splunk-otel-instrumentation-crewai 0.1.2

OpenTelemetry CrewAI instrumentation

This library provides OpenTelemetry instrumentation for CrewAI, a framework for orchestrating autonomous AI agents.

Installation

pip install splunk-otel-instrumentation-crewai

Usage

from opentelemetry.instrumentation.crewai import CrewAIInstrumentor
from crewai import Agent, Task, Crew

# Instrument CrewAI
CrewAIInstrumentor().instrument()

# Create your crew
agent = Agent(
    role="Research Analyst",
    goal="Provide accurate research",
    backstory="Expert researcher with attention to detail",
)

task = Task(
    description="Research the latest AI trends",
    expected_output="A comprehensive report on AI trends",
    agent=agent,
)

crew = Crew(agents=[agent], tasks=[task])

# Run your crew - telemetry is automatically captured
result = crew.kickoff()

What Gets Instrumented

This instrumentation captures:

• Crews → Mapped to Workflow spans

• Tasks → Mapped to Step spans

• Agents → Mapped to AgentInvocation spans

• Tool Usage → Mapped to ToolCall spans

All spans are properly nested with correct parent-child relationships and include rich attributes about the operation.

Compositional Instrumentation

This instrumentation focuses on CrewAI’s workflow orchestration. For complete observability:

CrewAI Only

from opentelemetry.instrumentation.crewai import CrewAIInstrumentor

CrewAIInstrumentor().instrument()

Provides workflow structure but no LLM call details.

CrewAI + OpenAI

from opentelemetry.instrumentation.crewai import CrewAIInstrumentor
from opentelemetry.instrumentation.openai import OpenAIInstrumentor

CrewAIInstrumentor().instrument()
OpenAIInstrumentor().instrument()

Adds LLM call spans with token usage, model names, and latency metrics.

Full Stack (CrewAI + OpenAI + Vector Store)

from opentelemetry.instrumentation.crewai import CrewAIInstrumentor
from opentelemetry.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.instrumentation.chromadb import ChromaDBInstrumentor

CrewAIInstrumentor().instrument()
OpenAIInstrumentor().instrument()
ChromaDBInstrumentor().instrument()

Complete RAG workflow visibility with vector store operations.

Configuration

Environment Variables

# Disable CrewAI's built-in telemetry (recommended)
export CREWAI_DISABLE_TELEMETRY=true

Instrumentation Options

from opentelemetry.instrumentation.crewai import CrewAIInstrumentor

# Basic instrumentation
CrewAIInstrumentor().instrument()

# With custom tracer provider
CrewAIInstrumentor().instrument(tracer_provider=my_tracer_provider)

# Uninstrumentation
CrewAIInstrumentor().uninstrument()

Requirements

• Python >= 3.9

• CrewAI >= 0.70.0

• OpenTelemetry API >= 1.38

• splunk-otel-util-genai >= 0.1.4

Trace Hierarchy Example

Crew: Customer Support (Workflow)
├── Task: inquiry_resolution (Step)
│   └── Agent: Senior Support Representative
│       ├── LLM: gpt-4o-mini (via openai-instrumentation)
│       └── Tool: docs_scrape
└── Task: quality_assurance (Step)
    └── Agent: QA Specialist
        └── LLM: gpt-4o-mini (via openai-instrumentation)

Each span includes rich attributes:

• gen_ai.system = “crewai”

• gen_ai.operation.name = “invoke_workflow” | “invoke_agent” | “execute_tool”

• Framework-specific attributes (agent role, task description, tool names, etc.)

Limitations

• Async Support: Currently supports synchronous workflows only. Async support (kickoff_async()) is planned for a future release.

• LLM Calls: Not instrumented here. Use provider-specific instrumentation (e.g., opentelemetry-instrumentation-openai).

Contributing

Contributions are welcome! Please ensure:

• All tests pass

• Code follows project style guidelines

• Instrumentation is defensive (catches exceptions)

• Documentation is updated

Links

• CrewAI Documentation

• OpenTelemetry Python

• Splunk GenAI Utilities

License

Apache-2.0