How AgentRuntimeWorks

A simple 5-step process to orchestrate your AI agents with confidence. From registration to optimization, we've got you covered.

Connect Your Agents

Register via API or UI, fetch tool definitions automatically. Import from multiple sources including Swagger, OpenAPI, or manual configuration.

API endpoint registration

Tool definition extraction

Dependency mapping

Schema validation

Configure & Validate

Fill environment keys, run schema linting, simulate flows. Comprehensive pre-flight checks ensure your agents work correctly before deployment.

Environment setup

Schema linting

Flow simulation

Dependency checks

Execute & Orchestrate

Trigger workflows, parallel runs, loop controls. Advanced orchestration with real-time execution management and dynamic scaling.

Workflow triggers

Parallel execution

Loop controls

Dynamic scaling

Monitor & Trace

Real-time context, logs, Jaeger spans, pause/edit mid-run. Complete visibility into execution with interactive debugging capabilities.

Real-time monitoring

Distributed tracing

Interactive debugging

Mid-run modifications

Analyze & Optimize

View analytics, spot bottlenecks, auto-tune patterns. Data-driven insights help you optimize performance and reduce costs.

Performance analytics

Bottleneck detection

Pattern optimization

Cost analysis

Simple API Integration

Get started with just a few lines of code. Our Python SDK makes it easy to integrate AgentRuntime into your existing workflows.

RESTful API with comprehensive documentation
Python, Go, and JavaScript SDKs available
WebSocket support for real-time updates

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}")

Ready to see it in action?

Try our interactive sandbox and see how easy it is to orchestrate AI agents.