> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agenthuman.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick Start

> Add an AgentHuman avatar to your Pipecat pipeline in four steps

## Step 1 — Install and configure

```bash theme={null}
pip install pipecat-ai[agenthuman]
```

Add your AgentHuman API key to your `.env` file, along with any STT, LLM, and TTS services you plan to use:

```bash theme={null}
AGENTHUMAN_API_KEY=ah_live_xxxxxxxxxxxxxxxxxxxxxxxxxxxx

# Example services (use whichever LLM / STT / TTS you prefer)
DAILY_ROOM_URL=https://your-domain.daily.co/your-room
DAILY_TOKEN=your-daily-participant-token
DEEPGRAM_API_KEY=your-deepgram-key
GOOGLE_API_KEY=your-google-ai-key
ELEVENLABS_API_KEY=your-elevenlabs-key
```

## Step 2 — Set up your transport with video output

`AgentHumanVideoService` **requires** a transport with `video_out_enabled=True`. The transport dimensions are used to auto-select the avatar's aspect ratio.

```python theme={null}
from pipecat.transports.daily.transport import DailyParams, DailyTransport

transport = DailyTransport(
    room_url=os.getenv("DAILY_ROOM_URL"),
    token=os.getenv("DAILY_TOKEN"),
    bot_name="AI Avatar",
    params=DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_out_enabled=True,    # required
        video_out_is_live=True,
        video_out_width=1280,
        video_out_height=960,
        video_out_bitrate=2_000_000,
    ),
)
```

## Step 3 — Add `AgentHumanVideoService` to your pipeline

Import `AgentHumanVideoService` and `NewSessionRequest` from `pipecat.services.agenthuman`, instantiate the service with your avatar ID and transport, and place it in the pipeline **after TTS** and **before `transport.output()`**.

```python theme={null}
from pipecat.services.agenthuman.api import NewSessionRequest
from pipecat.services.agenthuman.video import AgentHumanVideoService

agentHuman = AgentHumanVideoService(
    api_key=os.getenv("AGENTHUMAN_API_KEY"),
    session_request=NewSessionRequest(
        avatar="avat_01KMZHXFPBVCXA5ATK85HCP8G1"  # your avatar ID
    ),
    transport=transport,
)

pipeline = Pipeline([
    transport.input(),
    stt,
    user_aggregator,
    llm,
    tts,
    agentHuman,          # ← place after TTS
    transport.output(),
    assistant_aggregator,
])
```

## Step 4 — Run your bot

```python theme={null}
task = PipelineTask(pipeline, params=PipelineParams(enable_metrics=True))

@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
    context.add_message({"role": "user", "content": "Say hello and briefly introduce yourself."})
    await task.queue_frames([LLMRunFrame()])

@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
    await task.cancel()

runner = PipelineRunner()
await runner.run(task)
```

```bash theme={null}
python bot.py
```

<Note>
  `AgentHumanVideoService` creates the AgentHuman session and connects to the internal LiveKit room automatically on pipeline start. You don't need to manage room tokens or WebSocket connections manually.
</Note>