Getting Started with ADK Provider

from kubiya_workflow_sdk.providers import get_provider

# Initialize ADK provider
adk = get_provider("adk")

# Generate a workflow from natural language
result = await adk.compose(
    task="Create a workflow that checks disk space and sends an alert if usage is above 80%",
    mode="plan"  # Just generate, don't execute
)

# View the generated workflow
workflow = result["workflow"]
print(f"Generated workflow: {workflow.name}")
print(f"Steps: {len(workflow.steps)}")

# Print the workflow as YAML
print(workflow.to_yaml())

# Generate AND execute in one go
async for event in adk.compose(
    task="List all Python files in /tmp and count lines of code",
    mode="act",  # Generate and execute
    stream=True  # Stream execution events
):
    if event.get("type") == "text":
        # Generation progress
        print(f"AI: {event.get('content')}")
    elif event.get("type") == "execution":
        # Execution events
        print(f"Execution: {event.get('data')}")

task = """
Create a comprehensive backup and disaster recovery workflow that:
1. Backs up all PostgreSQL databases
2. Compresses backups with timestamps
3. Uploads to S3 with encryption
4. Validates backup integrity
5. Removes backups older than 30 days
6. Sends detailed report to Slack with:
   - Backup sizes
   - Duration
   - Success/failure status
7. Triggers PagerDuty alert if backup fails
"""

result = await adk.compose(task=task, mode="plan")

# The AI will generate a complete workflow with:
# - Proper error handling
# - Retry logic for network operations  
# - Parallel execution where appropriate
# - Integration with S3, Slack, and PagerDuty

task = """
Create a Kubernetes deployment workflow that:
1. Validates the deployment manifest
2. Checks if the namespace exists, creates if not
3. Applies the deployment with rolling update strategy
4. Waits for all pods to be ready (timeout 5 minutes)
5. Runs smoke tests against the new deployment
6. If tests fail, automatically rollback to previous version
7. Update deployment status in GitHub PR
"""

# Generate and execute with streaming
async for event in adk.compose(task=task, mode="act", stream=True):
    # Handle events as they come
    handle_event(event)

task = """
Create an incident response workflow that:
1. Fetches alert details from PagerDuty
2. Checks system health metrics for the affected service
3. Attempts automatic remediation based on alert type:
   - For high CPU: restart the service
   - For high memory: clear caches and restart
   - For disk space: clean up logs and temp files
4. Verifies the issue is resolved
5. Creates a JIRA ticket with incident details
6. Posts incident summary to #incidents Slack channel
"""

# With custom context
context = {
    "services": ["api", "web", "worker"],
    "monitoring_tool": "datadog",
    "ticketing_system": "jira"
}

result = await adk.compose(
    task=task,
    context=context,
    mode="plan"
)