PLACEHOLDER values with real config before deploying.
The flow:
welcome: greets the customer. Transfers during working hours, ends the call after-hours, or jumps straight topayment_confirmedif a payment event arrives.collect_order: collects an order id with silence handling and a per-node knowledge base.payment_confirmed: static confirmation message played from cache.transfer_call: routes the call to a human agent via the transfer tool.closing: static goodbye.
{
"agent_config": {
"agent_name": "Demo Graph Agent",
"agent_type": "other",
"agent_welcome_message": "Hello, my name is Neha.",
"tasks": [
{
"task_type": "conversation",
"toolchain": {
"execution": "parallel",
"pipelines": [["transcriber", "llm", "synthesizer"]]
},
"task_config": {
"optimize_latency": true,
"call_terminate": 600,
"hangup_after_LLMCall": true
},
"tools_config": {
"input": { "format": "wav", "provider": "plivo" },
"output": { "format": "wav", "provider": "plivo" },
"api_tools": {
"tools": [
{
"key": "transfer_call",
"name": "transfer_call_main",
"parameters": {
"type": "object",
"required": ["call_sid"],
"properties": {
"call_sid": { "type": "string", "description": "unique call id" }
}
},
"description": "Transfer call to human agent.",
"pre_call_message": "Please hold while I transfer your call..."
}
],
"tools_params": {
"transfer_call_main": {
"url": null,
"param": {
"call_sid": "%(call_sid)s",
"call_transfer_number": "+91XXXXXXXXXX"
},
"method": "POST",
"headers": {},
"scope": "node",
"nodes": ["transfer_call"]
}
}
},
"llm_agent": {
"agent_type": "graph_agent",
"agent_flow_type": "streaming",
"llm_config": {
"model": "gpt-4.1-mini",
"max_tokens": 200,
"temperature": 0.2,
"provider": "openai",
"routing_model": "gpt-4.1-mini",
"routing_max_tokens": 250,
"routing_instructions": "PLACEHOLDER: routing instructions",
"agent_information": "PLACEHOLDER: global agent prompt (persona, language, guardrails)",
"current_node_id": "welcome",
"variable_types": {
"recipient_data.current_hour": "number"
},
"nodes": [
{
"id": "welcome",
"prompt": "Greet the customer and identify their intent.",
"edges": [
{
"to_node_id": "collect_order",
"condition": "Customer wants order status",
"parameters": { "order_id": "string" }
},
{
"to_node_id": "transfer_call",
"condition": "Customer asks for a human",
"condition_type": "expression",
"expression": {
"logic": "and",
"conditions": [
{ "variable": "recipient_data.current_hour", "operator": "gte", "value": 10 },
{ "variable": "recipient_data.current_hour", "operator": "lt", "value": 18 }
]
}
},
{
"to_node_id": "closing",
"condition": "Outside working hours",
"condition_type": "expression",
"priority": 1,
"expression": {
"logic": "or",
"conditions": [
{ "variable": "recipient_data.current_hour", "operator": "lt", "value": 10 },
{ "variable": "recipient_data.current_hour", "operator": "gte", "value": 18 }
]
}
},
{
"to_node_id": "payment_confirmed",
"condition_type": "event",
"event_name": "payment_completed"
}
]
},
{
"id": "collect_order",
"prompt": "Confirm the order id and look up its status using @fetch_order_status.",
"repeat_after_silence_seconds": 10,
"rag_config": {
"vector_store": {
"provider_config": { "vector_id": "PLACEHOLDER_collection_id" }
},
"similarity_top_k": 8
},
"edges": [
{ "to_node_id": "closing", "condition": "Status delivered to customer" },
{
"to_node_id": "transfer_call",
"condition_type": "expression",
"expression": {
"conditions": [
{ "variable": "_silence_repeats", "operator": "gte", "value": 3 }
]
}
}
]
},
{
"id": "payment_confirmed",
"node_type": "static",
"static_message": "Your payment is confirmed. Thank you!",
"edges": [
{ "to_node_id": "closing", "condition_type": "unconditional" }
]
},
{
"id": "transfer_call",
"prompt": "Transfer the call to a human agent.",
"function_call": "transfer_call_main",
"edges": []
},
{
"id": "closing",
"node_type": "static",
"static_message": "Thank you for calling. Have a wonderful day. Goodbye!",
"edges": []
}
]
}
},
"transcriber": {
"model": "nova-3",
"language": "multi-hi",
"provider": "deepgram",
"stream": true,
"encoding": "linear16",
"sampling_rate": 16000,
"endpointing": 700
},
"synthesizer": {
"provider": "elevenlabs",
"stream": true,
"caching": true,
"buffer_size": 220,
"audio_format": "wav",
"provider_config": {
"model": "eleven_turbo_v2_5",
"voice": "PLACEHOLDER: voice name",
"voice_id": "PLACEHOLDER: voice ID",
"temperature": 0.2,
"similarity_boost": 0.6,
"speed": 0.9
}
}
}
}
]
}
}
For automatic post-call extraction (disposition, sentiment, funnel stage), add an
extraction task alongside the conversation task. See Using extractions.What this example shows
| Feature | Where |
|---|---|
| Mixed edge types on one node | welcome has LLM, expression, and event edges. |
Inline data extraction via parameters | welcome -> collect_order captures order_id. |
| Working-hours routing | welcome -> transfer_call (expression). |
| After-hours fallback | welcome -> closing (expression, priority 1). |
| Event-driven proactive speech | welcome -> payment_confirmed on payment_completed. |
| Typed expression variables | variable_types types recipient_data.current_hour as a number. |
| Per-node RAG | collect_order has rag_config. |
| Silence handling and escalation | collect_order has repeat_after_silence_seconds: 10 and an expression edge on _silence_repeats. |
| Static node | payment_confirmed, closing. |
| Call transfer | transfer_call with function_call. |
| Node-scoped tool | transfer_call_main is scoped to the transfer_call node via scope and nodes. |