{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/5"}],"enrichment":{"capability":"Automatically instruments CrewAI agent applications with OpenTelemetry tracing, sending execution traces to monitoring backends like Arize Phoenix or Arize AX for observability.","skillfed_tags":["observability","crewai","opentelemetry"],"use_cases":["Debug crew execution by inspecting traces in Phoenix to see which agents ran, what tools they called, and where latency occurred.","Monitor production CrewAI applications by exporting traces to Arize AX for alerting, performance analysis, and cost tracking.","Validate agent behavior during development by replaying traces and checking that agents followed expected task sequences.","Correlate crew performance with LLM provider costs and latency by analyzing span metadata and LLM call counts.","Troubleshoot tool failures by examining detailed traces showing which tools were invoked and what data they received."],"what_it_does":"This package wraps CrewAI agent execution to emit OpenTelemetry spans, capturing the structure and behavior of crews, agents, and tool calls. It integrates with the OpenInference semantic conventions to standardize how LLM agent traces are recorded, then exports them to any OpenTelemetry-compatible backend\u2014most commonly Arize Phoenix for local inspection or Arize AX for cloud-hosted analysis.\n\nThe instrumentation works in two modes: the default wrapper-based approach, which intercepts Python method calls on CrewAI objects, and an event-listener mode for low-code or AMP environments where execution flows through CrewAI's event bus instead. Once enabled, it requires minimal setup\u2014typically just instantiating the instrumentor with a tracer provider\u2014and automatically captures agent roles, task descriptions, tool invocations, and LLM calls without modifying your crew code.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, carries a permissive license, and solves a real need\u2014CrewAI applications are difficult to debug and monitor without instrumentation. If you run CrewAI in production or need visibility into agent behavior, this is the standard path. Install it if you have an OpenTelemetry collector available; skip it only if your crews are purely local experiments with no observability requirement."},"id":"openinference-instrumentation-crewai","links":{"html":"https://skillfed.io/packages/openinference-instrumentation-crewai","md":"https://skillfed.io/packages/openinference-instrumentation-crewai.md","pypi":"https://pypi.org/project/openinference-instrumentation-crewai/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"openinference-instrumentation-crewai","python_support":"supports_current","summary":"OpenInference Crewai Instrumentation"},"popularity":{"monthly_downloads":89205,"position":13674,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.12"}