CrewAI Observability & Monitoring with OpenTelemetry

SigNoz has released a guide for instrumenting CrewAI with OpenTelemetry to enable real-time observability into agent, model, and tool performance across AI workflows. The integration allows users to export traces, logs, and metrics to SigNoz for unified dashboards, alerting, and debugging of failed tasks, slow agents, and LLM usage patterns. The setup involves installing OpenTelemetry and OpenInference packages, configuring automatic instrumentation, and running example code with CrewAI agents and tasks.

What is CrewAI Observability? CrewAI observability gives you real-time visibility into agent, model, and tool performance across your AI workflows. This guide walks you through instrumenting CrewAI with OpenTelemetry https://opentelemetry.io/ and exporting traces, logs, and metrics to SigNoz, so you can track latency, error rates, and usage trends in your CrewAI applications. With full CrewAI observability in SigNoz, you can correlate traces, logs, and metrics in unified dashboards, configure alerts, and gain actionable insights to improve reliability and responsiveness. CrewAI monitoring across your AI workflows makes it straightforward to debug failed tasks, identify slow agents, and understand LLM usage patterns. Prerequisites - SigNoz setup choose one : SigNoz Cloud account https://signoz.io/teams/ with an active ingestion key- Self-hosted SigNoz instance - Internet access to send telemetry data to SigNoz Cloud - CrewAI integrated into your app - Basic understanding of AI Agents and tool calling workflow - For Python: pip installed for managing Python packages and optional but recommended a Python virtual environment to isolate dependencies CrewAI Monitoring with OpenTelemetry No-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. Step 1: Install the necessary packages in your Python environment. pip install \ opentelemetry-distro \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics \ openinference-instrumentation-crewai \ openinference-instrumentation-openai \ crewai \ crewai-tools Step 2: Add Automatic Instrumentation opentelemetry-bootstrap --action=install Step 3: Configure logging level To ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher: python import logging logging.getLogger .setLevel logging.INFO This sets the minimum log level for the root logger to INFO, which ensures that logger.info calls and higher severity logs WARNING, ERROR, CRITICAL are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz. Step 4: Run an example python import os from crewai import Agent, Task, Crew, Process from crewai tools import SerperDevTool search tool = SerperDevTool Define your agents with roles and goals researcher = Agent role='Senior Research Analyst', goal='Uncover cutting-edge developments in AI and data science', backstory="""You work at a leading tech think tank. Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting actionable insights.""", verbose=True, allow delegation=False, You can pass an optional llm attribute specifying what model you wanna use. llm=ChatOpenAI model name="gpt-3.5", temperature=0.7 , tools= search tool writer = Agent role='Tech Content Strategist', goal='Craft compelling content on tech advancements', backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles. You transform complex concepts into compelling narratives.""", verbose=True, allow delegation=True Create tasks for your agents task1 = Task description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024. Identify key trends, breakthrough technologies, and potential industry impacts.""", expected output="Full analysis report in bullet points", agent=researcher task2 = Task description="""Using the insights provided, develop an engaging blog post that highlights the most significant AI advancements. Your post should be informative yet accessible, catering to a tech-savvy audience. Make it sound cool, avoid complex words so it doesn't sound like AI.""", expected output="Full blog post of at least 4 paragraphs", agent=writer Instantiate your crew with a sequential process crew = Crew agents= researcher, writer , tasks= task1, task2 , verbose=True, process=Process.sequential Get your crew to work result = crew.kickoff print " " print result 📌 Note: Before running this code, ensure that the API key of the specific LLM you are choosing is set as an env variable. In this example, since OpenAI is being used, set OPENAI API KEY with your working API key. Additionally, for this specific example, you need to create a Serper account , generate an API key, and set it as the environment variable SERPER API KEY . Step 5: Run your application with auto-instrumentation OTEL RESOURCE ATTRIBUTES="service.name=<service name " \ OTEL EXPORTER OTLP ENDPOINT="https://ingest.<region .signoz.cloud:443" \ OTEL EXPORTER OTLP HEADERS="signoz-ingestion-key=<your-ingestion-key " \ OTEL EXPORTER OTLP PROTOCOL=grpc \ OTEL TRACES EXPORTER=otlp \ OTEL METRICS EXPORTER=otlp \ OTEL LOGS EXPORTER=otlp \ OTEL PYTHON LOG CORRELATION=true \ OTEL PYTHON LOGGING AUTO INSTRUMENTATION ENABLED=true \ opentelemetry-instrument <your run command is the name of your service <service name <region : Your SigNoz Cloud region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint <your-ingestion-key : Your SigNoz ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ - Replace <your run command with the actual command you would use to run your application. For example: python main.py Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Code-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. Step 1: Install the necessary packages in your Python environment. CrewAI supports a variety of LLM providers for agent workflows, including OpenAI, Anthropic, Google Gemini/Vertex AI , Azure, AWS Bedrock, Mistral, Groq, Ollama, and more. See the full list of supported LLM providers https://docs.crewai.com/en/concepts/llms . Based on which LLM provider you're using with CrewAI, you'll need to install the corresponding OpenInference instrumentor to track LLM-related traces. See the OpenInference documentation https://arize.com/docs/ax/observe/tracing/configure-tracing-options/instrument-with-openinference-helpers for available instrumentors. For this example using OpenAI: pip install \ opentelemetry-api \ opentelemetry-sdk \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics \ openinference-instrumentation-crewai \ openinference-instrumentation-openai \ crewai \ crewai-tools 📌 Note: If you're using a different LLM provider e.g., Anthropic, Bedrock, Mistral , replace openinference-instrumentation-openai with the appropriate instrumentor package such as openinference-instrumentation-anthropic , openinference-instrumentation-bedrock , etc. Step 2: Import the necessary modules in your Python application Traces: python from openinference.instrumentation.crewai import CrewAIInstrumentor from openinference.instrumentation.openai import OpenAIInstrumentor from opentelemetry import trace from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.exporter.otlp.proto.http.trace exporter import OTLPSpanExporter Logs: python from opentelemetry.sdk. logs import LoggerProvider, LoggingHandler from opentelemetry.sdk. logs.export import BatchLogRecordProcessor from opentelemetry.exporter.otlp.proto.http. log exporter import OTLPLogExporter from opentelemetry. logs import set logger provider import logging Metrics: python from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.exporter.otlp.proto.http.metric exporter import OTLPMetricExporter from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader from opentelemetry import metrics from opentelemetry.instrumentation.system metrics import SystemMetricsInstrumentor from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud python from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.exporter.otlp.proto.http.trace exporter import OTLPSpanExporter from opentelemetry import trace import os resource = Resource.create {"service.name": "<service name "} provider = TracerProvider resource=resource span exporter = OTLPSpanExporter endpoint= os.getenv "OTEL EXPORTER TRACES ENDPOINT" , headers={"signoz-ingestion-key": os.getenv "SIGNOZ INGESTION KEY" }, processor = BatchSpanProcessor span exporter provider.add span processor processor trace.set tracer provider provider is the name of your service <service name → SigNoz Cloud trace endpoint with appropriate OTEL EXPORTER TRACES ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/traces → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Step 4: Instrument CrewAI and your LLM provider using their respective instrumentors with the configured Tracer Provider python from openinference.instrumentation.crewai import CrewAIInstrumentor from openinference.instrumentation.openai import OpenAIInstrumentor CrewAIInstrumentor .instrument tracer provider=provider OpenAIInstrumentor .instrument tracer provider=provider 📌 Important: You must instrument both CrewAI and the LLM provider you're using with CrewAI. In this example, we're using OpenAI, so we instrument both CrewAIInstrumentor and OpenAIInstrumentor . If you're using a different LLM provider e.g., Anthropic, Bedrock, Mistral , replace OpenAIInstrumentor with the appropriate instrumentor for your provider.📌 Important: Place this code at the start of your application logic - before any CrewAI functions are called or used - to ensure telemetry is correctly captured. Step 5 : Setup Logs python import logging from opentelemetry.sdk.resources import Resource from opentelemetry. logs import set logger provider from opentelemetry.sdk. logs import LoggerProvider, LoggingHandler from opentelemetry.sdk. logs.export import BatchLogRecordProcessor from opentelemetry.exporter.otlp.proto.http. log exporter import OTLPLogExporter import os resource = Resource.create {"service.name": "<service name "} logger provider = LoggerProvider resource=resource set logger provider logger provider otlp log exporter = OTLPLogExporter endpoint= os.getenv "OTEL EXPORTER LOGS ENDPOINT" , headers={"signoz-ingestion-key": os.getenv "SIGNOZ INGESTION KEY" }, logger provider.add log record processor BatchLogRecordProcessor otlp log exporter Attach OTel logging handler to root logger handler = LoggingHandler level=logging.INFO, logger provider=logger provider logging.basicConfig level=logging.INFO, handlers= handler logger = logging.getLogger name is the name of your service <service name → SigNoz Cloud endpoint with appropriate OTEL EXPORTER LOGS ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/logs → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Step 6 : Setup Metrics python from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.exporter.otlp.proto.http.metric exporter import OTLPMetricExporter from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader from opentelemetry import metrics from opentelemetry.instrumentation.system metrics import SystemMetricsInstrumentor import os resource = Resource.create {"service.name": "<service-name "} metric exporter = OTLPMetricExporter endpoint= os.getenv "OTEL EXPORTER METRICS ENDPOINT" , headers={"signoz-ingestion-key": os.getenv "SIGNOZ INGESTION KEY" }, reader = PeriodicExportingMetricReader metric exporter metric provider = MeterProvider metric readers= reader , resource=resource metrics.set meter provider metric provider meter = metrics.get meter name turn on out-of-the-box metrics SystemMetricsInstrumentor .instrument HTTPXClientInstrumentor .instrument is the name of your service <service name → SigNoz Cloud endpoint with appropriate OTEL EXPORTER METRICS ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/metrics → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ 📌 Note: SystemMetricsInstrumentor provides system metrics CPU, memory, etc. , and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. These are not CrewAI-specific metrics. CrewAI does not expose metrics as part of their SDK. If you want to add custom metrics to your CrewAI application, see Python Custom Metrics . Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Step 7: Run an example python import os from crewai import Agent, Task, Crew, Process from crewai tools import SerperDevTool search tool = SerperDevTool Define your agents with roles and goals researcher = Agent role='Senior Research Analyst', goal='Uncover cutting-edge developments in AI and data science', backstory="""You work at a leading tech think tank. Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting actionable insights.""", verbose=True, allow delegation=False, You can pass an optional llm attribute specifying what model you wanna use. llm=ChatOpenAI model name="gpt-3.5", temperature=0.7 , tools= search tool writer = Agent role='Tech Content Strategist', goal='Craft compelling content on tech advancements', backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles. You transform complex concepts into compelling narratives.""", verbose=True, allow delegation=True Create tasks for your agents task1 = Task description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024. Identify key trends, breakthrough technologies, and potential industry impacts.""", expected output="Full analysis report in bullet points", agent=researcher task2 = Task description="""Using the insights provided, develop an engaging blog post that highlights the most significant AI advancements. Your post should be informative yet accessible, catering to a tech-savvy audience. Make it sound cool, avoid complex words so it doesn't sound like AI.""", expected output="Full blog post of at least 4 paragraphs", agent=writer Instantiate your crew with a sequential process crew = Crew agents= researcher, writer , tasks= task1, task2 , verbose=True, process=Process.sequential Get your crew to work result = crew.kickoff print " " print result 📌 Note: Before running this code, ensure that the API key of the specific LLM you are choosing is set as an env variable. In this example, since OpenAI is being used, set OPENAI API KEY with your working API key. Additionally, for this specific example, you need to create a Serper account , generate an API key, and set it as the environment variable SERPER API KEY . View CrewAI Traces, Logs, and Metrics in SigNoz Once configured, your CrewAI application automatically emits traces, logs, and metrics. CrewAI traces are available in SigNoz under the Traces tab: When 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. CrewAI logs are available in SigNoz under the Logs tab. You can also view correlated logs by clicking the “Related Logs” button in the trace view: When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes: CrewAI metrics are available in SigNoz under the Metrics tab: When you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes: CrewAI Monitoring Dashboard The CrewAI Monitoring Dashboard https://signoz.io/docs/dashboards/dashboard-templates/crewai-dashboard/ provides specialized visualizations for CrewAI observability, including pre-built charts for agent performance, tool usage, and LLM call metrics, along with import instructions to get started quickly.