+
+
diff --git a/examples/daily-custom-tracks/office-ambience-mono-16000.mp3 b/examples/daily-custom-tracks/office-ambience-mono-16000.mp3
new file mode 100644
index 000000000..ea98082c7
Binary files /dev/null and b/examples/daily-custom-tracks/office-ambience-mono-16000.mp3 differ
diff --git a/examples/daily-custom-tracks/requirements.txt b/examples/daily-custom-tracks/requirements.txt
new file mode 100644
index 000000000..b3d2deec3
--- /dev/null
+++ b/examples/daily-custom-tracks/requirements.txt
@@ -0,0 +1,2 @@
+pydub
+pipecat-ai[daily]
diff --git a/examples/canonical-metrics/runner.py b/examples/daily-custom-tracks/runner.py
similarity index 100%
rename from examples/canonical-metrics/runner.py
rename to examples/daily-custom-tracks/runner.py
diff --git a/examples/canonical-metrics/Dockerfile b/examples/daily-multi-translation/Dockerfile
similarity index 79%
rename from examples/canonical-metrics/Dockerfile
rename to examples/daily-multi-translation/Dockerfile
index a5b4668c6..419adca34 100644
--- a/examples/canonical-metrics/Dockerfile
+++ b/examples/daily-multi-translation/Dockerfile
@@ -1,7 +1,12 @@
FROM python:3.10-bullseye
+
RUN mkdir /app
+RUN mkdir /app/assets
+RUN mkdir /app/utils
COPY *.py /app/
COPY requirements.txt /app/
+
+
WORKDIR /app
RUN pip3 install -r requirements.txt
diff --git a/examples/daily-multi-translation/README.md b/examples/daily-multi-translation/README.md
new file mode 100644
index 000000000..7e27cb217
--- /dev/null
+++ b/examples/daily-multi-translation/README.md
@@ -0,0 +1,39 @@
+# Daily Multi Translation
+
+This example shows how to use Daily to stream multiple simultaneous translations using a single transport. Daily provides custom tracks and in this example we will simultaneously translate incoming audio in English to Spanish, French and German, each of them being sent to a custom track.
+
+## Get started
+
+```python
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+
+cp env.example .env # and add your credentials
+
+```
+
+## Run the server
+
+```bash
+python server.py
+```
+
+Then, visit `http://localhost:7860/` in your browser. This will open a Daily Prebuilt room where you will speak in English (make sure you are not muted).
+
+## Open client
+
+Next, you need to open the client that will listen to the translations.
+
+```bash
+open index.html
+```
+
+Once the client is opened, copy the URL of the Daily room created above and join it. You should be able to select which translation you want to hear.
+
+## Build and test the Docker image
+
+```
+docker build -t daily-multi-translation .
+docker run --env-file .env -p 7860:7860 daily-multi-translation
+```
diff --git a/examples/daily-multi-translation/bot.py b/examples/daily-multi-translation/bot.py
new file mode 100644
index 000000000..5f5037584
--- /dev/null
+++ b/examples/daily-multi-translation/bot.py
@@ -0,0 +1,165 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.mixers.soundfile_mixer import SoundfileMixer
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+BACKGROUND_SOUND_FILE = "office-ambience-mono-16000.mp3"
+
+
+async def main():
+ async with aiohttp.ClientSession() as session:
+ (room_url, token) = await configure(session)
+
+ transport = DailyTransport(
+ room_url,
+ token,
+ "Multi translation bot",
+ DailyParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ audio_out_mixer={
+ "spanish": SoundfileMixer(
+ sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
+ ),
+ "french": SoundfileMixer(
+ sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
+ ),
+ "german": SoundfileMixer(
+ sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
+ ),
+ },
+ audio_out_destinations=["spanish", "french", "german"],
+ microphone_out_enabled=False, # Disable since we just use custom tracks
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts_spanish = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="cefcb124-080b-4655-b31f-932f3ee743de",
+ transport_destination="spanish",
+ )
+ tts_french = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="8832a0b5-47b2-4751-bb22-6a8e2149303d",
+ transport_destination="french",
+ )
+ tts_german = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="38aabb6a-f52b-4fb0-a3d1-988518f4dc06",
+ transport_destination="german",
+ )
+
+ messages_spanish = [
+ {
+ "role": "system",
+ "content": "You will be provided with a sentence in English, and your task is to only translate it into Spanish.",
+ },
+ ]
+ messages_french = [
+ {
+ "role": "system",
+ "content": "You will be provided with a sentence in English, and your task is to only translate it into French.",
+ },
+ ]
+ messages_german = [
+ {
+ "role": "system",
+ "content": "You will be provided with a sentence in English, and your task is to only translate it into German.",
+ },
+ ]
+
+ llm_spanish = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+ llm_french = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+ llm_german = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ context_spanish = OpenAILLMContext(messages_spanish)
+ context_aggregator_spanish = llm_spanish.create_context_aggregator(context_spanish)
+
+ context_french = OpenAILLMContext(messages_french)
+ context_aggregator_french = llm_french.create_context_aggregator(context_french)
+
+ context_german = OpenAILLMContext(messages_german)
+ context_aggregator_german = llm_german.create_context_aggregator(context_german)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ ParallelPipeline(
+ # Spanish pipeline.
+ [
+ context_aggregator_spanish.user(),
+ llm_spanish,
+ tts_spanish,
+ context_aggregator_spanish.assistant(),
+ ],
+ # French pipeline.
+ [
+ context_aggregator_french.user(),
+ llm_french,
+ tts_french,
+ context_aggregator_french.assistant(),
+ ],
+ # German pipeline.
+ [
+ context_aggregator_german.user(),
+ llm_german,
+ tts_german,
+ context_aggregator_german.assistant(),
+ ],
+ ),
+ transport.output(), # Transport bot output
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ audio_in_sample_rate=16000,
+ audio_out_sample_rate=16000,
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ observers=[TranscriptionLogObserver()],
+ )
+
+ runner = PipelineRunner()
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/examples/canonical-metrics/env.example b/examples/daily-multi-translation/env.example
similarity index 70%
rename from examples/canonical-metrics/env.example
rename to examples/daily-multi-translation/env.example
index 6b865401a..a780ec7d8 100644
--- a/examples/canonical-metrics/env.example
+++ b/examples/daily-multi-translation/env.example
@@ -1,6 +1,5 @@
DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
DAILY_API_KEY=7df...
OPENAI_API_KEY=sk-PL...
-ELEVENLABS_API_KEY=aeb...
-CANONICAL_API_KEY=can...
-CANONICAL_API_URL=
+DEEPGRAM_API_KEY=efb...
+CARTESIA_API_KEY=aeb...
diff --git a/examples/daily-multi-translation/index.html b/examples/daily-multi-translation/index.html
new file mode 100644
index 000000000..c8ca0832d
--- /dev/null
+++ b/examples/daily-multi-translation/index.html
@@ -0,0 +1,202 @@
+
+
+ daily multi translation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/examples/daily-multi-translation/office-ambience-mono-16000.mp3 b/examples/daily-multi-translation/office-ambience-mono-16000.mp3
new file mode 100644
index 000000000..ea98082c7
Binary files /dev/null and b/examples/daily-multi-translation/office-ambience-mono-16000.mp3 differ
diff --git a/examples/daily-multi-translation/requirements.txt b/examples/daily-multi-translation/requirements.txt
new file mode 100644
index 000000000..e20c41d5a
--- /dev/null
+++ b/examples/daily-multi-translation/requirements.txt
@@ -0,0 +1,5 @@
+aiofiles
+python-dotenv
+fastapi[all]
+uvicorn
+pipecat-ai[daily,deepgram,openai,silero,cartesia]
diff --git a/examples/daily-multi-translation/runner.py b/examples/daily-multi-translation/runner.py
new file mode 100644
index 000000000..ad39a3ac4
--- /dev/null
+++ b/examples/daily-multi-translation/runner.py
@@ -0,0 +1,55 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+
+async def configure(aiohttp_session: aiohttp.ClientSession):
+ parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+ parser.add_argument(
+ "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+ )
+ parser.add_argument(
+ "-k",
+ "--apikey",
+ type=str,
+ required=False,
+ help="Daily API Key (needed to create an owner token for the room)",
+ )
+
+ args, unknown = parser.parse_known_args()
+
+ url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+ key = args.apikey or os.getenv("DAILY_API_KEY")
+
+ if not url:
+ raise Exception(
+ "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+ )
+
+ if not key:
+ raise Exception(
+ "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+ )
+
+ daily_rest_helper = DailyRESTHelper(
+ daily_api_key=key,
+ daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+ aiohttp_session=aiohttp_session,
+ )
+
+ # Create a meeting token for the given room with an expiration 1 hour in
+ # the future.
+ expiry_time: float = 60 * 60
+
+ token = await daily_rest_helper.get_token(url, expiry_time)
+
+ return (url, token)
diff --git a/examples/canonical-metrics/server.py b/examples/daily-multi-translation/server.py
similarity index 100%
rename from examples/canonical-metrics/server.py
rename to examples/daily-multi-translation/server.py
diff --git a/examples/deployment/modal-example/.gitignore b/examples/deployment/modal-example/.gitignore
index ecafe6e74..b74c7cace 100644
--- a/examples/deployment/modal-example/.gitignore
+++ b/examples/deployment/modal-example/.gitignore
@@ -1,3 +1,6 @@
+# Modal clone
+modal-examples
+
# Python
__pycache__/
*.py[cod]
diff --git a/examples/deployment/modal-example/README.md b/examples/deployment/modal-example/README.md
index b73a5b299..cd23935c2 100644
--- a/examples/deployment/modal-example/README.md
+++ b/examples/deployment/modal-example/README.md
@@ -1,37 +1,91 @@
# Deploying Pipecat to Modal.com
-Barebones deployment example for [modal.com](https://www.modal.com)
+Deployment example for [modal.com](https://www.modal.com). This example demonstrates how to deploy a FastAPI webapp to Modal with an RTVI compatible `/connect` endpoint that launches a Pipecat pipeline in a separate Modal container and returns a room/token for the client to join. This example also supports providing a parameter to the `/connect` endpoint for specifying which Pipecat pipeline to launch; openai, gemini, or vllm. The vllm pipeline points to a self-hosted OpenAI compatible LLM, using a llama model (neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16), deployed to Modal.
-1. Install dependencies
+
-```bash
-python -m venv venv
-source venv/bin/active # or OS equivalent
-pip install -r requirements.txt
-```
+# Running this Example
-2. Setup .env
+## Install the Modal CLI
-```bash
-cp env.example .env
-```
+Setup a Modal account and install it on your machine if you have not already, following their easy 3 steps in their [Getting Started Guide](https://modal.com/docs/guide#getting-started)
-Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
+## Deploy a self-serve LLM
-3. Test the app locally
+1. Deploy Modal's OpenAI-compatible LLM service:
-```bash
-modal serve app.py
-```
+ ```bash
+ git clone https://github.com/modal-labs/modal-examples
+ cd modal-examples
+ modal deploy 06_gpu_and_ml/llm-serving/vllm_inference.py
+ ```
+
+ Refer to Modal's guide and example for [Deploying an OpenAI-compatible LLM service with vLLM](https://modal.com/docs/examples/vllm_inference) for more details.
+
+2. Take note of the endpoint URL from the previous step, which will look like:
+ ```
+ https://{your-workspace}--example-vllm-openai-compatible-serve.modal.run
+ ```
+ You'll need this for the `bot_vllm.py` file in the next section.
+
+ **Note:** The default Modal LLM example uses Llama-3.1 and will shut down after 15 minutes of inactivity. Cold starts take 5-10 minutes. To prepare the service, we recommend visiting the `/docs` endpoint (`https://--example-vllm-openai-compatible-serve.modal.run/docs`) for your deployed LLM and wait for it to fully load before connecting your client.
+
+## Deploy FastAPI App and Pipecat pipeline to Modal
+
+1. Setup environment variables
+
+ ```bash
+ cd server
+ cp env.example .env
+ # Modify .env to provide your service API Keys
+ ```
+
+ Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
+
+2. Update the `modal_url` in `server/src/bot_vllm.py` to point to the url produced from the self-serve llm deploy, mentioned above.
+
+3. From within the `server` directory, test the app locally:
+
+ ```bash
+ modal serve app.py
+ ```
4. Deploy to production
-```bash
-modal deploy app.py
-```
+ ```bash
+ modal deploy app.py
+ ```
-## Configuration options
+5. Note the endpoint URL produced from this deployment. It will look like:
-This app sets some sensible defaults for reducing cold starts, such as `minkeep_warm=1`, which will keep at least 1 warm instance ready for your bot function.
+ ```bash
+ https://{your-workspace}--pipecat-modal-fastapi-app.modal.run
+ ```
-It has been configured to only allow a concurrency of 1 (`max_inputs=1`) as each user will require their own running function.
\ No newline at end of file
+ You'll need this URL for the client's `app.js` configuration mentioned in its README.
+
+## Launch your bots on Modal
+
+### Option 1: Direct Link
+
+Simply click on the url displayed after running the server or deploy step to launch an agent and be redirected to a Daily room to talk with the launched bot. This will use the OpenAI pipeline.
+
+### Option 2: Connect via an RTVI Client
+
+Follow the instructions provided in the [client folder's README](client/javascript/README.md) for building and running a custom client that connects to your Modal endpoint. The provided client provides a dropdown for choosing which bot pipeline to run.
+
+# Navigating your llm, server, and Pipecat logs
+
+In your [Modal dashboard](https://modal.com/apps), you should have two Apps listed under Live Apps:
+
+1. `example-vllm-openai-compatible`: This App contains the containers and logs used to run your self-hosted LLM. There will be just one App Function listed: `serve`. Click on this function to view logs for your LLM.
+2. `pipecat-modal`: This App contains the containers and logs used to run your `connect` endpoints and Pipecat pipelines. It will list two App Functions:
+ 1. `fastapi_app`: This function is running the endpoints that your client will interact with and initiate starting a new pipeline (`/`, `/connect`, `/status`). Click on this function to see logs for each endpoint hit.
+ 2. `bot_runner`: This function handles launching and running a bot pipeline. Click on this function to get a list of all pipeline runs and access each run's logs.
+
+# Modal + Pipecat Tips
+
+- In most other Pipecat examples, we use `Popen` to launch the pipeline process from the `/connect` endpoint. In this example, we use a Modal function instead. This allows us to run the pipelines using a separately defined Modal image as well as run each pipeline in an isolated container.
+- For the FastAPI and most common Pipecat Pipeline containers, a default `debian_slim` CPU-only should be all that's required to run. GPU containers are needed for self-hosted services.
+- To minimize cold starts of the pipeline and reduce latency for users, set `min_containers=1` on the Modal Function that launches the pipeline to ensure at least one warm instance of your function is always available.
+- For next steps on running a self-hosted llm and reducing latency, check out all of [Modal's LLM examples](https://modal.com/docs/examples/vllm_inference).
diff --git a/examples/deployment/modal-example/app.py b/examples/deployment/modal-example/app.py
deleted file mode 100644
index 9d12cabe6..000000000
--- a/examples/deployment/modal-example/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-import modal
-from bot import _voice_bot_process
-from fastapi import HTTPException
-from fastapi.responses import JSONResponse
-from loguru import logger
-
-MAX_SESSION_TIME = 15 * 60 # 15 minutes
-
-app = modal.App("pipecat-modal")
-
-
-image = modal.Image.debian_slim(python_version="3.12").pip_install_from_requirements(
- "requirements.txt"
-)
-
-
-@app.function(
- image=image,
- cpu=1.0,
- secrets=[modal.Secret.from_dotenv()],
- keep_warm=1,
- enable_memory_snapshot=True,
- max_inputs=1, # Do not reuse instances across requests
- retries=0,
-)
-def launch_bot_process(room_url: str, token: str):
- _voice_bot_process(room_url, token)
-
-
-@app.function(
- image=image,
- secrets=[modal.Secret.from_dotenv()],
-)
-@modal.web_endpoint(method="POST")
-async def start():
- from pipecat.transports.services.helpers.daily_rest import (
- DailyRESTHelper,
- DailyRoomParams,
- )
-
- logger.info("Request received")
-
- async with aiohttp.ClientSession() as session:
- daily_rest_helper = DailyRESTHelper(
- daily_api_key=os.getenv("DAILY_API_KEY", ""),
- daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
- aiohttp_session=session,
- )
-
- # Create new Daily room
- room = await daily_rest_helper.create_room(DailyRoomParams())
- if not room.url:
- raise HTTPException(
- status_code=500,
- detail="Unable to create room",
- )
- logger.info(f"Created room: {room.url}")
-
- # Create bot token for room
- token = await daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
- if not token:
- raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
-
- logger.info(f"Bot token created: {token}")
-
- # Spawn a new bot process
- launch_bot_process.spawn(room_url=room.url, token=token)
-
- # Return room URL to the user to join
- # Note: in production, you would want to return a token to the user
- return JSONResponse(content={"room_url": room.url, token: token})
diff --git a/examples/deployment/modal-example/bot.py b/examples/deployment/modal-example/bot.py
deleted file mode 100644
index 1d8949ca0..000000000
--- a/examples/deployment/modal-example/bot.py
+++ /dev/null
@@ -1,95 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main(room_url: str, token: str):
- transport = DailyTransport(
- room_url,
- token,
- "bot",
- DailyParams(
- audio_in_enabled=True,
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
-
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
- )
-
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
- ]
-
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
-
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
-
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
-
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
-
-
-def _voice_bot_process(room_url: str, token: str):
- asyncio.run(main(room_url, token))
diff --git a/examples/deployment/modal-example/client/javascript/.gitignore b/examples/deployment/modal-example/client/javascript/.gitignore
new file mode 100644
index 000000000..b512c09d4
--- /dev/null
+++ b/examples/deployment/modal-example/client/javascript/.gitignore
@@ -0,0 +1 @@
+node_modules
\ No newline at end of file
diff --git a/examples/deployment/modal-example/client/javascript/README.md b/examples/deployment/modal-example/client/javascript/README.md
new file mode 100644
index 000000000..f1cf6a678
--- /dev/null
+++ b/examples/deployment/modal-example/client/javascript/README.md
@@ -0,0 +1,29 @@
+# JavaScript Implementation
+
+Basic implementation using the [Pipecat JavaScript SDK](https://docs.pipecat.ai/client/js/introduction).
+
+## Setup
+
+1. Deploy the Modal server. See the main [README](../../README).
+
+2. Navigate to the `client/javascript` directory:
+
+```bash
+cd client/javascript
+```
+
+3. Modify the baseUrl in src/app.js to point to your deployed Modal endpoint
+
+4. Install dependencies:
+
+```bash
+npm install
+```
+
+5. Run the client app:
+
+```
+npm run dev
+```
+
+6. Visit http://localhost:5173 in your browser.
diff --git a/examples/deployment/modal-example/client/javascript/index.html b/examples/deployment/modal-example/client/javascript/index.html
new file mode 100644
index 000000000..eb9c96dc1
--- /dev/null
+++ b/examples/deployment/modal-example/client/javascript/index.html
@@ -0,0 +1,49 @@
+
+
+
+
+
+ AI Chatbot
+
+
+
+