Level Up to SuperDialog

A plain LLM brain answers questions. It does not reliably follow a multi-step process, collect required fields in order, branch on conditions, or call your tools at the right moment. When your voice agent needs that - appointment booking, triage, verification flows - level up to SuperDialog: a conversation runtime that executes a playbook - journeys of checkpoints that gate outcomes - turn by turn. (Flow graphs still work too, run compiled on the same engine.)

SuperDialog session integration diagram showing user speech passing through STT, user_turn hooks, DialogMachine.turn, agent_turn hooks, TTS, and caller playback.

Wire it in

Assign a DialogMachine to ctx.session.dialog_machine - the SDK auto-wraps it in a SuperDialogAdapter (see Adapters). On the Playbook engine the Talker streams real tokens straight to TTS:

from superdialog import DialogMachine
from unpod import AgentRunner, CallContext

async def handle_call(ctx: CallContext) -> None:
    ctx.session.dialog_machine = DialogMachine(
        "clinic.yaml",                       # any format; Playbook engine by default
        llm="anthropic/claude-haiku-4-5",    # runtime model
    )
    await ctx.session.run()  # hands every turn to the agent

AgentRunner(entrypoint=handle_call, agent_id="my-agent").start()

Generate a playbook once with superdialog generate and version-control the YAML. You can edit it by hand and re-run superdialog chat to test - no voice setup needed. See the SuperDialog Quickstart.

Voice-specific patterns

These patterns are specific to running a SuperDialog agent inside a live call. For playbooks, tools, and sessions themselves, see the SuperDialog docs.

Give tools call context

Define tools as closures inside your entrypoint to capture per-call data:

from superdialog.tools import tool

async def handle_call(ctx: CallContext) -> None:
    caller_number = ctx.user_number

    @tool
    def lookup_caller() -> dict:
        """Look up the caller's account by phone number."""
        return crm.lookup_phone(caller_number)   # closure over call data

    ctx.session.dialog_machine = DialogMachine(
        "clinic.yaml", llm="anthropic/claude-haiku-4-5", tools=[lookup_caller]
    )
    await ctx.session.run()

Inject instructions mid-call

Push a runtime directive into the machine from a session hook:

@ctx.session.on("user_turn")
async def on_user_turn(text: str) -> None:
    if "premium" in ctx.session.data.get("tier", ""):
        ctx.session.dialog_machine.assist(
            "This is a premium customer - prioritize quick resolution."
        )

Switch flows mid-call (graph engine)

switch_flow is a graph-engine feature - build the machine with a FlowSet. On the Playbook engine, model the same behaviour with multiple journeys and interrupts in one playbook instead.

from superdialog import DialogMachine, Flow, FlowSet

flows = FlowSet({"triage": Flow.load("triage.json"), "billing": Flow.load("billing.json")})
ctx.session.dialog_machine = DialogMachine(flows, llm="anthropic/claude-haiku-4-5")

@ctx.session.on("user_turn")
async def on_user_turn(text: str) -> None:
    if "billing" in text.lower():
        ctx.session.dialog_machine.switch_flow("billing", preserve_memory=True)

Detect completion

Both engines have terminal states. After run() returns, check whether the dialog finished or the caller hung up mid-conversation:

async def handle_call(ctx: CallContext) -> None:
    machine = DialogMachine("clinic.yaml", llm="anthropic/claude-haiku-4-5")
    ctx.session.dialog_machine = machine
    await ctx.session.run()

    if machine.is_complete:
        print("Dialog reached a terminal checkpoint")
    else:
        print("Call ended mid-conversation (hang up / timeout)")

Go deeper

SuperDialog

The framework itself: playbooks, tools, sessions, CLI.

Embedding Guide: Unpod Voice

The full worked example: a SuperDialog agent inside an AgentRunner session.

Thinking in Playbooks

The mental model: checkpoints that gate outcomes.

Bring Your Agent

Prefer your own brain? Adapters for LangChain, OpenAI, Anthropic, HTTP.

Getting Started

Speech

Connectivity

Dialog

Calls

Session

Production

Level Up to SuperDialog

Wire it in

Voice-specific patterns

Give tools call context

Inject instructions mid-call

Switch flows mid-call (graph engine)

Detect completion

Go deeper

SuperDialog

Embedding Guide: Unpod Voice

Thinking in Playbooks

Bring Your Agent

​Wire it in

​Voice-specific patterns

​Give tools call context

​Inject instructions mid-call

​Switch flows mid-call (graph engine)

​Detect completion

​Go deeper

SuperDialog

Embedding Guide: Unpod Voice

Thinking in Playbooks

Bring Your Agent

Wire it in

Voice-specific patterns

Give tools call context

Inject instructions mid-call

Switch flows mid-call (graph engine)

Detect completion

Go deeper