Cleanup'

README.md

@@ -2,19 +2,21 @@
   <img src="./.github/assets/livekit-mark.png" alt="LiveKit logo" width="100" height="100">
 </a>
 
-# Voice AI Assistant with LiveKit Agents
+# LiveKit Agents Starter - Python
 
-<p>
-  <a href="https://cloud.livekit.io/projects/p_/sandbox"><strong>Deploy a sandbox app</strong></a>
-  •
-  <a href="https://docs.livekit.io/agents/">LiveKit Agents Docs</a>
-  •
-  <a href="https://livekit.io/cloud">LiveKit Cloud</a>
-  •
-  <a href="https://blog.livekit.io/">Blog</a>
-</p>
+A complete starter project for building voice AI apps with [LiveKit Agents for Python](https://github.com/livekit/agents).
 
-A simple voice AI assistant built with [LiveKit Agents for Python](https://github.com/livekit/agents).
+The starter project includes:
+
+- A simple voice AI assistant based on the [Voice AI quickstart](https://docs.livekit.io/agents/start/voice-ai/)
+- Voice AI pipeline based on [OpenAI](https://docs.livekit.io/agents/integrations/llm/openai/), [Cartesia](https://docs.livekit.io/agents/integrations/tts/cartesia/), and [Deepgram](https://docs.livekit.io/agents/integrations/llm/deepgram/)
+  - Easily integrate your preferred [LLM](https://docs.livekit.io/agents/integrations/llm/), [STT](https://docs.livekit.io/agents/integrations/stt/), and [TTS](https://docs.livekit.io/agents/integrations/tts/) instead, or swap to a realtime model like the [OpenAI Realtime API](https://docs.livekit.io/agents/integrations/realtime/openai)
+- Eval suite based on the LiveKit Agents [testing & evaluation framework](https://docs.livekit.io/agents/testing/)
+- [LiveKit Turn Detector](https://docs.livekit.io/agents/build/turns/turn-detector/) for contextually-aware speaker detection, with multilingual support
+- [LiveKit Cloud enhanced noise cancellation](https://docs.livekit.io/home/cloud/noise-cancellation/)
+- Integrated [metrics and logging](https://docs.livekit.io/agents/build/metrics/)
+
+This starter app is compatible with [SIP-based telephony](https://docs.livekit.io/agents/start/telephony/) or any [custom web/mobile frontend](https://docs.livekit.io/agents/start/frontend/).
 
 ## Dev Setup
 
@@ -27,30 +29,55 @@ uv sync
 
 Set up the environment by copying `.env.example` to `.env` and filling in the required values:
 
-- `LIVEKIT_URL`
+- `LIVEKIT_URL`: Use [LiveKit Cloud](https://cloud.livekit.io/) or [run your own](https://docs.livekit.io/home/self-hosting/)
 - `LIVEKIT_API_KEY`
 - `LIVEKIT_API_SECRET`
-- `OPENAI_API_KEY`
-- `DEEPGRAM_API_KEY`
+- `OPENAI_API_KEY`: [Get a key](https://platform.openai.com/api-keys) or use your [preferred LLM provider](https://docs.livekit.io/agents/integrations/llm/)
+- `DEEPGRAM_API_KEY`: [Get a key](https://console.deepgram.com/) or use your [preferred STT provider](https://docs.livekit.io/agents/integrations/stt/)
+- `CARTESIA_API_KEY`: [Get a key](https://play.cartesia.ai/keys) or use your [preferred TTS provider](https://docs.livekit.io/agents/integrations/tts/)
 
-You can also do this automatically using the LiveKit CLI:
+You can load the LiveKit environment automatically using the [LiveKit CLI](https://docs.livekit.io/home/cli/cli-setup):
 
 ```bash
 lk app env -w .env
 ```
 
-Run the agent in console mode:
+## Run the agent
+
+Run this command to speak to your agent directly in your terminal:
 
 ```console
 uv run python src/agent.py console
 ```
 
+To run the agent for use with a frontend or telephony, use the `dev` command:
 
-This agent requires a frontend application to communicate with. Use a [starter app](https://docs.livekit.io/agents/start/frontend/#starter-apps), our hosted [Sandbox](https://cloud.livekit.io/projects/p_/sandbox) frontends, or the [LiveKit Agents Playground](https://agents-playground.livekit.io/).
+```console
+uv run python src/agent.py dev
+```
 
+In production, use the `start` command:
 
-Run evals
+```console
+uv run python src/agent.py start
+```
+
+## Web and mobile frontends
+
+To use a prebuilt frontend or build your own, see the [agents frontend guide](https://docs.livekit.io/agents/start/frontend/).
+
+## Telephony
+
+To add a phone number, see the [agents telephony guide](https://docs.livekit.io/agents/start/telephony/).
+
+## Tests and evals
+
+This project includes a complete suite of evals, based on the LiveKit Agents [testing & evaluation framework](https://docs.livekit.io/agents/testing/). To run them, use `pytest`.
 
 ```console
 uv run pytest evals
-```
+```
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "agent-starter-python"
-version = "0.1.0"
+version = "1.0.0"
 description = "Simple voice AI assistant built with LiveKit Agents for Python"
 requires-python = ">=3.9"
 

src/agent.py

@@ -66,15 +66,21 @@ async def entrypoint(ctx: JobContext):
         "room": ctx.room.name,
     }
 
+    # Set up a voice AI pipeline using OpenAI, Cartesia, Deepgram, and the LiveKit turn detector
     session = AgentSession(
-        vad=ctx.proc.userdata["vad"],
         # any combination of STT, LLM, TTS, or realtime API can be used
         llm=openai.LLM(model="gpt-4o-mini"),
         stt=deepgram.STT(model="nova-3", language="multi"),
         tts=cartesia.TTS(),
         # use LiveKit's turn detection model
         turn_detection=MultilingualModel(),
+        vad=ctx.proc.userdata["vad"],
     )
+    
+    # To use the OpenAI Realtime API, use the following session setup instead:
+    # session = AgentSession(
+    #     llm=openai.realtime.RealtimeModel()
+    # )
 
     # log metrics as they are emitted, and total usage after session is over
     usage_collector = metrics.UsageCollector()