A simple 5-step process to orchestrate your AI agents with confidence. From registration to optimization, we've got you covered.
Register via API or UI, fetch tool definitions automatically. Import from multiple sources including Swagger, OpenAPI, or manual configuration.
Fill environment keys, run schema linting, simulate flows. Comprehensive pre-flight checks ensure your agents work correctly before deployment.
Trigger workflows, parallel runs, loop controls. Advanced orchestration with real-time execution management and dynamic scaling.
Real-time context, logs, Jaeger spans, pause/edit mid-run. Complete visibility into execution with interactive debugging capabilities.
View analytics, spot bottlenecks, auto-tune patterns. Data-driven insights help you optimize performance and reduce costs.
Get started with just a few lines of code. Our Python SDK makes it easy to integrate AgentRuntime into your existing workflows.
import agentruntime
# Initialize the runtime client
client = agentruntime.Client(api_key="your_api_key")
# Start a new workflow run
run = client.workflows.start(
workflow_id="customer-support-v2",
inputs={
"customer_query": "Need help with billing",
"priority": "high"
},
context={
"session_id": "sess_123",
"user_id": "user_456"
}
)
# Monitor execution
print(f"Run started: {run.id}")
print(f"Status: {run.status}")
# Get real-time updates
for event in client.runs.stream(run.id):
print(f"Event: {event.type} - {event.data}")Try our interactive sandbox and see how easy it is to orchestrate AI agents.