Chat

Like chat completions - except the model is your trained agent. A playbook + a small model trained on your use case, or any third-party model, served as one OpenAI-compatible endpoint. Text in, text out.

How it connects

Your stack	You swap	Unpod runs
LiveKit · Pipecat · Vapi · chat app	the LLM base URL (or the `llm=` slot)	agent, sessions, memory

Go live in 3 steps

1. Get an endpoint

Build and publish a playbook in the Playground, then open Deploy as Endpoint → Manage API Keys. You get the endpoint, a model id (public:PB_...), and prefilled snippets. See Publish & Share.

2. Point your stack at it

Any OpenAI client
LiveKit
Pipecat

curl -X POST "https://inference.unpod.ai/v1/chat/completions" \
  -H "Authorization: Bearer $UNPOD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"public:PB_7ZRMzCA1ojQ9LlcK","messages":[{"role":"user","content":"hi"}],"user":"sess_abc"}'

Any OpenAI-compatible SDK works - swap base URL, key, and model. user is your session id, so the conversation stays stateful across calls.

from livekit.agents import Agent, AgentSession
from superdialog import DialogMachine
from superdialog.adapters.livekit import DialogMachineLLM

dm = DialogMachine("kyc.yaml", llm="anthropic/claude-haiku-4-5")

async def entrypoint(ctx):
    await AgentSession().start(agent=Agent(llm=DialogMachineLLM(dm)), room=ctx.room)

The agent runs in-process in the llm= slot. Full guide: LiveKit embedding.

from superdialog import DialogMachine
from superdialog.adapters.pipecat import make_processor

agent = DialogMachine("kyc.yaml", llm="anthropic/claude-haiku-4-5")
processor = make_processor(agent)  # place between STT and TTS

Full guide: Pipecat embedding.

3. Test it

curl the endpoint, or talk to the same agent in the Playground Preview. Same brain, same behaviour.

Go deeper

Chat API reference

Full request/response reference.

Build a playbook

Describe the job; the playbook writes itself.

Embedding guides

LiveKit, Pipecat, FastAPI, CLI.

What is a playbook?

Persona, checkpoints, slots - the format.

Start Here

Use it like a model

How it connects

Go live in 3 steps

1. Get an endpoint

2. Point your stack at it

3. Test it

Go deeper

Chat API reference

Build a playbook

Embedding guides

What is a playbook?

​How it connects

​Go live in 3 steps

​1. Get an endpoint

​2. Point your stack at it

​3. Test it

​Go deeper

Chat API reference

Build a playbook

Embedding guides

What is a playbook?

How it connects

Go live in 3 steps

1. Get an endpoint

2. Point your stack at it

3. Test it

Go deeper