Async Python SDK for the Metered Realtime Messaging service — WebSocket pub/sub + WebRTC (aiortc).
Project description
metered-realtime
Async Python SDK for the Metered Realtime Messaging service. The Python counterpart to the JavaScript SDK — a Python peer can share a room with browser peers.
Built for server-side and edge use cases the browser can't reach: AI voice agents that join a room to run STT→LLM→TTS, IoT camera/telemetry bridges, recording bots, and headless automation.
Building with AI? Point Claude Code, Cursor, or Copilot at the SDK reference file —
llms-realtime-messaging-python-sdk.txt— and describe what you want to build. More prompts: Build with AI.
Install
pip install metered-realtime # pub/sub only (SignallingClient)
pip install "metered-realtime[webrtc]" # + the WebRTC peer layer (MeteredPeer)
Requires Python 3.10+.
Quick start — rooms with WebRTC (MeteredPeer)
import asyncio
from metered_realtime import MeteredPeer, PeerJoined, Data, Track
async def main() -> None:
async with MeteredPeer(api_key="pk_live_...") as peer:
@peer.on(PeerJoined)
def _(ev: PeerJoined) -> None:
print("peer joined:", ev.peer.id)
@ev.peer.on(Track)
def _(t: Track) -> None:
print("receiving", t.track.kind, "stream:", [s.id for s in t.streams])
@peer.on(Data)
def _(ev: Data) -> None:
print(ev.sender_peer_id, "says", ev.data)
await peer.join("room-42")
await peer.send({"hello": True}) # broadcast to the room
# Attach media to every peer (current + future). add_track takes a track;
# add_stream takes a MediaStream grouping. Any aiortc track works, e.g.:
# from aiortc.contrib.media import MediaPlayer
# peer.add_track(MediaPlayer("clip.mp3").audio)
await asyncio.sleep(30)
asyncio.run(main())
Quick start — pub/sub only (SignallingClient)
import asyncio
from metered_realtime import SignallingClient, Message
async def main() -> None:
async with SignallingClient(api_key="pk_live_...") as client:
@client.on(Message)
def _(ev: Message) -> None:
print(ev.sender_peer_id, ev.data)
await client.subscribe("room-42")
await client.publish("room-42", {"hello": True})
await asyncio.sleep(5)
asyncio.run(main())
Media input
Pass any aiortc-compatible track to add_track, or use the built-in helpers
(metered-realtime[webrtc]):
from metered_realtime import AudioSource, from_file, from_rtsp, iter_frames
# Push synthesized audio (e.g. an AI agent's speech) into the room:
source = AudioSource(input_rate=16_000) # 16 kHz mono PCM in
peer.add_track(source)
await source.push(pcm_bytes) # emitted as real-time 48 kHz audio
source.end()
# Or feed from a file / IP camera (.audio / .video is None if the source lacks it):
peer.add_track(from_file("clip.mp4").audio)
peer.add_track(from_rtsp("rtsp://cam:554/stream").video)
# Consume a peer's incoming media (e.g. for speech-to-text):
async for frame in iter_frames(track): # ends cleanly when the track stops
...
from_camera / from_microphone / screen_share capture from local devices
(platform-dependent). See the
documentation for auth (publishable keys vs.
token providers), presence, direct messages, media, and data channels.
Provisional: the media-input helpers (
AudioSource,iter_frames,from_file/from_rtsp/from_camera/from_microphone/screen_share) may change in a minor release. The core media API onMeteredPeer—add_stream/add_track/replace_track, andMediaStream— is stable.
Documentation
Full Python SDK docs: metered.ca/docs/realtime-messaging/sdk-python
- Getting started — install, auth, your first peer
- API reference —
MeteredPeer,RemotePeer,DataChannel, media, every event/error/code - Guides — authentication, reconnect best-practices, AI agent communication, IoT telemetry
- Errors & codes — close codes, exception types, how to react
Examples
Runnable end-to-end demos, each walked through in the examples docs (each takes METERED_KEY=pk_live_…):
data_channel_echo.py— open a peer-to-peer data channel and echo messages.audio_agent.py— stream synthesized audio into a room and consume a peer's audio (the "agent in the call" shape).
License
MIT — see LICENSE.
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