opentelemetry-instrumentation-crewai

import os from opentelemetry import trace from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleExportSpanProcessor from opentelemetry.instrumentation.crewai import CrewAIInstrumentor from opentelemetry.instrumentation.openai import OpenAIInstrumentor # For LLM calls within CrewAI # Configure OpenTelemetry to export traces to the console resource = Resource.create({"service.name": "crewai-otel-example"}) tracer_provider = TracerProvider(resource=resource) span_processor = SimpleExportSpanProcessor(ConsoleSpanExporter()) tracer_provider.add_span_processor(span_processor) trace.set_tracer_provider(tracer_provider) # Instrument CrewAI and the underlying LLM (e.g., OpenAI) # It's crucial to instrument *both* for full visibility across agentic workflow and LLM calls. CrewAIInstrumentor().instrument() OpenAIInstrumentor().instrument() # Instrument your LLM provider as well # Ensure API key is set (e.g., from environment variable) # Replace with your actual LLM API key if not using OpenAI or different env var if not os.environ.get("OPENAI_API_KEY"): print("Please set the OPENAI_API_KEY environment variable to run this example.") exit(1) # Minimal CrewAI example from crewai import Agent, Task, Crew, Process # Define your agents researcher = Agent( role='Senior Research Analyst', goal='Uncover groundbreaking insights about AI advancements', backstory='A meticulous analyst dedicated to revealing the next big thing in AI.', verbose=True, allow_delegation=False ) writer = Agent( role='Content Strategist', goal='Craft compelling narratives about AI innovations', backstory='A skilled writer who transforms complex technical topics into engaging stories.', verbose=True, allow_delegation=False ) # Define your tasks task1 = Task( description='Research the latest trends in generative AI for the year 2026.', agent=researcher ) task2 = Task( description='Write a blog post summary (approx 200 words) based on the research findings.', agent=writer ) # Form the crew crew = Crew( agents=[researcher, writer], tasks=[task1, task2], process=Process.sequential, verbose=2 ) # Kickoff the crew print("\n### CrewAI Workflow Started (traces will be printed to console) ###") result = crew.kickoff() print("\n### CrewAI Workflow Finished ###") print(f"\nFinal Result: {result}")