{"slug": "pipecat-monitoring-observability-with-opentelemetry", "title": "Pipecat Monitoring & Observability with OpenTelemetry", "summary": "Pipecat, an open-source framework for voice AI agents, now supports monitoring and observability through OpenTelemetry, enabling developers to export logs, traces, and metrics to SigNoz for real-time visibility into latency, error rates, and usage trends. The integration, detailed in a new guide, requires Python 3.10+, a SigNoz account, and credentials for Deepgram, Cartesia, and OpenAI, and can be set up with automatic instrumentation and minimal code changes.", "body_md": "Overview\n\nThis guide walks you through setting up monitoring and observability for Pipecat using [OpenTelemetry](https://opentelemetry.io/) and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe the performance of various models, capture request/response details, and track system-level metrics in SigNoz, giving you real-time visibility into latency, error rates, and usage trends for your Pipecat applications.\n\nInstrumenting Pipecat in your AI applications with telemetry ensures full observability across your voice agent workflows, making it easier to debug issues, optimize performance, and understand user interactions. By leveraging SigNoz, you can analyze correlated traces, logs, and metrics in unified dashboards, configure alerts, and gain actionable insights to continuously improve reliability, responsiveness, and user experience.\n\nPrerequisites\n\n- A\n[SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key or[Self Hosted SigNoz instance](https://signoz.io/docs/install/self-host/) - Internet access to send telemetry data to SigNoz Cloud\n- Python 3.10+ with Pipecat installed\n- For Python:\n`uv`\n\ninstalled for managing Python packages [Deepgram Account](https://console.deepgram.com/signup)for STT[Cartesia Account](https://play.cartesia.ai/text-to-speech)for TTS- OpenAI API Key\n\nMonitoring Pipecat\n\nFor detailed information on instrumenting Pipecat applications with OpenTelemetry, see the [Pipecat OpenTelemetry documentation](https://docs.pipecat.ai/server/utilities/opentelemetry#opentelemetry-tracing).\n\nGet started with a sample Pipecat starter project by following the [Pipecat quickstart docs](https://docs.pipecat.ai/getting-started/quickstart)\n\nNo-code auto-instrumentation is recommended for quick setup with minimal code changes. It's ideal when you want to get observability up and running without modifying your application code and are leveraging standard instrumentor libraries.\n\nStep 1: Clone the sample voice agent project and setup dependencies\n\n```\ngit clone https://github.com/pipecat-ai/pipecat-quickstart.git\ncd agent-starter-python\nuv sync\n```\n\nStep 2: Setup Credentials\n\nCopy .env.example to .env and filling in the required keys:\n\n`DEEPGRAM_API_KEY`\n\n`OPENAI_API_KEY`\n\n`CARTESIA_API_KEY`\n\nStep 3: Add Automatic Instrumentation\n\n```\nuv pip install opentelemetry-distro opentelemetry-exporter-otlp\nuv run opentelemetry-bootstrap -a requirements | uv pip install --requirement -\n```\n\nStep 4: Instrument your Pipecat application\n\n```\ntask = PipelineTask(\n    pipeline,\n    params=PipelineParams(\n        enable_metrics=True,                              # Required for some service metrics\n    ),\n    enable_tracing=True,                                  # Enable tracing for this task\n    enable_turn_tracking=True,                            # Enable turn tracking for this task\n    conversation_id=\"customer-123\",                       # Optional - will auto-generate if not provided\n    additional_span_attributes={\"session.id\": \"abc-123\"} # Optional - additional attributes to attach to the otel span\n)\n```\n\nSee this [example repo](https://github.com/pipecat-ai/pipecat-examples/blob/main/open-telemetry/langfuse/bot.py) for more details on how to configure instrumentation.\n\nStep 5: Your `bot.py`\n\nshould look something like this:\n\n```\n#\n# Copyright (c) 2024–2025, Daily\n#\n# SPDX-License-Identifier: BSD 2-Clause License\n#\n \n\"\"\"Pipecat Quickstart Example.\n \nThe example runs a simple voice AI bot that you can connect to using your\nbrowser and speak with it. You can also deploy this bot to Pipecat Cloud.\n \nRequired AI services:\n- Deepgram (Speech-to-Text)\n- OpenAI (LLM)\n- Cartesia (Text-to-Speech)\n \nRun the bot using::\n \n    uv run bot.py\n\"\"\"\n \nimport os\n \nfrom dotenv import load_dotenv\nfrom loguru import logger\n \nprint(\"🚀 Starting Pipecat bot...\")\nprint(\"⏳ Loading models and imports (20 seconds, first run only)\\n\")\n \nlogger.info(\"Loading Local Smart Turn Analyzer V3...\")\nfrom pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3\n \nlogger.info(\"✅ Local Smart Turn Analyzer V3 loaded\")\nlogger.info(\"Loading Silero VAD model...\")\nfrom pipecat.audio.vad.silero import SileroVADAnalyzer\n \nlogger.info(\"✅ Silero VAD model loaded\")\n \nfrom pipecat.audio.vad.vad_analyzer import VADParams\nfrom pipecat.frames.frames import LLMRunFrame\n \nlogger.info(\"Loading pipeline components...\")\nfrom pipecat.pipeline.pipeline import Pipeline\nfrom pipecat.pipeline.runner import PipelineRunner\nfrom pipecat.pipeline.task import PipelineParams, PipelineTask\nfrom pipecat.processors.aggregators.llm_context import LLMContext\nfrom pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair\nfrom pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor\nfrom pipecat.runner.types import RunnerArguments\nfrom pipecat.runner.utils import create_transport\nfrom pipecat.services.cartesia.tts import CartesiaTTSService\nfrom pipecat.services.deepgram.stt import DeepgramSTTService\nfrom pipecat.services.openai.llm import OpenAILLMService\nfrom pipecat.transports.base_transport import BaseTransport, TransportParams\nfrom pipecat.transports.daily.transport import DailyParams\n \n \nlogger.info(\"✅ All components loaded successfully!\")\n \nload_dotenv(override=True)\n \n \nasync def run_bot(transport: BaseTransport, runner_args: RunnerArguments):\n    logger.info(f\"Starting bot\")\n \n    stt = DeepgramSTTService(api_key=os.getenv(\"DEEPGRAM_API_KEY\"))\n \n    tts = CartesiaTTSService(\n        api_key=os.getenv(\"CARTESIA_API_KEY\"),\n        voice_id=\"71a7ad14-091c-4e8e-a314-022ece01c121\",  # British Reading Lady\n    )\n \n    llm = OpenAILLMService(api_key=os.getenv(\"OPENAI_API_KEY\"))\n \n    messages = [\n        {\n            \"role\": \"system\",\n            \"content\": \"You are a friendly AI assistant. Respond naturally and keep your answers conversational.\",\n        },\n    ]\n \n    context = LLMContext(messages)\n    context_aggregator = LLMContextAggregatorPair(context)\n \n    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))\n \n    pipeline = Pipeline(\n        [\n            transport.input(),  # Transport user input\n            rtvi,  # RTVI processor\n            stt,\n            context_aggregator.user(),  # User responses\n            llm,  # LLM\n            tts,  # TTS\n            transport.output(),  # Transport bot output\n            context_aggregator.assistant(),  # Assistant spoken responses\n        ]\n    )\n \n    # Enable tracing in your PipelineTask\n    task = PipelineTask(\n        pipeline,\n        params=PipelineParams(\n            enable_metrics=True,\n            enable_usage_metrics=True,\n        ),\n        enable_tracing=True,                                  # Enable tracing for this task\n        enable_turn_tracking=True,\n        observers=[RTVIObserver(rtvi)],\n    )\n \n    @transport.event_handler(\"on_client_connected\")\n    async def on_client_connected(transport, client):\n        logger.info(f\"Client connected\")\n        # Kick off the conversation.\n        messages.append({\"role\": \"system\", \"content\": \"Say hello and briefly introduce yourself.\"})\n        await task.queue_frames([LLMRunFrame()])\n \n    @transport.event_handler(\"on_client_disconnected\")\n    async def on_client_disconnected(transport, client):\n        logger.info(f\"Client disconnected\")\n        await task.cancel()\n \n    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)\n \n    await runner.run(task)\n \n \nasync def bot(runner_args: RunnerArguments):\n    \"\"\"Main bot entry point for the bot starter.\"\"\"\n \n    transport_params = {\n        \"daily\": lambda: DailyParams(\n            audio_in_enabled=True,\n            audio_out_enabled=True,\n            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),\n            turn_analyzer=LocalSmartTurnAnalyzerV3(),\n        ),\n        \"webrtc\": lambda: TransportParams(\n            audio_in_enabled=True,\n            audio_out_enabled=True,\n            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),\n            turn_analyzer=LocalSmartTurnAnalyzerV3(),\n        ),\n    }\n \n    transport = await create_transport(runner_args, transport_params)\n \n    await run_bot(transport, runner_args)\n \n \nif __name__ == \"__main__\":\n    from pipecat.runner.run import main\n \n    main()\n```\n\nStep 6: Run your application with auto-instrumentation\n\n```\nOTEL_RESOURCE_ATTRIBUTES=\"service.name=<service_name>\" \\\nOTEL_EXPORTER_OTLP_ENDPOINT=\"https://ingest.<region>.signoz.cloud:443\" \\\nOTEL_EXPORTER_OTLP_HEADERS=\"signoz-ingestion-key=<your-ingestion-key>\" \\\nOTEL_EXPORTER_OTLP_PROTOCOL=grpc \\\nOTEL_TRACES_EXPORTER=otlp \\\nOTEL_METRICS_EXPORTER=otlp \\\nOTEL_LOGS_EXPORTER=otlp \\\nOTEL_PYTHON_LOG_CORRELATION=true \\\nOTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \\\n<your_run_command with opentelemetry-instrument>\n```\n\nis the name of your service`<service_name>`\n\n`<region>`\n\n: Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)`<your-ingestion-key>`\n\n: Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)- Replace\n`<your_run_command>`\n\nwith the actual command you would use to run your application. In this case we would use:`uv run opentelemetry-instrument python bot.py`\n\nOpen [http://localhost:7860](http://localhost:7860) in your browser and click `Connect`\n\nto start talking to your bot.\n\nCode-based instrumentation gives you fine-grained control over your telemetry configuration. Use this approach when you need to customize resource attributes, sampling strategies, or integrate with existing observability infrastructure.\n\nStep 1: Clone the sample voice agent project and setup dependencies\n\n```\ngit clone https://github.com/pipecat-ai/pipecat-quickstart.git\ncd agent-starter-python\nuv sync\n```\n\nStep 2: Setup Credentials\n\nCopy .env.example to .env and filling in the required keys:\n\n`DEEPGRAM_API_KEY`\n\n`OPENAI_API_KEY`\n\n`CARTESIA_API_KEY`\n\nStep 3: Install additional OpenTelemetry dependencies\n\n```\nuv pip install \\\n  opentelemetry-api \\\n  opentelemetry-sdk \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics\n```\n\nStep 4: Import the necessary modules in your Python application\n\n**Traces:**\n\n``` python\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n```\n\n**Logs:**\n\n``` python\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nfrom opentelemetry._logs import set_logger_provider\nimport logging\n```\n\n**Metrics:**\n\n``` python\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nfrom opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor\n```\n\nStep 5: Set up the OpenTelemetry Span Exporter to send traces directly to SigNoz Cloud\n\n``` python\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\nimport os\n \n \nspan_exporter = OTLPSpanExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_TRACES_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\n```\n\n→ SigNoz Cloud trace endpoint with appropriate`OTEL_EXPORTER_TRACES_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/traces`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nStep 6: Setup Logs\n\n``` python\nimport logging\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry._logs import set_logger_provider\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nimport os\n \nresource = Resource.create({\"service.name\": \"<service_name>\"})\nlogger_provider = LoggerProvider(resource=resource)\nset_logger_provider(logger_provider)\n \notlp_log_exporter = OTLPLogExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_LOGS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nlogger_provider.add_log_record_processor(\n    BatchLogRecordProcessor(otlp_log_exporter)\n)\n# Attach OTel logging handler to root logger\nhandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)\nlogging.basicConfig(level=logging.INFO, handlers=[handler])\n \nlogger = logging.getLogger(__name__)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_LOGS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/logs`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nStep 7: Setup Metrics\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nimport os\n \nresource = Resource.create({\"service.name\": \"<service-name>\"})\nmetric_exporter = OTLPMetricExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_METRICS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nreader = PeriodicExportingMetricReader(metric_exporter)\nmetric_provider = MeterProvider(metric_readers=[reader], resource=resource)\nmetrics.set_meter_provider(metric_provider)\n \nmeter = metrics.get_meter(__name__)\n \n# turn on out-of-the-box metrics\nSystemMetricsInstrumentor().instrument()\nHTTPXClientInstrumentor().instrument()\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_METRICS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/metrics`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\n📌 Note: SystemMetricsInstrumentor provides system metrics (CPU, memory, etc.), and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. If you want to add custom metrics to your Pipecat application, see\n\n[Python Custom Metrics].\n\nStep 8: Instrument your Pipecat application\n\n```\n#\n# Copyright (c) 2024–2025, Daily\n#\n# SPDX-License-Identifier: BSD 2-Clause License\n#\n \n\"\"\"Pipecat Quickstart Example.\n \nThe example runs a simple voice AI bot that you can connect to using your\nbrowser and speak with it. You can also deploy this bot to Pipecat Cloud.\n \nRequired AI services:\n- Deepgram (Speech-to-Text)\n- OpenAI (LLM)\n- Cartesia (Text-to-Speech)\n \nRun the bot using::\n \n    uv run bot.py\n\"\"\"\n \nimport os\n \nfrom dotenv import load_dotenv\nfrom loguru import logger\n \nprint(\"🚀 Starting Pipecat bot...\")\nprint(\"⏳ Loading models and imports (20 seconds, first run only)\\n\")\n \nlogger.info(\"Loading Local Smart Turn Analyzer V3...\")\nfrom pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3\n \nlogger.info(\"✅ Local Smart Turn Analyzer V3 loaded\")\nlogger.info(\"Loading Silero VAD model...\")\nfrom pipecat.audio.vad.silero import SileroVADAnalyzer\n \nlogger.info(\"✅ Silero VAD model loaded\")\n \nfrom pipecat.audio.vad.vad_analyzer import VADParams\nfrom pipecat.frames.frames import LLMRunFrame\n \nlogger.info(\"Loading pipeline components...\")\nfrom pipecat.pipeline.pipeline import Pipeline\nfrom pipecat.pipeline.runner import PipelineRunner\nfrom pipecat.pipeline.task import PipelineParams, PipelineTask\nfrom pipecat.processors.aggregators.llm_context import LLMContext\nfrom pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair\nfrom pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor\nfrom pipecat.runner.types import RunnerArguments\nfrom pipecat.runner.utils import create_transport\nfrom pipecat.services.cartesia.tts import CartesiaTTSService\nfrom pipecat.services.deepgram.stt import DeepgramSTTService\nfrom pipecat.services.openai.llm import OpenAILLMService\nfrom pipecat.transports.base_transport import BaseTransport, TransportParams\nfrom pipecat.transports.daily.transport import DailyParams\nfrom pipecat.utils.tracing.setup import setup_tracing \n \n \nlogger.info(\"✅ All components loaded successfully!\")\n \nload_dotenv(override=True)\n \n \nasync def run_bot(transport: BaseTransport, runner_args: RunnerArguments):\n    logger.info(f\"Starting bot\")\n \n    stt = DeepgramSTTService(api_key=os.getenv(\"DEEPGRAM_API_KEY\"))\n \n    tts = CartesiaTTSService(\n        api_key=os.getenv(\"CARTESIA_API_KEY\"),\n        voice_id=\"71a7ad14-091c-4e8e-a314-022ece01c121\",  # British Reading Lady\n    )\n \n    llm = OpenAILLMService(api_key=os.getenv(\"OPENAI_API_KEY\"))\n \n    messages = [\n        {\n            \"role\": \"system\",\n            \"content\": \"You are a friendly AI assistant. Respond naturally and keep your answers conversational.\",\n        },\n    ]\n \n    context = LLMContext(messages)\n    context_aggregator = LLMContextAggregatorPair(context)\n \n    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))\n \n    pipeline = Pipeline(\n        [\n            transport.input(),  # Transport user input\n            rtvi,  # RTVI processor\n            stt,\n            context_aggregator.user(),  # User responses\n            llm,  # LLM\n            tts,  # TTS\n            transport.output(),  # Transport bot output\n            context_aggregator.assistant(),  # Assistant spoken responses\n        ]\n    )\n \n    setup_tracing(\n        service_name=\"<service-name>\",\n        exporter=span_exporter, #from initialized Span Exporter in Step 5\n        console_export=False,  # Set to True for debug output\n    )\n \n    # Enable tracing in your PipelineTask\n    task = PipelineTask(\n        pipeline,\n        params=PipelineParams(\n            enable_metrics=True,\n            enable_usage_metrics=True,\n        ),\n        enable_tracing=True,                                  # Enable tracing for this task\n        enable_turn_tracking=True,\n        observers=[RTVIObserver(rtvi)],\n    )\n \n    @transport.event_handler(\"on_client_connected\")\n    async def on_client_connected(transport, client):\n        logger.info(f\"Client connected\")\n        # Kick off the conversation.\n        messages.append({\"role\": \"system\", \"content\": \"Say hello and briefly introduce yourself.\"})\n        await task.queue_frames([LLMRunFrame()])\n \n    @transport.event_handler(\"on_client_disconnected\")\n    async def on_client_disconnected(transport, client):\n        logger.info(f\"Client disconnected\")\n        await task.cancel()\n \n    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)\n \n    await runner.run(task)\n \n \nasync def bot(runner_args: RunnerArguments):\n    \"\"\"Main bot entry point for the bot starter.\"\"\"\n \n    transport_params = {\n        \"daily\": lambda: DailyParams(\n            audio_in_enabled=True,\n            audio_out_enabled=True,\n            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),\n            turn_analyzer=LocalSmartTurnAnalyzerV3(),\n        ),\n        \"webrtc\": lambda: TransportParams(\n            audio_in_enabled=True,\n            audio_out_enabled=True,\n            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),\n            turn_analyzer=LocalSmartTurnAnalyzerV3(),\n        ),\n    }\n \n    transport = await create_transport(runner_args, transport_params)\n \n    await run_bot(transport, runner_args)\n \n \nif __name__ == \"__main__\":\n    from pipecat.runner.run import main\n \n    main()\n```\n\nStep 9: Run your example `bot.py`\n\n```\nuv run bot.py\n```\n\nOpen [http://localhost:7860](http://localhost:7860) in your browser and click `Connect`\n\nto start talking to your bot.\n\nView Traces, Logs, and Metrics in SigNoz\n\nYour Pipecat voice agent usage should now automatically emit traces, logs, and metrics.\n\nYou should be able to view traces in Signoz Cloud under the traces tab:\n\nWhen you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes.\n\nYou should be able to view logs in Signoz Cloud under the logs tab. You can also view logs by clicking on the “Related Logs” button in the trace view to see correlated logs:\n\nWhen you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:\n\nYou should be able to see Pipecat related metrics in Signoz Cloud under the metrics tab:\n\nWhen you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:\n\nTroubleshooting\n\n[Troubleshooting](#troubleshooting)\n\nIf you don't see your telemetry data:\n\n**Verify network connectivity**- Ensure your application can reach SigNoz Cloud endpoints** Check ingestion key**- Verify your SigNoz ingestion key is correct** Wait for data**- OpenTelemetry batches data before sending, so wait 10-30 seconds after making API calls** Try a console exporter**— Enable a console exporter locally to confirm that your application is generating telemetry data before it's sent to SigNoz\n\nNext Steps\n\nYou can also check out our custom Pipecat dashboard [here](https://signoz.io/docs/dashboards/dashboard-templates/pipecat-dashboard/) which provides specialized visualizations for monitoring your Pipecat usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.\n\nRelated integrations\n\nTrace the rest of the stack behind your voice agents:\n\n[LiveKit observability with OpenTelemetry](https://signoz.io/docs/livekit-observability/)- trace voice agent sessions, turn latency, and STT and TTS calls[Mastra observability with OpenTelemetry](https://signoz.io/docs/mastra-observability/)- trace Mastra agents, workflows, and tool calls[LlamaIndex observability with OpenTelemetry](https://signoz.io/docs/llamaindex-observability/)- trace RAG queries, retrievers, and index operations[Pydantic AI observability with OpenTelemetry](https://signoz.io/docs/pydantic-ai-observability/)- trace agent runs, output validation, and model calls[Monitor the Anthropic API with OpenTelemetry](https://signoz.io/docs/anthropic-monitoring/)- trace Claude requests and break down input, output, and cache token usage\n\nBrowse [all LLM observability integrations](https://signoz.io/docs/llm-observability/) to instrument the rest of your stack.", "url": "https://wpnews.pro/news/pipecat-monitoring-observability-with-opentelemetry", "canonical_source": "https://signoz.io/docs/pipecat-monitoring", "published_at": "2026-08-03 00:00:00+00:00", "updated_at": "2026-08-05 01:54:32.170712+00:00", "lang": "en", "topics": ["ai-tools", "developer-tools", "ai-infrastructure"], "entities": ["Pipecat", "OpenTelemetry", "SigNoz", "Deepgram", "Cartesia", "OpenAI"], "alternates": {"html": "https://wpnews.pro/news/pipecat-monitoring-observability-with-opentelemetry", "markdown": "https://wpnews.pro/news/pipecat-monitoring-observability-with-opentelemetry.md", "text": "https://wpnews.pro/news/pipecat-monitoring-observability-with-opentelemetry.txt", "jsonld": "https://wpnews.pro/news/pipecat-monitoring-observability-with-opentelemetry.jsonld"}}