].message.parsed
|
||||
if parsed_object:
|
||||
structured_json = parsed_object.model_dump_json()
|
||||
self._emit_call_completed_event(
|
||||
response=parsed_object.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return parsed_object
|
||||
return structured_json
|
||||
|
||||
response: ChatCompletion = await self.async_client.chat.completions.create(
|
||||
**params
|
||||
@@ -2003,7 +2004,7 @@ class OpenAICompletion(BaseLLM):
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | BaseModel:
|
||||
) -> str:
|
||||
"""Handle async streaming chat completion."""
|
||||
full_response = ""
|
||||
tool_calls: dict[int, dict[str, Any]] = {}
|
||||
@@ -2016,7 +2017,7 @@ class OpenAICompletion(BaseLLM):
|
||||
accumulated_content = ""
|
||||
usage_data = {"total_tokens": 0}
|
||||
async for chunk in completion_stream:
|
||||
response_id_stream = chunk.id if hasattr(chunk, "id") else None
|
||||
response_id_stream=chunk.id if hasattr(chunk,"id") else None
|
||||
|
||||
if hasattr(chunk, "usage") and chunk.usage:
|
||||
usage_data = self._extract_openai_token_usage(chunk)
|
||||
@@ -2034,23 +2035,24 @@ class OpenAICompletion(BaseLLM):
|
||||
chunk=delta.content,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_id=response_id_stream,
|
||||
response_id=response_id_stream
|
||||
)
|
||||
|
||||
self._track_token_usage_internal(usage_data)
|
||||
|
||||
try:
|
||||
parsed_object = response_model.model_validate_json(accumulated_content)
|
||||
structured_json = parsed_object.model_dump_json()
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=parsed_object.model_dump_json(),
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
|
||||
return parsed_object
|
||||
return structured_json
|
||||
except Exception as e:
|
||||
logging.error(f"Failed to parse structured output from stream: {e}")
|
||||
self._emit_call_completed_event(
|
||||
@@ -2069,7 +2071,7 @@ class OpenAICompletion(BaseLLM):
|
||||
usage_data = {"total_tokens": 0}
|
||||
|
||||
async for chunk in stream:
|
||||
response_id_stream = chunk.id if hasattr(chunk, "id") else None
|
||||
response_id_stream=chunk.id if hasattr(chunk,"id") else None
|
||||
|
||||
if hasattr(chunk, "usage") and chunk.usage:
|
||||
usage_data = self._extract_openai_token_usage(chunk)
|
||||
@@ -2087,7 +2089,7 @@ class OpenAICompletion(BaseLLM):
|
||||
chunk=chunk_delta.content,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_id=response_id_stream,
|
||||
response_id=response_id_stream
|
||||
)
|
||||
|
||||
if chunk_delta.tool_calls:
|
||||
@@ -2126,7 +2128,7 @@ class OpenAICompletion(BaseLLM):
|
||||
"index": tool_calls[tool_index]["index"],
|
||||
},
|
||||
call_type=LLMCallType.TOOL_CALL,
|
||||
response_id=response_id_stream,
|
||||
response_id=response_id_stream
|
||||
)
|
||||
|
||||
self._track_token_usage_internal(usage_data)
|
||||
|
||||
@@ -2,7 +2,6 @@ import logging
|
||||
import re
|
||||
from typing import Any
|
||||
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.string_utils import sanitize_tool_name
|
||||
|
||||
|
||||
@@ -78,8 +77,7 @@ def extract_tool_info(tool: dict[str, Any]) -> tuple[str, str, dict[str, Any]]:
|
||||
# Also check for args_schema (Pydantic format)
|
||||
if not parameters and "args_schema" in tool:
|
||||
if hasattr(tool["args_schema"], "model_json_schema"):
|
||||
schema_output = generate_model_description(tool["args_schema"])
|
||||
parameters = schema_output.get("json_schema", {}).get("schema", {})
|
||||
parameters = tool["args_schema"].model_json_schema()
|
||||
|
||||
return name, description, parameters
|
||||
|
||||
|
||||
@@ -12,17 +12,15 @@ from crewai.utilities.paths import db_storage_path
|
||||
class LTMSQLiteStorage:
|
||||
"""SQLite storage class for long-term memory data."""
|
||||
|
||||
def __init__(self, db_path: str | None = None, verbose: bool = True) -> None:
|
||||
def __init__(self, db_path: str | None = None) -> None:
|
||||
"""Initialize the SQLite storage.
|
||||
|
||||
Args:
|
||||
db_path: Optional path to the database file.
|
||||
verbose: Whether to print error messages.
|
||||
"""
|
||||
if db_path is None:
|
||||
db_path = str(Path(db_storage_path()) / "long_term_memory_storage.db")
|
||||
self.db_path = db_path
|
||||
self._verbose = verbose
|
||||
self._printer: Printer = Printer()
|
||||
Path(self.db_path).parent.mkdir(parents=True, exist_ok=True)
|
||||
self._initialize_db()
|
||||
@@ -46,11 +44,10 @@ class LTMSQLiteStorage:
|
||||
|
||||
conn.commit()
|
||||
except sqlite3.Error as e:
|
||||
if self._verbose:
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred during database initialization: {e}",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred during database initialization: {e}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
def save(
|
||||
self,
|
||||
@@ -72,11 +69,10 @@ class LTMSQLiteStorage:
|
||||
)
|
||||
conn.commit()
|
||||
except sqlite3.Error as e:
|
||||
if self._verbose:
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
def load(self, task_description: str, latest_n: int) -> list[dict[str, Any]] | None:
|
||||
"""Queries the LTM table by task description with error handling."""
|
||||
@@ -105,11 +101,10 @@ class LTMSQLiteStorage:
|
||||
]
|
||||
|
||||
except sqlite3.Error as e:
|
||||
if self._verbose:
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
return None
|
||||
|
||||
def reset(self) -> None:
|
||||
@@ -121,11 +116,10 @@ class LTMSQLiteStorage:
|
||||
conn.commit()
|
||||
|
||||
except sqlite3.Error as e:
|
||||
if self._verbose:
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
async def asave(
|
||||
self,
|
||||
@@ -153,11 +147,10 @@ class LTMSQLiteStorage:
|
||||
)
|
||||
await conn.commit()
|
||||
except aiosqlite.Error as e:
|
||||
if self._verbose:
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
async def aload(
|
||||
self, task_description: str, latest_n: int
|
||||
@@ -194,11 +187,10 @@ class LTMSQLiteStorage:
|
||||
for row in rows
|
||||
]
|
||||
except aiosqlite.Error as e:
|
||||
if self._verbose:
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
return None
|
||||
|
||||
async def areset(self) -> None:
|
||||
@@ -208,8 +200,7 @@ class LTMSQLiteStorage:
|
||||
await conn.execute("DELETE FROM long_term_memories")
|
||||
await conn.commit()
|
||||
except aiosqlite.Error as e:
|
||||
if self._verbose:
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
|
||||
color="red",
|
||||
)
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
"""IBM WatsonX embedding function implementation."""
|
||||
|
||||
from typing import Any, cast
|
||||
from typing import cast
|
||||
|
||||
from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
|
||||
from typing_extensions import Unpack
|
||||
@@ -15,18 +15,14 @@ _printer = Printer()
|
||||
class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
|
||||
"""Embedding function for IBM WatsonX models."""
|
||||
|
||||
def __init__(
|
||||
self, *, verbose: bool = True, **kwargs: Unpack[WatsonXProviderConfig]
|
||||
) -> None:
|
||||
def __init__(self, **kwargs: Unpack[WatsonXProviderConfig]) -> None:
|
||||
"""Initialize WatsonX embedding function.
|
||||
|
||||
Args:
|
||||
verbose: Whether to print error messages.
|
||||
**kwargs: Configuration parameters for WatsonX Embeddings and Credentials.
|
||||
"""
|
||||
super().__init__(**kwargs)
|
||||
self._config = kwargs
|
||||
self._verbose = verbose
|
||||
|
||||
@staticmethod
|
||||
def name() -> str:
|
||||
@@ -60,7 +56,7 @@ class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
|
||||
if isinstance(input, str):
|
||||
input = [input]
|
||||
|
||||
embeddings_config: dict[str, Any] = {
|
||||
embeddings_config: dict = {
|
||||
"model_id": self._config["model_id"],
|
||||
}
|
||||
if "params" in self._config and self._config["params"] is not None:
|
||||
@@ -94,7 +90,7 @@ class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
|
||||
if "credentials" in self._config and self._config["credentials"] is not None:
|
||||
embeddings_config["credentials"] = self._config["credentials"]
|
||||
else:
|
||||
cred_config: dict[str, Any] = {}
|
||||
cred_config: dict = {}
|
||||
if "url" in self._config and self._config["url"] is not None:
|
||||
cred_config["url"] = self._config["url"]
|
||||
if "api_key" in self._config and self._config["api_key"] is not None:
|
||||
@@ -163,6 +159,5 @@ class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
|
||||
embeddings = embedding.embed_documents(input)
|
||||
return cast(Embeddings, embeddings)
|
||||
except Exception as e:
|
||||
if self._verbose:
|
||||
_printer.print(f"Error during WatsonX embedding: {e}", color="red")
|
||||
_printer.print(f"Error during WatsonX embedding: {e}", color="red")
|
||||
raise
|
||||
|
||||
@@ -767,11 +767,10 @@ class Task(BaseModel):
|
||||
if files:
|
||||
supported_types: list[str] = []
|
||||
if self.agent.llm and self.agent.llm.supports_multimodal():
|
||||
provider: str = str(
|
||||
getattr(self.agent.llm, "provider", None)
|
||||
or getattr(self.agent.llm, "model", "openai")
|
||||
provider = getattr(self.agent.llm, "provider", None) or getattr(
|
||||
self.agent.llm, "model", "openai"
|
||||
)
|
||||
api: str | None = getattr(self.agent.llm, "api", None)
|
||||
api = getattr(self.agent.llm, "api", None)
|
||||
supported_types = get_supported_content_types(provider, api)
|
||||
|
||||
def is_auto_injected(content_type: str) -> bool:
|
||||
@@ -888,11 +887,10 @@ Follow these guidelines:
|
||||
try:
|
||||
crew_chat_messages = json.loads(crew_chat_messages_json)
|
||||
except json.JSONDecodeError as e:
|
||||
if self.agent and self.agent.verbose:
|
||||
_printer.print(
|
||||
f"An error occurred while parsing crew chat messages: {e}",
|
||||
color="red",
|
||||
)
|
||||
_printer.print(
|
||||
f"An error occurred while parsing crew chat messages: {e}",
|
||||
color="red",
|
||||
)
|
||||
raise
|
||||
|
||||
conversation_history = "\n".join(
|
||||
@@ -1134,12 +1132,11 @@ Follow these guidelines:
|
||||
guardrail_result_error=guardrail_result.error,
|
||||
task_output=task_output.raw,
|
||||
)
|
||||
if agent and agent.verbose:
|
||||
printer = Printer()
|
||||
printer.print(
|
||||
content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
|
||||
color="yellow",
|
||||
)
|
||||
printer = Printer()
|
||||
printer.print(
|
||||
content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
# Regenerate output from agent
|
||||
result = agent.execute_task(
|
||||
@@ -1232,12 +1229,11 @@ Follow these guidelines:
|
||||
guardrail_result_error=guardrail_result.error,
|
||||
task_output=task_output.raw,
|
||||
)
|
||||
if agent and agent.verbose:
|
||||
printer = Printer()
|
||||
printer.print(
|
||||
content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
|
||||
color="yellow",
|
||||
)
|
||||
printer = Printer()
|
||||
printer.print(
|
||||
content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
result = await agent.aexecute_task(
|
||||
task=self,
|
||||
|
||||
@@ -173,6 +173,13 @@ class Telemetry:
|
||||
|
||||
self._original_handlers: dict[int, Any] = {}
|
||||
|
||||
if threading.current_thread() is not threading.main_thread():
|
||||
logger.debug(
|
||||
"CrewAI telemetry: Skipping signal handler registration "
|
||||
"(not running in main thread)."
|
||||
)
|
||||
return
|
||||
|
||||
self._register_signal_handler(signal.SIGTERM, SigTermEvent, shutdown=True)
|
||||
self._register_signal_handler(signal.SIGINT, SigIntEvent, shutdown=True)
|
||||
if hasattr(signal, "SIGHUP"):
|
||||
|
||||
@@ -384,8 +384,6 @@ class ToolUsage:
|
||||
if (
|
||||
hasattr(available_tool, "max_usage_count")
|
||||
and available_tool.max_usage_count is not None
|
||||
and self.agent
|
||||
and self.agent.verbose
|
||||
):
|
||||
self._printer.print(
|
||||
content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
|
||||
@@ -398,8 +396,6 @@ class ToolUsage:
|
||||
if (
|
||||
hasattr(available_tool, "max_usage_count")
|
||||
and available_tool.max_usage_count is not None
|
||||
and self.agent
|
||||
and self.agent.verbose
|
||||
):
|
||||
self._printer.print(
|
||||
content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
|
||||
@@ -614,8 +610,6 @@ class ToolUsage:
|
||||
if (
|
||||
hasattr(available_tool, "max_usage_count")
|
||||
and available_tool.max_usage_count is not None
|
||||
and self.agent
|
||||
and self.agent.verbose
|
||||
):
|
||||
self._printer.print(
|
||||
content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
|
||||
@@ -628,8 +622,6 @@ class ToolUsage:
|
||||
if (
|
||||
hasattr(available_tool, "max_usage_count")
|
||||
and available_tool.max_usage_count is not None
|
||||
and self.agent
|
||||
and self.agent.verbose
|
||||
):
|
||||
self._printer.print(
|
||||
content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
|
||||
@@ -892,17 +884,15 @@ class ToolUsage:
|
||||
# Attempt 4: Repair JSON
|
||||
try:
|
||||
repaired_input = str(repair_json(tool_input, skip_json_loads=True))
|
||||
if self.agent and self.agent.verbose:
|
||||
self._printer.print(
|
||||
content=f"Repaired JSON: {repaired_input}", color="blue"
|
||||
)
|
||||
self._printer.print(
|
||||
content=f"Repaired JSON: {repaired_input}", color="blue"
|
||||
)
|
||||
arguments = json.loads(repaired_input)
|
||||
if isinstance(arguments, dict):
|
||||
return arguments
|
||||
except Exception as e:
|
||||
error = f"Failed to repair JSON: {e}"
|
||||
if self.agent and self.agent.verbose:
|
||||
self._printer.print(content=error, color="red")
|
||||
self._printer.print(content=error, color="red")
|
||||
|
||||
error_message = (
|
||||
"Tool input must be a valid dictionary in JSON or Python literal format"
|
||||
|
||||
@@ -10,10 +10,9 @@
|
||||
"memory": "\n\n# Useful context: \n{memory}",
|
||||
"role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
|
||||
"tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
|
||||
"no_tools": "",
|
||||
"task_no_tools": "\nCurrent Task: {input}\n\nProvide your complete response:",
|
||||
"native_tools": "",
|
||||
"native_task": "\nCurrent Task: {input}",
|
||||
"no_tools": "\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
|
||||
"native_tools": "\nUse available tools to gather information and complete your task.",
|
||||
"native_task": "\nCurrent Task: {input}\n\nThis is VERY important to you, your job depends on it!",
|
||||
"post_tool_reasoning": "Analyze the tool result. If requirements are met, provide the Final Answer. Otherwise, call the next tool. Deliver only the answer without meta-commentary.",
|
||||
"format": "Decide if you need a tool or can provide the final answer. Use one at a time.\nTo use a tool, use:\nThought: [reasoning]\nAction: [name from {tool_names}]\nAction Input: [JSON object]\n\nTo provide the final answer, use:\nThought: [reasoning]\nFinal Answer: [complete response]",
|
||||
"final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",
|
||||
|
||||
@@ -28,7 +28,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
)
|
||||
from crewai.utilities.i18n import I18N
|
||||
from crewai.utilities.printer import ColoredText, Printer
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.string_utils import sanitize_tool_name
|
||||
from crewai.utilities.token_counter_callback import TokenCalcHandler
|
||||
from crewai.utilities.types import LLMMessage
|
||||
@@ -37,7 +36,6 @@ from crewai.utilities.types import LLMMessage
|
||||
if TYPE_CHECKING:
|
||||
from crewai.agent import Agent
|
||||
from crewai.agents.crew_agent_executor import CrewAgentExecutor
|
||||
from crewai.experimental.agent_executor import AgentExecutor
|
||||
from crewai.lite_agent import LiteAgent
|
||||
from crewai.llm import LLM
|
||||
from crewai.task import Task
|
||||
@@ -160,8 +158,7 @@ def convert_tools_to_openai_schema(
|
||||
parameters: dict[str, Any] = {}
|
||||
if hasattr(tool, "args_schema") and tool.args_schema is not None:
|
||||
try:
|
||||
schema_output = generate_model_description(tool.args_schema)
|
||||
parameters = schema_output.get("json_schema", {}).get("schema", {})
|
||||
parameters = tool.args_schema.model_json_schema()
|
||||
# Remove title and description from schema root as they're redundant
|
||||
parameters.pop("title", None)
|
||||
parameters.pop("description", None)
|
||||
@@ -210,7 +207,6 @@ def handle_max_iterations_exceeded(
|
||||
messages: list[LLMMessage],
|
||||
llm: LLM | BaseLLM,
|
||||
callbacks: list[TokenCalcHandler],
|
||||
verbose: bool = True,
|
||||
) -> AgentFinish:
|
||||
"""Handles the case when the maximum number of iterations is exceeded. Performs one more LLM call to get the final answer.
|
||||
|
||||
@@ -221,16 +217,14 @@ def handle_max_iterations_exceeded(
|
||||
messages: List of messages to send to the LLM.
|
||||
llm: The LLM instance to call.
|
||||
callbacks: List of callbacks for the LLM call.
|
||||
verbose: Whether to print output.
|
||||
|
||||
Returns:
|
||||
AgentFinish with the final answer after exceeding max iterations.
|
||||
"""
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Maximum iterations reached. Requesting final answer.",
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content="Maximum iterations reached. Requesting final answer.",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
if formatted_answer and hasattr(formatted_answer, "text"):
|
||||
assistant_message = (
|
||||
@@ -248,11 +242,10 @@ def handle_max_iterations_exceeded(
|
||||
)
|
||||
|
||||
if answer is None or answer == "":
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
raise ValueError("Invalid response from LLM call - None or empty.")
|
||||
|
||||
formatted = format_answer(answer=answer)
|
||||
@@ -325,9 +318,8 @@ def get_llm_response(
|
||||
from_task: Task | None = None,
|
||||
from_agent: Agent | LiteAgent | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None = None,
|
||||
verbose: bool = True,
|
||||
) -> str | BaseModel | Any:
|
||||
executor_context: CrewAgentExecutor | LiteAgent | None = None,
|
||||
) -> str | Any:
|
||||
"""Call the LLM and return the response, handling any invalid responses.
|
||||
|
||||
Args:
|
||||
@@ -341,11 +333,10 @@ def get_llm_response(
|
||||
from_agent: Optional agent context for the LLM call.
|
||||
response_model: Optional Pydantic model for structured outputs.
|
||||
executor_context: Optional executor context for hook invocation.
|
||||
verbose: Whether to print output.
|
||||
|
||||
Returns:
|
||||
The response from the LLM as a string, Pydantic model (when response_model is provided),
|
||||
or tool call results if native function calling is used.
|
||||
The response from the LLM as a string, or tool call results if
|
||||
native function calling is used.
|
||||
|
||||
Raises:
|
||||
Exception: If an error occurs.
|
||||
@@ -353,7 +344,7 @@ def get_llm_response(
|
||||
"""
|
||||
|
||||
if executor_context is not None:
|
||||
if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
|
||||
if not _setup_before_llm_call_hooks(executor_context, printer):
|
||||
raise ValueError("LLM call blocked by before_llm_call hook")
|
||||
messages = executor_context.messages
|
||||
|
||||
@@ -370,16 +361,13 @@ def get_llm_response(
|
||||
except Exception as e:
|
||||
raise e
|
||||
if not answer:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
raise ValueError("Invalid response from LLM call - None or empty.")
|
||||
|
||||
return _setup_after_llm_call_hooks(
|
||||
executor_context, answer, printer, verbose=verbose
|
||||
)
|
||||
return _setup_after_llm_call_hooks(executor_context, answer, printer)
|
||||
|
||||
|
||||
async def aget_llm_response(
|
||||
@@ -392,9 +380,8 @@ async def aget_llm_response(
|
||||
from_task: Task | None = None,
|
||||
from_agent: Agent | LiteAgent | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
executor_context: CrewAgentExecutor | AgentExecutor | None = None,
|
||||
verbose: bool = True,
|
||||
) -> str | BaseModel | Any:
|
||||
executor_context: CrewAgentExecutor | None = None,
|
||||
) -> str | Any:
|
||||
"""Call the LLM asynchronously and return the response.
|
||||
|
||||
Args:
|
||||
@@ -410,15 +397,15 @@ async def aget_llm_response(
|
||||
executor_context: Optional executor context for hook invocation.
|
||||
|
||||
Returns:
|
||||
The response from the LLM as a string, Pydantic model (when response_model is provided),
|
||||
or tool call results if native function calling is used.
|
||||
The response from the LLM as a string, or tool call results if
|
||||
native function calling is used.
|
||||
|
||||
Raises:
|
||||
Exception: If an error occurs.
|
||||
ValueError: If the response is None or empty.
|
||||
"""
|
||||
if executor_context is not None:
|
||||
if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
|
||||
if not _setup_before_llm_call_hooks(executor_context, printer):
|
||||
raise ValueError("LLM call blocked by before_llm_call hook")
|
||||
messages = executor_context.messages
|
||||
|
||||
@@ -435,16 +422,13 @@ async def aget_llm_response(
|
||||
except Exception as e:
|
||||
raise e
|
||||
if not answer:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
raise ValueError("Invalid response from LLM call - None or empty.")
|
||||
|
||||
return _setup_after_llm_call_hooks(
|
||||
executor_context, answer, printer, verbose=verbose
|
||||
)
|
||||
return _setup_after_llm_call_hooks(executor_context, answer, printer)
|
||||
|
||||
|
||||
def process_llm_response(
|
||||
@@ -511,19 +495,13 @@ def handle_agent_action_core(
|
||||
return formatted_answer
|
||||
|
||||
|
||||
def handle_unknown_error(
|
||||
printer: Printer, exception: Exception, verbose: bool = True
|
||||
) -> None:
|
||||
def handle_unknown_error(printer: Printer, exception: Exception) -> None:
|
||||
"""Handle unknown errors by informing the user.
|
||||
|
||||
Args:
|
||||
printer: Printer instance for output
|
||||
exception: The exception that occurred
|
||||
verbose: Whether to print output.
|
||||
"""
|
||||
if not verbose:
|
||||
return
|
||||
|
||||
error_message = str(exception)
|
||||
|
||||
if "litellm" in error_message:
|
||||
@@ -545,7 +523,6 @@ def handle_output_parser_exception(
|
||||
iterations: int,
|
||||
log_error_after: int = 3,
|
||||
printer: Printer | None = None,
|
||||
verbose: bool = True,
|
||||
) -> AgentAction:
|
||||
"""Handle OutputParserError by updating messages and formatted_answer.
|
||||
|
||||
@@ -568,7 +545,7 @@ def handle_output_parser_exception(
|
||||
thought="",
|
||||
)
|
||||
|
||||
if verbose and iterations > log_error_after and printer:
|
||||
if iterations > log_error_after and printer:
|
||||
printer.print(
|
||||
content=f"Error parsing LLM output, agent will retry: {e.error}",
|
||||
color="red",
|
||||
@@ -598,7 +575,6 @@ def handle_context_length(
|
||||
llm: LLM | BaseLLM,
|
||||
callbacks: list[TokenCalcHandler],
|
||||
i18n: I18N,
|
||||
verbose: bool = True,
|
||||
) -> None:
|
||||
"""Handle context length exceeded by either summarizing or raising an error.
|
||||
|
||||
@@ -614,20 +590,16 @@ def handle_context_length(
|
||||
SystemExit: If context length is exceeded and user opts not to summarize
|
||||
"""
|
||||
if respect_context_window:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Context length exceeded. Summarizing content to fit the model context window. Might take a while...",
|
||||
color="yellow",
|
||||
)
|
||||
summarize_messages(
|
||||
messages=messages, llm=llm, callbacks=callbacks, i18n=i18n, verbose=verbose
|
||||
printer.print(
|
||||
content="Context length exceeded. Summarizing content to fit the model context window. Might take a while...",
|
||||
color="yellow",
|
||||
)
|
||||
summarize_messages(messages=messages, llm=llm, callbacks=callbacks, i18n=i18n)
|
||||
else:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
|
||||
color="red",
|
||||
)
|
||||
printer.print(
|
||||
content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
|
||||
color="red",
|
||||
)
|
||||
raise SystemExit(
|
||||
"Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
|
||||
)
|
||||
@@ -638,7 +610,6 @@ def summarize_messages(
|
||||
llm: LLM | BaseLLM,
|
||||
callbacks: list[TokenCalcHandler],
|
||||
i18n: I18N,
|
||||
verbose: bool = True,
|
||||
) -> None:
|
||||
"""Summarize messages to fit within context window.
|
||||
|
||||
@@ -670,11 +641,10 @@ def summarize_messages(
|
||||
|
||||
total_groups = len(messages_groups)
|
||||
for idx, group in enumerate(messages_groups, 1):
|
||||
if verbose:
|
||||
Printer().print(
|
||||
content=f"Summarizing {idx}/{total_groups}...",
|
||||
color="yellow",
|
||||
)
|
||||
Printer().print(
|
||||
content=f"Summarizing {idx}/{total_groups}...",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
summarization_messages = [
|
||||
format_message_for_llm(
|
||||
@@ -930,16 +900,13 @@ def extract_tool_call_info(
|
||||
|
||||
|
||||
def _setup_before_llm_call_hooks(
|
||||
executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
|
||||
printer: Printer,
|
||||
verbose: bool = True,
|
||||
executor_context: CrewAgentExecutor | LiteAgent | None, printer: Printer
|
||||
) -> bool:
|
||||
"""Setup and invoke before_llm_call hooks for the executor context.
|
||||
|
||||
Args:
|
||||
executor_context: The executor context to setup the hooks for.
|
||||
printer: Printer instance for error logging.
|
||||
verbose: Whether to print output.
|
||||
|
||||
Returns:
|
||||
True if LLM execution should proceed, False if blocked by a hook.
|
||||
@@ -954,29 +921,26 @@ def _setup_before_llm_call_hooks(
|
||||
for hook in executor_context.before_llm_call_hooks:
|
||||
result = hook(hook_context)
|
||||
if result is False:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="LLM call blocked by before_llm_call hook",
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content="LLM call blocked by before_llm_call hook",
|
||||
color="yellow",
|
||||
)
|
||||
return False
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content=f"Error in before_llm_call hook: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content=f"Error in before_llm_call hook: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
if not isinstance(executor_context.messages, list):
|
||||
if verbose:
|
||||
printer.print(
|
||||
content=(
|
||||
"Warning: before_llm_call hook replaced messages with non-list. "
|
||||
"Restoring original messages list. Hooks should modify messages in-place, "
|
||||
"not replace the list (e.g., use context.messages.append() not context.messages = [])."
|
||||
),
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content=(
|
||||
"Warning: before_llm_call hook replaced messages with non-list. "
|
||||
"Restoring original messages list. Hooks should modify messages in-place, "
|
||||
"not replace the list (e.g., use context.messages.append() not context.messages = [])."
|
||||
),
|
||||
color="yellow",
|
||||
)
|
||||
if isinstance(original_messages, list):
|
||||
executor_context.messages = original_messages
|
||||
else:
|
||||
@@ -986,80 +950,50 @@ def _setup_before_llm_call_hooks(
|
||||
|
||||
|
||||
def _setup_after_llm_call_hooks(
|
||||
executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
|
||||
answer: str | BaseModel,
|
||||
executor_context: CrewAgentExecutor | LiteAgent | None,
|
||||
answer: str,
|
||||
printer: Printer,
|
||||
verbose: bool = True,
|
||||
) -> str | BaseModel:
|
||||
) -> str:
|
||||
"""Setup and invoke after_llm_call hooks for the executor context.
|
||||
|
||||
Args:
|
||||
executor_context: The executor context to setup the hooks for.
|
||||
answer: The LLM response (string or Pydantic model).
|
||||
answer: The LLM response string.
|
||||
printer: Printer instance for error logging.
|
||||
verbose: Whether to print output.
|
||||
|
||||
Returns:
|
||||
The potentially modified response (string or Pydantic model).
|
||||
The potentially modified response string.
|
||||
"""
|
||||
if executor_context and executor_context.after_llm_call_hooks:
|
||||
from crewai.hooks.llm_hooks import LLMCallHookContext
|
||||
|
||||
original_messages = executor_context.messages
|
||||
|
||||
# For Pydantic models, serialize to JSON for hooks
|
||||
if isinstance(answer, BaseModel):
|
||||
pydantic_answer = answer
|
||||
hook_response: str = pydantic_answer.model_dump_json()
|
||||
original_json: str = hook_response
|
||||
else:
|
||||
pydantic_answer = None
|
||||
hook_response = str(answer)
|
||||
|
||||
hook_context = LLMCallHookContext(executor_context, response=hook_response)
|
||||
hook_context = LLMCallHookContext(executor_context, response=answer)
|
||||
try:
|
||||
for hook in executor_context.after_llm_call_hooks:
|
||||
modified_response = hook(hook_context)
|
||||
if modified_response is not None and isinstance(modified_response, str):
|
||||
hook_response = modified_response
|
||||
answer = modified_response
|
||||
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content=f"Error in after_llm_call hook: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content=f"Error in after_llm_call hook: {e}",
|
||||
color="yellow",
|
||||
)
|
||||
|
||||
if not isinstance(executor_context.messages, list):
|
||||
if verbose:
|
||||
printer.print(
|
||||
content=(
|
||||
"Warning: after_llm_call hook replaced messages with non-list. "
|
||||
"Restoring original messages list. Hooks should modify messages in-place, "
|
||||
"not replace the list (e.g., use context.messages.append() not context.messages = [])."
|
||||
),
|
||||
color="yellow",
|
||||
)
|
||||
printer.print(
|
||||
content=(
|
||||
"Warning: after_llm_call hook replaced messages with non-list. "
|
||||
"Restoring original messages list. Hooks should modify messages in-place, "
|
||||
"not replace the list (e.g., use context.messages.append() not context.messages = [])."
|
||||
),
|
||||
color="yellow",
|
||||
)
|
||||
if isinstance(original_messages, list):
|
||||
executor_context.messages = original_messages
|
||||
else:
|
||||
executor_context.messages = []
|
||||
|
||||
# If hooks modified the response, update answer accordingly
|
||||
if pydantic_answer is not None:
|
||||
# For Pydantic models, reparse the JSON if it was modified
|
||||
if hook_response != original_json:
|
||||
try:
|
||||
model_class: type[BaseModel] = type(pydantic_answer)
|
||||
answer = model_class.model_validate_json(hook_response)
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content=f"Warning: Hook modified response but failed to reparse as {type(pydantic_answer).__name__}: {e}. Using original model.",
|
||||
color="yellow",
|
||||
)
|
||||
else:
|
||||
# For string responses, use the hook-modified response
|
||||
answer = hook_response
|
||||
|
||||
return answer
|
||||
|
||||
@@ -62,10 +62,7 @@ class Converter(OutputConverter):
|
||||
],
|
||||
response_model=self.model,
|
||||
)
|
||||
if isinstance(response, BaseModel):
|
||||
result = response
|
||||
else:
|
||||
result = self.model.model_validate_json(response)
|
||||
result = self.model.model_validate_json(response)
|
||||
else:
|
||||
response = self.llm.call(
|
||||
[
|
||||
@@ -208,11 +205,10 @@ def convert_to_model(
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
if agent and getattr(agent, "verbose", True):
|
||||
Printer().print(
|
||||
content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
|
||||
color="red",
|
||||
)
|
||||
Printer().print(
|
||||
content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
|
||||
color="red",
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
@@ -266,11 +262,10 @@ def handle_partial_json(
|
||||
except ValidationError:
|
||||
raise
|
||||
except Exception as e:
|
||||
if agent and getattr(agent, "verbose", True):
|
||||
Printer().print(
|
||||
content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
|
||||
color="red",
|
||||
)
|
||||
Printer().print(
|
||||
content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
|
||||
color="red",
|
||||
)
|
||||
|
||||
return convert_with_instructions(
|
||||
result=result,
|
||||
@@ -328,11 +323,10 @@ def convert_with_instructions(
|
||||
)
|
||||
|
||||
if isinstance(exported_result, ConverterError):
|
||||
if agent and getattr(agent, "verbose", True):
|
||||
Printer().print(
|
||||
content=f"Failed to convert result to model: {exported_result}",
|
||||
color="red",
|
||||
)
|
||||
Printer().print(
|
||||
content=f"Failed to convert result to model: {exported_result}",
|
||||
color="red",
|
||||
)
|
||||
return result
|
||||
|
||||
return exported_result
|
||||
|
||||
@@ -4,7 +4,6 @@ from collections.abc import Generator
|
||||
import contextlib
|
||||
import io
|
||||
import logging
|
||||
import os
|
||||
import warnings
|
||||
|
||||
|
||||
@@ -57,39 +56,3 @@ def suppress_warnings() -> Generator[None, None, None]:
|
||||
"ignore", message="open_text is deprecated*", category=DeprecationWarning
|
||||
)
|
||||
yield
|
||||
|
||||
|
||||
def should_enable_verbose(*, override: bool | None = None) -> bool:
|
||||
"""Determine if verbose logging should be enabled.
|
||||
|
||||
This is the single source of truth for verbose logging enablement.
|
||||
Priority order:
|
||||
1. Explicit override (e.g., Crew.verbose=True/False or Flow.verbose=True/False)
|
||||
2. Environment variable CREWAI_VERBOSE
|
||||
|
||||
Args:
|
||||
override: Explicit override for verbose (True=always enable, False=always disable,
|
||||
None=check environment variable, defaults to True if not set)
|
||||
|
||||
Returns:
|
||||
True if verbose logging should be enabled, False otherwise.
|
||||
|
||||
Example:
|
||||
# Disable verbose logging globally via environment variable
|
||||
export CREWAI_VERBOSE=false
|
||||
|
||||
# Or in code
|
||||
flow = Flow(verbose=False)
|
||||
crew = Crew(verbose=False)
|
||||
"""
|
||||
if override is not None:
|
||||
return override
|
||||
|
||||
env_value = os.getenv("CREWAI_VERBOSE", "").lower()
|
||||
if env_value in ("false", "0"):
|
||||
return False
|
||||
if env_value in ("true", "1"):
|
||||
return True
|
||||
|
||||
# Default to True if not set
|
||||
return True
|
||||
|
||||
@@ -23,13 +23,7 @@ class SystemPromptResult(StandardPromptResult):
|
||||
|
||||
|
||||
COMPONENTS = Literal[
|
||||
"role_playing",
|
||||
"tools",
|
||||
"no_tools",
|
||||
"native_tools",
|
||||
"task",
|
||||
"native_task",
|
||||
"task_no_tools",
|
||||
"role_playing", "tools", "no_tools", "native_tools", "task", "native_task"
|
||||
]
|
||||
|
||||
|
||||
@@ -80,14 +74,11 @@ class Prompts(BaseModel):
|
||||
slices.append("no_tools")
|
||||
system: str = self._build_prompt(slices)
|
||||
|
||||
# Determine which task slice to use:
|
||||
task_slice: COMPONENTS
|
||||
if self.use_native_tool_calling:
|
||||
task_slice = "native_task"
|
||||
elif self.has_tools:
|
||||
task_slice = "task"
|
||||
else:
|
||||
task_slice = "task_no_tools"
|
||||
# Use native_task for native tool calling (no "Thought:" prompt)
|
||||
# Use task for ReAct pattern (includes "Thought:" prompt)
|
||||
task_slice: COMPONENTS = (
|
||||
"native_task" if self.use_native_tool_calling else "task"
|
||||
)
|
||||
slices.append(task_slice)
|
||||
|
||||
if (
|
||||
|
||||
@@ -1,72 +1,14 @@
|
||||
"""Dynamic Pydantic model creation from JSON schemas.
|
||||
|
||||
This module provides utilities for converting JSON schemas to Pydantic models at runtime.
|
||||
The main function is `create_model_from_schema`, which takes a JSON schema and returns
|
||||
a dynamically created Pydantic model class.
|
||||
|
||||
This is used by the A2A server to honor response schemas sent by clients, allowing
|
||||
structured output from agent tasks.
|
||||
|
||||
Based on dydantic (https://github.com/zenbase-ai/dydantic).
|
||||
"""Utilities for generating JSON schemas from Pydantic models.
|
||||
|
||||
This module provides functions for converting Pydantic models to JSON schemas
|
||||
suitable for use with LLMs and tool definitions.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from copy import deepcopy
|
||||
import datetime
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Annotated, Any, Literal, Union
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
from pydantic import (
|
||||
UUID1,
|
||||
UUID3,
|
||||
UUID4,
|
||||
UUID5,
|
||||
AnyUrl,
|
||||
BaseModel,
|
||||
ConfigDict,
|
||||
DirectoryPath,
|
||||
Field,
|
||||
FilePath,
|
||||
FileUrl,
|
||||
HttpUrl,
|
||||
Json,
|
||||
MongoDsn,
|
||||
NewPath,
|
||||
PostgresDsn,
|
||||
SecretBytes,
|
||||
SecretStr,
|
||||
StrictBytes,
|
||||
create_model as create_model_base,
|
||||
)
|
||||
from pydantic.networks import ( # type: ignore[attr-defined]
|
||||
IPv4Address,
|
||||
IPv6Address,
|
||||
IPvAnyAddress,
|
||||
IPvAnyInterface,
|
||||
IPvAnyNetwork,
|
||||
)
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pydantic import EmailStr
|
||||
from pydantic.main import AnyClassMethod
|
||||
else:
|
||||
try:
|
||||
from pydantic import EmailStr
|
||||
except ImportError:
|
||||
logger.warning(
|
||||
"EmailStr unavailable, using str fallback",
|
||||
extra={"missing_package": "email_validator"},
|
||||
)
|
||||
EmailStr = str
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
def resolve_refs(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
@@ -301,319 +243,3 @@ def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
|
||||
"schema": json_schema,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
FORMAT_TYPE_MAP: dict[str, type[Any]] = {
|
||||
"base64": Annotated[bytes, Field(json_schema_extra={"format": "base64"})], # type: ignore[dict-item]
|
||||
"binary": StrictBytes,
|
||||
"date": datetime.date,
|
||||
"time": datetime.time,
|
||||
"date-time": datetime.datetime,
|
||||
"duration": datetime.timedelta,
|
||||
"directory-path": DirectoryPath,
|
||||
"email": EmailStr,
|
||||
"file-path": FilePath,
|
||||
"ipv4": IPv4Address,
|
||||
"ipv6": IPv6Address,
|
||||
"ipvanyaddress": IPvAnyAddress, # type: ignore[dict-item]
|
||||
"ipvanyinterface": IPvAnyInterface, # type: ignore[dict-item]
|
||||
"ipvanynetwork": IPvAnyNetwork, # type: ignore[dict-item]
|
||||
"json-string": Json,
|
||||
"multi-host-uri": PostgresDsn | MongoDsn, # type: ignore[dict-item]
|
||||
"password": SecretStr,
|
||||
"path": NewPath,
|
||||
"uri": AnyUrl,
|
||||
"uuid": uuid.UUID,
|
||||
"uuid1": UUID1,
|
||||
"uuid3": UUID3,
|
||||
"uuid4": UUID4,
|
||||
"uuid5": UUID5,
|
||||
}
|
||||
|
||||
|
||||
def create_model_from_schema( # type: ignore[no-any-unimported]
|
||||
json_schema: dict[str, Any],
|
||||
*,
|
||||
root_schema: dict[str, Any] | None = None,
|
||||
__config__: ConfigDict | None = None,
|
||||
__base__: type[BaseModel] | None = None,
|
||||
__module__: str = __name__,
|
||||
__validators__: dict[str, AnyClassMethod] | None = None,
|
||||
__cls_kwargs__: dict[str, Any] | None = None,
|
||||
) -> type[BaseModel]:
|
||||
"""Create a Pydantic model from a JSON schema.
|
||||
|
||||
This function takes a JSON schema as input and dynamically creates a Pydantic
|
||||
model class based on the schema. It supports various JSON schema features such
|
||||
as nested objects, referenced definitions ($ref), arrays with typed items,
|
||||
union types (anyOf/oneOf), and string formats.
|
||||
|
||||
Args:
|
||||
json_schema: A dictionary representing the JSON schema.
|
||||
root_schema: The root schema containing $defs. If not provided, the
|
||||
current schema is treated as the root schema.
|
||||
__config__: Pydantic configuration for the generated model.
|
||||
__base__: Base class for the generated model. Defaults to BaseModel.
|
||||
__module__: Module name for the generated model class.
|
||||
__validators__: A dictionary of custom validators for the generated model.
|
||||
__cls_kwargs__: Additional keyword arguments for the generated model class.
|
||||
|
||||
Returns:
|
||||
A dynamically created Pydantic model class based on the provided JSON schema.
|
||||
|
||||
Example:
|
||||
>>> schema = {
|
||||
... "title": "Person",
|
||||
... "type": "object",
|
||||
... "properties": {
|
||||
... "name": {"type": "string"},
|
||||
... "age": {"type": "integer"},
|
||||
... },
|
||||
... "required": ["name"],
|
||||
... }
|
||||
>>> Person = create_model_from_schema(schema)
|
||||
>>> person = Person(name="John", age=30)
|
||||
>>> person.name
|
||||
'John'
|
||||
"""
|
||||
effective_root = root_schema or json_schema
|
||||
|
||||
if "allOf" in json_schema:
|
||||
json_schema = _merge_all_of_schemas(json_schema["allOf"], effective_root)
|
||||
if "title" not in json_schema and "title" in (root_schema or {}):
|
||||
json_schema["title"] = (root_schema or {}).get("title")
|
||||
|
||||
model_name = json_schema.get("title", "DynamicModel")
|
||||
field_definitions = {
|
||||
name: _json_schema_to_pydantic_field(
|
||||
name, prop, json_schema.get("required", []), effective_root
|
||||
)
|
||||
for name, prop in (json_schema.get("properties", {}) or {}).items()
|
||||
}
|
||||
|
||||
return create_model_base(
|
||||
model_name,
|
||||
__config__=__config__,
|
||||
__base__=__base__,
|
||||
__module__=__module__,
|
||||
__validators__=__validators__,
|
||||
__cls_kwargs__=__cls_kwargs__,
|
||||
**field_definitions,
|
||||
)
|
||||
|
||||
|
||||
def _json_schema_to_pydantic_field(
|
||||
name: str,
|
||||
json_schema: dict[str, Any],
|
||||
required: list[str],
|
||||
root_schema: dict[str, Any],
|
||||
) -> Any:
|
||||
"""Convert a JSON schema property to a Pydantic field definition.
|
||||
|
||||
Args:
|
||||
name: The field name.
|
||||
json_schema: The JSON schema for this field.
|
||||
required: List of required field names.
|
||||
root_schema: The root schema for resolving $ref.
|
||||
|
||||
Returns:
|
||||
A tuple of (type, Field) for use with create_model.
|
||||
"""
|
||||
type_ = _json_schema_to_pydantic_type(json_schema, root_schema, name_=name.title())
|
||||
description = json_schema.get("description")
|
||||
examples = json_schema.get("examples")
|
||||
is_required = name in required
|
||||
|
||||
field_params: dict[str, Any] = {}
|
||||
schema_extra: dict[str, Any] = {}
|
||||
|
||||
if description:
|
||||
field_params["description"] = description
|
||||
if examples:
|
||||
schema_extra["examples"] = examples
|
||||
|
||||
default = ... if is_required else None
|
||||
|
||||
if isinstance(type_, type) and issubclass(type_, (int, float)):
|
||||
if "minimum" in json_schema:
|
||||
field_params["ge"] = json_schema["minimum"]
|
||||
if "exclusiveMinimum" in json_schema:
|
||||
field_params["gt"] = json_schema["exclusiveMinimum"]
|
||||
if "maximum" in json_schema:
|
||||
field_params["le"] = json_schema["maximum"]
|
||||
if "exclusiveMaximum" in json_schema:
|
||||
field_params["lt"] = json_schema["exclusiveMaximum"]
|
||||
if "multipleOf" in json_schema:
|
||||
field_params["multiple_of"] = json_schema["multipleOf"]
|
||||
|
||||
format_ = json_schema.get("format")
|
||||
if format_ in FORMAT_TYPE_MAP:
|
||||
pydantic_type = FORMAT_TYPE_MAP[format_]
|
||||
|
||||
if format_ == "password":
|
||||
if json_schema.get("writeOnly"):
|
||||
pydantic_type = SecretBytes
|
||||
elif format_ == "uri":
|
||||
allowed_schemes = json_schema.get("scheme")
|
||||
if allowed_schemes:
|
||||
if len(allowed_schemes) == 1 and allowed_schemes[0] == "http":
|
||||
pydantic_type = HttpUrl
|
||||
elif len(allowed_schemes) == 1 and allowed_schemes[0] == "file":
|
||||
pydantic_type = FileUrl
|
||||
|
||||
type_ = pydantic_type
|
||||
|
||||
if isinstance(type_, type) and issubclass(type_, str):
|
||||
if "minLength" in json_schema:
|
||||
field_params["min_length"] = json_schema["minLength"]
|
||||
if "maxLength" in json_schema:
|
||||
field_params["max_length"] = json_schema["maxLength"]
|
||||
if "pattern" in json_schema:
|
||||
field_params["pattern"] = json_schema["pattern"]
|
||||
|
||||
if not is_required:
|
||||
type_ = type_ | None
|
||||
|
||||
if schema_extra:
|
||||
field_params["json_schema_extra"] = schema_extra
|
||||
|
||||
return type_, Field(default, **field_params)
|
||||
|
||||
|
||||
def _resolve_ref(ref: str, root_schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Resolve a $ref to its actual schema.
|
||||
|
||||
Args:
|
||||
ref: The $ref string (e.g., "#/$defs/MyType").
|
||||
root_schema: The root schema containing $defs.
|
||||
|
||||
Returns:
|
||||
The resolved schema dict.
|
||||
"""
|
||||
from typing import cast
|
||||
|
||||
ref_path = ref.split("/")
|
||||
if ref.startswith("#/$defs/"):
|
||||
ref_schema: dict[str, Any] = root_schema["$defs"]
|
||||
start_idx = 2
|
||||
else:
|
||||
ref_schema = root_schema
|
||||
start_idx = 1
|
||||
for path in ref_path[start_idx:]:
|
||||
ref_schema = cast(dict[str, Any], ref_schema[path])
|
||||
return ref_schema
|
||||
|
||||
|
||||
def _merge_all_of_schemas(
|
||||
schemas: list[dict[str, Any]],
|
||||
root_schema: dict[str, Any],
|
||||
) -> dict[str, Any]:
|
||||
"""Merge multiple allOf schemas into a single schema.
|
||||
|
||||
Combines properties and required fields from all schemas.
|
||||
|
||||
Args:
|
||||
schemas: List of schemas to merge.
|
||||
root_schema: The root schema for resolving $ref.
|
||||
|
||||
Returns:
|
||||
Merged schema with combined properties and required fields.
|
||||
"""
|
||||
merged: dict[str, Any] = {"type": "object", "properties": {}, "required": []}
|
||||
|
||||
for schema in schemas:
|
||||
if "$ref" in schema:
|
||||
schema = _resolve_ref(schema["$ref"], root_schema)
|
||||
|
||||
if "properties" in schema:
|
||||
merged["properties"].update(schema["properties"])
|
||||
|
||||
if "required" in schema:
|
||||
for field in schema["required"]:
|
||||
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|
||||
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|
||||
|
||||
if "title" in schema and "title" not in merged:
|
||||
merged["title"] = schema["title"]
|
||||
|
||||
return merged
|
||||
|
||||
|
||||
def _json_schema_to_pydantic_type(
|
||||
json_schema: dict[str, Any],
|
||||
root_schema: dict[str, Any],
|
||||
*,
|
||||
name_: str | None = None,
|
||||
) -> Any:
|
||||
"""Convert a JSON schema to a Python/Pydantic type.
|
||||
|
||||
Args:
|
||||
json_schema: The JSON schema to convert.
|
||||
root_schema: The root schema for resolving $ref.
|
||||
name_: Optional name for nested models.
|
||||
|
||||
Returns:
|
||||
A Python type corresponding to the JSON schema.
|
||||
"""
|
||||
ref = json_schema.get("$ref")
|
||||
if ref:
|
||||
ref_schema = _resolve_ref(ref, root_schema)
|
||||
return _json_schema_to_pydantic_type(ref_schema, root_schema, name_=name_)
|
||||
|
||||
enum_values = json_schema.get("enum")
|
||||
if enum_values:
|
||||
return Literal[tuple(enum_values)]
|
||||
|
||||
if "const" in json_schema:
|
||||
return Literal[json_schema["const"]]
|
||||
|
||||
any_of_schemas = []
|
||||
if "anyOf" in json_schema or "oneOf" in json_schema:
|
||||
any_of_schemas = json_schema.get("anyOf", []) + json_schema.get("oneOf", [])
|
||||
if any_of_schemas:
|
||||
any_of_types = [
|
||||
_json_schema_to_pydantic_type(schema, root_schema)
|
||||
for schema in any_of_schemas
|
||||
]
|
||||
return Union[tuple(any_of_types)] # noqa: UP007
|
||||
|
||||
all_of_schemas = json_schema.get("allOf")
|
||||
if all_of_schemas:
|
||||
if len(all_of_schemas) == 1:
|
||||
return _json_schema_to_pydantic_type(
|
||||
all_of_schemas[0], root_schema, name_=name_
|
||||
)
|
||||
merged = _merge_all_of_schemas(all_of_schemas, root_schema)
|
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return _json_schema_to_pydantic_type(merged, root_schema, name_=name_)
|
||||
|
||||
type_ = json_schema.get("type")
|
||||
|
||||
if type_ == "string":
|
||||
return str
|
||||
if type_ == "integer":
|
||||
return int
|
||||
if type_ == "number":
|
||||
return float
|
||||
if type_ == "boolean":
|
||||
return bool
|
||||
if type_ == "array":
|
||||
items_schema = json_schema.get("items")
|
||||
if items_schema:
|
||||
item_type = _json_schema_to_pydantic_type(
|
||||
items_schema, root_schema, name_=name_
|
||||
)
|
||||
return list[item_type] # type: ignore[valid-type]
|
||||
return list
|
||||
if type_ == "object":
|
||||
properties = json_schema.get("properties")
|
||||
if properties:
|
||||
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|
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|
||||
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|
||||
return create_model_from_schema(json_schema_, root_schema=root_schema)
|
||||
return dict
|
||||
if type_ == "null":
|
||||
return None
|
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if type_ is None:
|
||||
return Any
|
||||
raise ValueError(f"Unsupported JSON schema type: {type_} from {json_schema}")
|
||||
|
||||
@@ -26,5 +26,4 @@ class LLMMessage(TypedDict):
|
||||
tool_call_id: NotRequired[str]
|
||||
name: NotRequired[str]
|
||||
tool_calls: NotRequired[list[dict[str, Any]]]
|
||||
raw_tool_call_parts: NotRequired[list[Any]]
|
||||
files: NotRequired[dict[str, FileInput]]
|
||||
|
||||
@@ -1004,53 +1004,3 @@ def test_prepare_kickoff_param_files_override_message_files():
|
||||
|
||||
assert "files" in inputs
|
||||
assert inputs["files"]["same.png"] is param_file # param takes precedence
|
||||
|
||||
|
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def test_lite_agent_verbose_false_suppresses_printer_output():
|
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"""Test that setting verbose=False suppresses all printer output."""
|
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from crewai.agents.parser import AgentFinish
|
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|
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|
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mock_llm = Mock(spec=LLM)
|
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mock_llm.call.return_value = "Final Answer: Hello!"
|
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mock_llm.stop = []
|
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mock_llm.supports_stop_words.return_value = False
|
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|
||||
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|
||||
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|
||||
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|
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cached_prompt_tokens=0,
|
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|
||||
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|
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|
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with pytest.warns(DeprecationWarning):
|
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agent = LiteAgent(
|
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role="Test Agent",
|
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goal="Test goal",
|
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backstory="Test backstory",
|
||||
llm=mock_llm,
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
result = agent.kickoff("Say hello")
|
||||
|
||||
assert result is not None
|
||||
assert isinstance(result, LiteAgentOutput)
|
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# Verify the printer was never called
|
||||
agent._printer.print = Mock()
|
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# For a clean verification, patch printer before execution
|
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with pytest.warns(DeprecationWarning):
|
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agent2 = LiteAgent(
|
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|
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|
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|
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llm=mock_llm,
|
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|
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|
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|
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|
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agent2._printer = mock_printer
|
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|
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def before_hook(context: ToolCallHookContext) -> bool | None:
|
||||
hook_calls["before"].append({
|
||||
"tool_name": context.tool_name,
|
||||
"tool_input": dict(context.tool_input),
|
||||
"has_agent": context.agent is not None,
|
||||
})
|
||||
return None
|
||||
|
||||
def after_hook(context: ToolCallHookContext) -> str | None:
|
||||
hook_calls["after"].append({
|
||||
"tool_name": context.tool_name,
|
||||
"tool_result": context.tool_result,
|
||||
"has_agent": context.agent is not None,
|
||||
})
|
||||
return None
|
||||
|
||||
register_before_tool_call_hook(before_hook)
|
||||
register_after_tool_call_hook(after_hook)
|
||||
|
||||
try:
|
||||
agent = Agent(
|
||||
role="Calculator",
|
||||
goal="Perform calculations",
|
||||
backstory="You are a calculator assistant",
|
||||
tools=[multiply_numbers],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
agent.kickoff(
|
||||
messages="What is 7 times 6? Use the multiply_numbers tool."
|
||||
)
|
||||
|
||||
# Verify before hook was called
|
||||
assert len(hook_calls["before"]) > 0, "Before hook was never called"
|
||||
before_call = hook_calls["before"][0]
|
||||
assert before_call["tool_name"] == "multiply_numbers"
|
||||
assert "a" in before_call["tool_input"]
|
||||
assert "b" in before_call["tool_input"]
|
||||
assert before_call["has_agent"] is True
|
||||
|
||||
# Verify after hook was called
|
||||
assert len(hook_calls["after"]) > 0, "After hook was never called"
|
||||
after_call = hook_calls["after"][0]
|
||||
assert after_call["tool_name"] == "multiply_numbers"
|
||||
assert "42" in str(after_call["tool_result"])
|
||||
assert after_call["has_agent"] is True
|
||||
|
||||
finally:
|
||||
unregister_before_tool_call_hook(before_hook)
|
||||
unregister_after_tool_call_hook(after_hook)
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_crew_native_tool_hooks_before_and_after(self):
|
||||
"""Test that Crew with Agent executes before/after hooks with full context."""
|
||||
import os
|
||||
from crewai import Agent, Crew, Task
|
||||
from crewai.tools import tool
|
||||
|
||||
|
||||
hook_calls = {"before": [], "after": []}
|
||||
|
||||
@tool("divide_numbers")
|
||||
def divide_numbers(a: int, b: int) -> float:
|
||||
"""Divide first number by second number."""
|
||||
return a / b
|
||||
|
||||
def before_hook(context: ToolCallHookContext) -> bool | None:
|
||||
hook_calls["before"].append({
|
||||
"tool_name": context.tool_name,
|
||||
"tool_input": dict(context.tool_input),
|
||||
"has_agent": context.agent is not None,
|
||||
"has_task": context.task is not None,
|
||||
"has_crew": context.crew is not None,
|
||||
"agent_role": context.agent.role if context.agent else None,
|
||||
})
|
||||
return None
|
||||
|
||||
def after_hook(context: ToolCallHookContext) -> str | None:
|
||||
hook_calls["after"].append({
|
||||
"tool_name": context.tool_name,
|
||||
"tool_result": context.tool_result,
|
||||
"has_agent": context.agent is not None,
|
||||
"has_task": context.task is not None,
|
||||
"has_crew": context.crew is not None,
|
||||
})
|
||||
return None
|
||||
|
||||
register_before_tool_call_hook(before_hook)
|
||||
register_after_tool_call_hook(after_hook)
|
||||
|
||||
try:
|
||||
agent = Agent(
|
||||
role="Math Assistant",
|
||||
goal="Perform division calculations accurately",
|
||||
backstory="You are a math assistant that helps with division",
|
||||
tools=[divide_numbers],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Calculate 100 divided by 4 using the divide_numbers tool.",
|
||||
expected_output="The result of the division",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
crew.kickoff()
|
||||
|
||||
# Verify before hook was called with full context
|
||||
assert len(hook_calls["before"]) > 0, "Before hook was never called"
|
||||
before_call = hook_calls["before"][0]
|
||||
assert before_call["tool_name"] == "divide_numbers"
|
||||
assert "a" in before_call["tool_input"]
|
||||
assert "b" in before_call["tool_input"]
|
||||
assert before_call["has_agent"] is True
|
||||
assert before_call["has_task"] is True
|
||||
assert before_call["has_crew"] is True
|
||||
assert before_call["agent_role"] == "Math Assistant"
|
||||
|
||||
# Verify after hook was called with full context
|
||||
assert len(hook_calls["after"]) > 0, "After hook was never called"
|
||||
after_call = hook_calls["after"][0]
|
||||
assert after_call["tool_name"] == "divide_numbers"
|
||||
assert "25" in str(after_call["tool_result"])
|
||||
assert after_call["has_agent"] is True
|
||||
assert after_call["has_task"] is True
|
||||
assert after_call["has_crew"] is True
|
||||
|
||||
finally:
|
||||
unregister_before_tool_call_hook(before_hook)
|
||||
unregister_after_tool_call_hook(after_hook)
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_before_hook_blocks_tool_execution_in_crew(self):
|
||||
"""Test that returning False from before hook blocks tool execution."""
|
||||
import os
|
||||
from crewai import Agent, Crew, Task
|
||||
from crewai.tools import tool
|
||||
|
||||
hook_calls = {"before": [], "after": [], "tool_executed": False}
|
||||
|
||||
@tool("dangerous_operation")
|
||||
def dangerous_operation(action: str) -> str:
|
||||
"""Perform a dangerous operation that should be blocked."""
|
||||
hook_calls["tool_executed"] = True
|
||||
return f"Executed: {action}"
|
||||
|
||||
def blocking_before_hook(context: ToolCallHookContext) -> bool | None:
|
||||
hook_calls["before"].append({
|
||||
"tool_name": context.tool_name,
|
||||
"tool_input": dict(context.tool_input),
|
||||
})
|
||||
# Block all calls to dangerous_operation
|
||||
if context.tool_name == "dangerous_operation":
|
||||
return False
|
||||
return None
|
||||
|
||||
def after_hook(context: ToolCallHookContext) -> str | None:
|
||||
hook_calls["after"].append({
|
||||
"tool_name": context.tool_name,
|
||||
"tool_result": context.tool_result,
|
||||
})
|
||||
return None
|
||||
|
||||
register_before_tool_call_hook(blocking_before_hook)
|
||||
register_after_tool_call_hook(after_hook)
|
||||
|
||||
try:
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="Try to use the dangerous operation tool",
|
||||
backstory="You are a test agent",
|
||||
tools=[dangerous_operation],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Use the dangerous_operation tool with action 'delete_all'.",
|
||||
expected_output="The result of the operation",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
crew.kickoff()
|
||||
|
||||
# Verify before hook was called
|
||||
assert len(hook_calls["before"]) > 0, "Before hook was never called"
|
||||
before_call = hook_calls["before"][0]
|
||||
assert before_call["tool_name"] == "dangerous_operation"
|
||||
|
||||
# Verify the actual tool function was NOT executed
|
||||
assert hook_calls["tool_executed"] is False, "Tool should have been blocked"
|
||||
|
||||
# Verify after hook was still called (with blocked message)
|
||||
assert len(hook_calls["after"]) > 0, "After hook was never called"
|
||||
after_call = hook_calls["after"][0]
|
||||
assert "blocked" in after_call["tool_result"].lower()
|
||||
|
||||
finally:
|
||||
unregister_before_tool_call_hook(blocking_before_hook)
|
||||
unregister_after_tool_call_hook(after_hook)
|
||||
|
||||
@@ -157,10 +157,10 @@ async def test_anthropic_async_with_response_model():
|
||||
"Say hello in French",
|
||||
response_model=GreetingResponse
|
||||
)
|
||||
# When response_model is provided, the result is already a parsed Pydantic model instance
|
||||
assert isinstance(result, GreetingResponse)
|
||||
assert isinstance(result.greeting, str)
|
||||
assert isinstance(result.language, str)
|
||||
model = GreetingResponse.model_validate_json(result)
|
||||
assert isinstance(model, GreetingResponse)
|
||||
assert isinstance(model.greeting, str)
|
||||
assert isinstance(model.language, str)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
||||
@@ -799,131 +799,3 @@ def test_google_express_mode_works() -> None:
|
||||
assert result.token_usage.prompt_tokens > 0
|
||||
assert result.token_usage.completion_tokens > 0
|
||||
assert result.token_usage.successful_requests >= 1
|
||||
|
||||
|
||||
def test_gemini_2_0_model_detection():
|
||||
"""Test that Gemini 2.0 models are properly detected."""
|
||||
# Test Gemini 2.0 models
|
||||
llm_2_0 = LLM(model="google/gemini-2.0-flash-001")
|
||||
from crewai.llms.providers.gemini.completion import GeminiCompletion
|
||||
assert isinstance(llm_2_0, GeminiCompletion)
|
||||
assert llm_2_0.is_gemini_2_0 is True
|
||||
|
||||
llm_2_5 = LLM(model="google/gemini-2.5-flash")
|
||||
assert isinstance(llm_2_5, GeminiCompletion)
|
||||
assert llm_2_5.is_gemini_2_0 is True
|
||||
|
||||
# Test non-2.0 models
|
||||
llm_1_5 = LLM(model="google/gemini-1.5-pro")
|
||||
assert isinstance(llm_1_5, GeminiCompletion)
|
||||
assert llm_1_5.is_gemini_2_0 is False
|
||||
|
||||
|
||||
def test_add_property_ordering_to_schema():
|
||||
"""Test that _add_property_ordering correctly adds propertyOrdering to schemas."""
|
||||
from crewai.llms.providers.gemini.completion import GeminiCompletion
|
||||
|
||||
# Test simple object schema
|
||||
simple_schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {"type": "string"},
|
||||
"age": {"type": "integer"},
|
||||
"email": {"type": "string"}
|
||||
}
|
||||
}
|
||||
|
||||
result = GeminiCompletion._add_property_ordering(simple_schema)
|
||||
|
||||
assert "propertyOrdering" in result
|
||||
assert result["propertyOrdering"] == ["name", "age", "email"]
|
||||
|
||||
# Test nested object schema
|
||||
nested_schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"user": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {"type": "string"},
|
||||
"contact": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"email": {"type": "string"},
|
||||
"phone": {"type": "string"}
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"id": {"type": "integer"}
|
||||
}
|
||||
}
|
||||
|
||||
result = GeminiCompletion._add_property_ordering(nested_schema)
|
||||
|
||||
assert "propertyOrdering" in result
|
||||
assert result["propertyOrdering"] == ["user", "id"]
|
||||
assert "propertyOrdering" in result["properties"]["user"]
|
||||
assert result["properties"]["user"]["propertyOrdering"] == ["name", "contact"]
|
||||
assert "propertyOrdering" in result["properties"]["user"]["properties"]["contact"]
|
||||
assert result["properties"]["user"]["properties"]["contact"]["propertyOrdering"] == ["email", "phone"]
|
||||
|
||||
|
||||
def test_gemini_2_0_response_model_with_property_ordering():
|
||||
"""Test that Gemini 2.0 models include propertyOrdering in response schemas."""
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
class TestResponse(BaseModel):
|
||||
"""Test response model."""
|
||||
name: str = Field(..., description="The name")
|
||||
age: int = Field(..., description="The age")
|
||||
email: str = Field(..., description="The email")
|
||||
|
||||
llm = LLM(model="google/gemini-2.0-flash-001")
|
||||
|
||||
# Prepare generation config with response model
|
||||
config = llm._prepare_generation_config(response_model=TestResponse)
|
||||
|
||||
# Verify that the config has response_json_schema
|
||||
assert hasattr(config, 'response_json_schema') or 'response_json_schema' in config.__dict__
|
||||
|
||||
# Get the schema
|
||||
if hasattr(config, 'response_json_schema'):
|
||||
schema = config.response_json_schema
|
||||
else:
|
||||
schema = config.__dict__.get('response_json_schema', {})
|
||||
|
||||
# Verify propertyOrdering is present for Gemini 2.0
|
||||
assert "propertyOrdering" in schema
|
||||
assert "name" in schema["propertyOrdering"]
|
||||
assert "age" in schema["propertyOrdering"]
|
||||
assert "email" in schema["propertyOrdering"]
|
||||
|
||||
|
||||
def test_gemini_1_5_response_model_uses_response_schema():
|
||||
"""Test that Gemini 1.5 models use response_schema parameter (not response_json_schema)."""
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
class TestResponse(BaseModel):
|
||||
"""Test response model."""
|
||||
name: str = Field(..., description="The name")
|
||||
age: int = Field(..., description="The age")
|
||||
|
||||
llm = LLM(model="google/gemini-1.5-pro")
|
||||
|
||||
# Prepare generation config with response model
|
||||
config = llm._prepare_generation_config(response_model=TestResponse)
|
||||
|
||||
# Verify that the config uses response_schema (not response_json_schema)
|
||||
assert hasattr(config, 'response_schema') or 'response_schema' in config.__dict__
|
||||
assert not (hasattr(config, 'response_json_schema') and config.response_json_schema is not None)
|
||||
|
||||
# Get the schema
|
||||
if hasattr(config, 'response_schema'):
|
||||
schema = config.response_schema
|
||||
else:
|
||||
schema = config.__dict__.get('response_schema')
|
||||
|
||||
# For Gemini 1.5, response_schema should be the Pydantic model itself
|
||||
# The SDK handles conversion internally
|
||||
assert schema is TestResponse or isinstance(schema, type)
|
||||
|
||||
@@ -540,9 +540,7 @@ def test_openai_streaming_with_response_model():
|
||||
result = llm.call("Test question", response_model=TestResponse)
|
||||
|
||||
assert result is not None
|
||||
assert isinstance(result, TestResponse)
|
||||
assert result.answer == "test"
|
||||
assert result.confidence == 0.95
|
||||
assert isinstance(result, str)
|
||||
|
||||
assert mock_stream.called
|
||||
call_kwargs = mock_stream.call_args[1]
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import threading
|
||||
from unittest.mock import patch
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from crewai import Agent, Crew, Task
|
||||
@@ -121,3 +121,90 @@ def test_telemetry_singleton_pattern():
|
||||
thread.join()
|
||||
|
||||
assert all(instance is telemetry1 for instance in instances)
|
||||
|
||||
|
||||
def test_signal_handler_registration_skipped_in_non_main_thread():
|
||||
"""Test that signal handler registration is skipped when running from a non-main thread.
|
||||
|
||||
This test verifies that when Telemetry is initialized from a non-main thread,
|
||||
the signal handler registration is skipped without raising noisy ValueError tracebacks.
|
||||
See: https://github.com/crewAIInc/crewAI/issues/4289
|
||||
"""
|
||||
Telemetry._instance = None
|
||||
|
||||
result = {"register_signal_handler_called": False, "error": None}
|
||||
|
||||
def init_telemetry_in_thread():
|
||||
try:
|
||||
with patch("crewai.telemetry.telemetry.TracerProvider"):
|
||||
with patch.object(
|
||||
Telemetry,
|
||||
"_register_signal_handler",
|
||||
wraps=lambda *args, **kwargs: None,
|
||||
) as mock_register:
|
||||
telemetry = Telemetry()
|
||||
result["register_signal_handler_called"] = mock_register.called
|
||||
result["telemetry"] = telemetry
|
||||
except Exception as e:
|
||||
result["error"] = e
|
||||
|
||||
thread = threading.Thread(target=init_telemetry_in_thread)
|
||||
thread.start()
|
||||
thread.join()
|
||||
|
||||
assert result["error"] is None, f"Unexpected error: {result['error']}"
|
||||
assert (
|
||||
result["register_signal_handler_called"] is False
|
||||
), "Signal handler should not be registered in non-main thread"
|
||||
|
||||
|
||||
def test_signal_handler_registration_skipped_logs_debug_message():
|
||||
"""Test that a debug message is logged when signal handler registration is skipped.
|
||||
|
||||
This test verifies that when Telemetry is initialized from a non-main thread,
|
||||
a debug message is logged indicating that signal handler registration was skipped.
|
||||
"""
|
||||
Telemetry._instance = None
|
||||
|
||||
result = {"telemetry": None, "error": None, "debug_calls": []}
|
||||
|
||||
mock_logger_debug = MagicMock()
|
||||
|
||||
def init_telemetry_in_thread():
|
||||
try:
|
||||
with patch("crewai.telemetry.telemetry.TracerProvider"):
|
||||
with patch(
|
||||
"crewai.telemetry.telemetry.logger.debug", mock_logger_debug
|
||||
):
|
||||
result["telemetry"] = Telemetry()
|
||||
result["debug_calls"] = [
|
||||
str(call) for call in mock_logger_debug.call_args_list
|
||||
]
|
||||
except Exception as e:
|
||||
result["error"] = e
|
||||
|
||||
thread = threading.Thread(target=init_telemetry_in_thread)
|
||||
thread.start()
|
||||
thread.join()
|
||||
|
||||
assert result["error"] is None, f"Unexpected error: {result['error']}"
|
||||
assert result["telemetry"] is not None
|
||||
|
||||
debug_calls = result["debug_calls"]
|
||||
assert any(
|
||||
"Skipping signal handler registration" in call for call in debug_calls
|
||||
), f"Expected debug message about skipping signal handler registration, got: {debug_calls}"
|
||||
|
||||
|
||||
def test_signal_handlers_registered_in_main_thread():
|
||||
"""Test that signal handlers are registered when running from the main thread."""
|
||||
Telemetry._instance = None
|
||||
|
||||
with patch("crewai.telemetry.telemetry.TracerProvider"):
|
||||
with patch(
|
||||
"crewai.telemetry.telemetry.Telemetry._register_signal_handler"
|
||||
) as mock_register:
|
||||
telemetry = Telemetry()
|
||||
|
||||
assert telemetry.ready is True
|
||||
assert mock_register.call_count >= 2
|
||||
|
||||
@@ -2585,7 +2585,6 @@ def test_warning_long_term_memory_without_entity_memory():
|
||||
goal="You research about math.",
|
||||
backstory="You're an expert in research and you love to learn new things.",
|
||||
allow_delegation=False,
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
task1 = Task(
|
||||
|
||||
@@ -1,258 +0,0 @@
|
||||
"""Test verbose control for Flow and Crew."""
|
||||
|
||||
import os
|
||||
from unittest.mock import patch
|
||||
|
||||
from crewai.events.event_listener import EventListener
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.utilities.logger_utils import should_enable_verbose
|
||||
|
||||
|
||||
class TestShouldEnableVerbose:
|
||||
"""Test the should_enable_verbose utility function."""
|
||||
|
||||
def test_override_true_returns_true(self):
|
||||
"""Test that explicit override=True always returns True."""
|
||||
assert should_enable_verbose(override=True) is True
|
||||
|
||||
def test_override_false_returns_false(self):
|
||||
"""Test that explicit override=False always returns False."""
|
||||
assert should_enable_verbose(override=False) is False
|
||||
|
||||
def test_env_var_false_disables_verbose(self):
|
||||
"""Test that CREWAI_VERBOSE=false disables verbose."""
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "false"}):
|
||||
assert should_enable_verbose() is False
|
||||
|
||||
def test_env_var_0_disables_verbose(self):
|
||||
"""Test that CREWAI_VERBOSE=0 disables verbose."""
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "0"}):
|
||||
assert should_enable_verbose() is False
|
||||
|
||||
def test_env_var_true_enables_verbose(self):
|
||||
"""Test that CREWAI_VERBOSE=true enables verbose."""
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "true"}):
|
||||
assert should_enable_verbose() is True
|
||||
|
||||
def test_env_var_1_enables_verbose(self):
|
||||
"""Test that CREWAI_VERBOSE=1 enables verbose."""
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "1"}):
|
||||
assert should_enable_verbose() is True
|
||||
|
||||
def test_no_env_var_defaults_to_true(self):
|
||||
"""Test that no CREWAI_VERBOSE env var defaults to True."""
|
||||
with patch.dict(os.environ, {}, clear=True):
|
||||
# Remove CREWAI_VERBOSE if it exists
|
||||
os.environ.pop("CREWAI_VERBOSE", None)
|
||||
assert should_enable_verbose() is True
|
||||
|
||||
def test_override_takes_precedence_over_env_var(self):
|
||||
"""Test that explicit override takes precedence over env var."""
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "false"}):
|
||||
assert should_enable_verbose(override=True) is True
|
||||
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "true"}):
|
||||
assert should_enable_verbose(override=False) is False
|
||||
|
||||
|
||||
class TestFlowVerboseControl:
|
||||
"""Test verbose control in Flow class."""
|
||||
|
||||
def test_flow_verbose_default_is_true(self):
|
||||
"""Test that Flow verbose defaults to True when no env var is set."""
|
||||
# Remove CREWAI_VERBOSE if it exists
|
||||
os.environ.pop("CREWAI_VERBOSE", None)
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def step_1(self):
|
||||
return "done"
|
||||
|
||||
flow = SimpleFlow()
|
||||
assert flow.verbose is True
|
||||
|
||||
def test_flow_verbose_false_disables_logging(self):
|
||||
"""Test that Flow with verbose=False disables logging."""
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def step_1(self):
|
||||
return "done"
|
||||
|
||||
flow = SimpleFlow(verbose=False)
|
||||
assert flow.verbose is False
|
||||
|
||||
# Verify EventListener is also set to verbose=False
|
||||
event_listener = EventListener()
|
||||
assert event_listener.verbose is False
|
||||
assert event_listener.formatter.verbose is False
|
||||
|
||||
def test_flow_verbose_true_enables_logging(self):
|
||||
"""Test that Flow with verbose=True enables logging."""
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def step_1(self):
|
||||
return "done"
|
||||
|
||||
flow = SimpleFlow(verbose=True)
|
||||
assert flow.verbose is True
|
||||
|
||||
# Verify EventListener is also set to verbose=True
|
||||
event_listener = EventListener()
|
||||
assert event_listener.verbose is True
|
||||
assert event_listener.formatter.verbose is True
|
||||
|
||||
def test_flow_respects_env_var_false(self):
|
||||
"""Test that Flow respects CREWAI_VERBOSE=false env var."""
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def step_1(self):
|
||||
return "done"
|
||||
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "false"}, clear=False):
|
||||
flow = SimpleFlow()
|
||||
assert flow.verbose is False
|
||||
|
||||
def test_flow_respects_env_var_true(self):
|
||||
"""Test that Flow respects CREWAI_VERBOSE=true env var."""
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def step_1(self):
|
||||
return "done"
|
||||
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "true"}, clear=False):
|
||||
flow = SimpleFlow()
|
||||
assert flow.verbose is True
|
||||
|
||||
def test_flow_explicit_verbose_overrides_env_var(self):
|
||||
"""Test that explicit verbose parameter overrides env var."""
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def step_1(self):
|
||||
return "done"
|
||||
|
||||
# Explicit verbose=True overrides CREWAI_VERBOSE=false
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "false"}, clear=False):
|
||||
flow = SimpleFlow(verbose=True)
|
||||
assert flow.verbose is True
|
||||
|
||||
# Explicit verbose=False overrides CREWAI_VERBOSE=true
|
||||
with patch.dict(os.environ, {"CREWAI_VERBOSE": "true"}, clear=False):
|
||||
flow = SimpleFlow(verbose=False)
|
||||
assert flow.verbose is False
|
||||
|
||||
|
||||
class TestFlowVerboseExecution:
|
||||
"""Test that verbose setting actually suppresses output during Flow execution."""
|
||||
|
||||
def test_flow_verbose_false_suppresses_console_output(self):
|
||||
"""Test that Flow with verbose=False suppresses console output."""
|
||||
execution_order = []
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def step_1(self):
|
||||
execution_order.append("step_1")
|
||||
return "step_1_done"
|
||||
|
||||
@listen(step_1)
|
||||
def step_2(self):
|
||||
execution_order.append("step_2")
|
||||
return "step_2_done"
|
||||
|
||||
# Create flow with verbose=False
|
||||
flow = SimpleFlow(verbose=False)
|
||||
|
||||
# Verify the formatter's verbose is False
|
||||
event_listener = EventListener()
|
||||
assert event_listener.formatter.verbose is False
|
||||
|
||||
# Execute the flow
|
||||
result = flow.kickoff()
|
||||
|
||||
# Flow should still execute correctly
|
||||
assert execution_order == ["step_1", "step_2"]
|
||||
assert result == "step_2_done"
|
||||
|
||||
def test_flow_verbose_true_allows_console_output(self):
|
||||
"""Test that Flow with verbose=True allows console output."""
|
||||
execution_order = []
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def step_1(self):
|
||||
execution_order.append("step_1")
|
||||
return "step_1_done"
|
||||
|
||||
@listen(step_1)
|
||||
def step_2(self):
|
||||
execution_order.append("step_2")
|
||||
return "step_2_done"
|
||||
|
||||
# Create flow with verbose=True
|
||||
flow = SimpleFlow(verbose=True)
|
||||
|
||||
# Verify the formatter's verbose is True
|
||||
event_listener = EventListener()
|
||||
assert event_listener.formatter.verbose is True
|
||||
|
||||
# Execute the flow
|
||||
result = flow.kickoff()
|
||||
|
||||
# Flow should execute correctly
|
||||
assert execution_order == ["step_1", "step_2"]
|
||||
assert result == "step_2_done"
|
||||
|
||||
|
||||
class TestConsoleFormatterVerbose:
|
||||
"""Test that ConsoleFormatter respects verbose setting."""
|
||||
|
||||
def test_console_formatter_print_panel_respects_verbose_false(self):
|
||||
"""Test that print_panel does not print when verbose=False."""
|
||||
from rich.text import Text
|
||||
from crewai.events.utils.console_formatter import ConsoleFormatter
|
||||
|
||||
formatter = ConsoleFormatter(verbose=False)
|
||||
|
||||
# Create a mock to capture print calls
|
||||
with patch.object(formatter, "print") as mock_print:
|
||||
content = Text("Test content")
|
||||
formatter.print_panel(content, "Test Title", "blue", is_flow=True)
|
||||
|
||||
# print should not be called when verbose=False
|
||||
mock_print.assert_not_called()
|
||||
|
||||
def test_console_formatter_print_panel_respects_verbose_true(self):
|
||||
"""Test that print_panel prints when verbose=True."""
|
||||
from rich.text import Text
|
||||
from crewai.events.utils.console_formatter import ConsoleFormatter
|
||||
|
||||
formatter = ConsoleFormatter(verbose=True)
|
||||
|
||||
# Create a mock to capture print calls
|
||||
with patch.object(formatter, "print") as mock_print:
|
||||
content = Text("Test content")
|
||||
formatter.print_panel(content, "Test Title", "blue", is_flow=True)
|
||||
|
||||
# print should be called when verbose=True
|
||||
assert mock_print.call_count >= 1
|
||||
|
||||
def test_console_formatter_flow_events_respect_verbose_false(self):
|
||||
"""Test that flow events are suppressed when verbose=False."""
|
||||
from rich.text import Text
|
||||
from crewai.events.utils.console_formatter import ConsoleFormatter
|
||||
|
||||
formatter = ConsoleFormatter(verbose=False)
|
||||
|
||||
# Create a mock to capture print calls
|
||||
with patch.object(formatter, "print") as mock_print:
|
||||
content = Text("Flow event content")
|
||||
# is_flow=True should still respect verbose=False
|
||||
formatter.print_panel(content, "Flow Event", "blue", is_flow=True)
|
||||
|
||||
# print should not be called even for flow events when verbose=False
|
||||
mock_print.assert_not_called()
|
||||
@@ -1,234 +0,0 @@
|
||||
"""Tests for prompt generation to prevent thought leakage.
|
||||
|
||||
These tests verify that:
|
||||
1. Agents without tools don't get ReAct format instructions
|
||||
2. The generated prompts don't encourage "Thought:" prefixes that leak into output
|
||||
3. Real LLM calls produce clean output without internal reasoning
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from crewai import Agent, Crew, Task
|
||||
from crewai.llm import LLM
|
||||
from crewai.utilities.prompts import Prompts
|
||||
|
||||
|
||||
class TestNoToolsPromptGeneration:
|
||||
"""Tests for prompt generation when agent has no tools."""
|
||||
|
||||
def test_no_tools_uses_task_no_tools_slice(self) -> None:
|
||||
"""Test that agents without tools use task_no_tools slice instead of task."""
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.role = "Test Agent"
|
||||
mock_agent.goal = "Test goal"
|
||||
mock_agent.backstory = "Test backstory"
|
||||
|
||||
prompts = Prompts(
|
||||
has_tools=False,
|
||||
use_native_tool_calling=False,
|
||||
use_system_prompt=True,
|
||||
agent=mock_agent,
|
||||
)
|
||||
|
||||
result = prompts.task_execution()
|
||||
|
||||
# Verify it's a SystemPromptResult with system and user keys
|
||||
assert "system" in result
|
||||
assert "user" in result
|
||||
assert "prompt" in result
|
||||
|
||||
# The user prompt should NOT contain "Thought:" (ReAct format)
|
||||
assert "Thought:" not in result["user"]
|
||||
|
||||
# The user prompt should NOT mention tools
|
||||
assert "use the tools available" not in result["user"]
|
||||
assert "tools available" not in result["user"].lower()
|
||||
|
||||
# The system prompt should NOT contain ReAct format instructions
|
||||
assert "Thought:" not in result["system"]
|
||||
assert "Final Answer:" not in result["system"]
|
||||
|
||||
def test_no_tools_prompt_is_simple(self) -> None:
|
||||
"""Test that no-tools prompt is simple and direct."""
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.role = "Language Detector"
|
||||
mock_agent.goal = "Detect language"
|
||||
mock_agent.backstory = "Expert linguist"
|
||||
|
||||
prompts = Prompts(
|
||||
has_tools=False,
|
||||
use_native_tool_calling=False,
|
||||
use_system_prompt=True,
|
||||
agent=mock_agent,
|
||||
)
|
||||
|
||||
result = prompts.task_execution()
|
||||
|
||||
# Should contain the role playing info
|
||||
assert "Language Detector" in result["system"]
|
||||
|
||||
# User prompt should be simple with just the task
|
||||
assert "Current Task:" in result["user"]
|
||||
assert "Provide your complete response:" in result["user"]
|
||||
|
||||
def test_with_tools_uses_task_slice_with_react(self) -> None:
|
||||
"""Test that agents WITH tools use the task slice (ReAct format)."""
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.role = "Test Agent"
|
||||
mock_agent.goal = "Test goal"
|
||||
mock_agent.backstory = "Test backstory"
|
||||
|
||||
prompts = Prompts(
|
||||
has_tools=True,
|
||||
use_native_tool_calling=False,
|
||||
use_system_prompt=True,
|
||||
agent=mock_agent,
|
||||
)
|
||||
|
||||
result = prompts.task_execution()
|
||||
|
||||
# With tools and ReAct, the prompt SHOULD contain Thought:
|
||||
assert "Thought:" in result["user"]
|
||||
|
||||
def test_native_tools_uses_native_task_slice(self) -> None:
|
||||
"""Test that native tool calling uses native_task slice."""
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.role = "Test Agent"
|
||||
mock_agent.goal = "Test goal"
|
||||
mock_agent.backstory = "Test backstory"
|
||||
|
||||
prompts = Prompts(
|
||||
has_tools=True,
|
||||
use_native_tool_calling=True,
|
||||
use_system_prompt=True,
|
||||
agent=mock_agent,
|
||||
)
|
||||
|
||||
result = prompts.task_execution()
|
||||
|
||||
# Native tool calling should NOT have Thought: in user prompt
|
||||
assert "Thought:" not in result["user"]
|
||||
|
||||
# Should NOT have emotional manipulation
|
||||
assert "your job depends on it" not in result["user"]
|
||||
|
||||
|
||||
class TestNoThoughtLeakagePatterns:
|
||||
"""Tests to verify prompts don't encourage thought leakage."""
|
||||
|
||||
def test_no_job_depends_on_it_in_no_tools(self) -> None:
|
||||
"""Test that 'your job depends on it' is not in no-tools prompts."""
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.role = "Test"
|
||||
mock_agent.goal = "Test"
|
||||
mock_agent.backstory = "Test"
|
||||
|
||||
prompts = Prompts(
|
||||
has_tools=False,
|
||||
use_native_tool_calling=False,
|
||||
use_system_prompt=True,
|
||||
agent=mock_agent,
|
||||
)
|
||||
|
||||
result = prompts.task_execution()
|
||||
|
||||
full_prompt = result["prompt"]
|
||||
assert "your job depends on it" not in full_prompt.lower()
|
||||
assert "i must use these formats" not in full_prompt.lower()
|
||||
|
||||
def test_no_job_depends_on_it_in_native_task(self) -> None:
|
||||
"""Test that 'your job depends on it' is not in native task prompts."""
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.role = "Test"
|
||||
mock_agent.goal = "Test"
|
||||
mock_agent.backstory = "Test"
|
||||
|
||||
prompts = Prompts(
|
||||
has_tools=True,
|
||||
use_native_tool_calling=True,
|
||||
use_system_prompt=True,
|
||||
agent=mock_agent,
|
||||
)
|
||||
|
||||
result = prompts.task_execution()
|
||||
|
||||
full_prompt = result["prompt"]
|
||||
assert "your job depends on it" not in full_prompt.lower()
|
||||
|
||||
|
||||
class TestRealLLMNoThoughtLeakage:
|
||||
"""Integration tests with real LLM calls to verify no thought leakage."""
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_agent_without_tools_no_thought_in_output(self) -> None:
|
||||
"""Test that agent without tools produces clean output without 'Thought:' prefix."""
|
||||
agent = Agent(
|
||||
role="Language Detector",
|
||||
goal="Detect the language of text",
|
||||
backstory="You are an expert linguist who can identify languages.",
|
||||
tools=[], # No tools
|
||||
llm=LLM(model="gpt-4o-mini"),
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="What language is this text written in: 'Hello, how are you?'",
|
||||
expected_output="The detected language (e.g., English, Spanish, etc.)",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
result = crew.kickoff()
|
||||
|
||||
assert result is not None
|
||||
assert result.raw is not None
|
||||
|
||||
# The output should NOT start with "Thought:" or contain ReAct artifacts
|
||||
output = str(result.raw)
|
||||
assert not output.strip().startswith("Thought:")
|
||||
assert "Final Answer:" not in output
|
||||
assert "I now can give a great answer" not in output
|
||||
|
||||
# Should contain an actual answer about the language
|
||||
assert any(
|
||||
lang in output.lower()
|
||||
for lang in ["english", "en", "language"]
|
||||
)
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_simple_task_clean_output(self) -> None:
|
||||
"""Test that a simple task produces clean output without internal reasoning."""
|
||||
agent = Agent(
|
||||
role="Classifier",
|
||||
goal="Classify text sentiment",
|
||||
backstory="You classify text sentiment accurately.",
|
||||
tools=[],
|
||||
llm=LLM(model="gpt-4o-mini"),
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Classify the sentiment of: 'I love this product!'",
|
||||
expected_output="One word: positive, negative, or neutral",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
result = crew.kickoff()
|
||||
|
||||
assert result is not None
|
||||
output = str(result.raw).strip().lower()
|
||||
|
||||
# Output should be clean - just the classification
|
||||
assert not output.startswith("thought:")
|
||||
assert "final answer:" not in output
|
||||
|
||||
# Should contain the actual classification
|
||||
assert any(
|
||||
sentiment in output
|
||||
for sentiment in ["positive", "negative", "neutral"]
|
||||
)
|
||||
@@ -1,3 +1,3 @@
|
||||
"""CrewAI development tools."""
|
||||
|
||||
__version__ = "1.9.2"
|
||||
__version__ = "1.9.0"
|
||||
|
||||
Reference in New Issue
Block a user