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Documentation Index

Fetch the complete documentation index at: https://docs.pandaprobe.com/llms.txt

Use this file to discover all available pages before exploring further.

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Installation

pip install "pandaprobe[crewai]"

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Setup

from pandaprobe.integrations.crewai import CrewAIAdapter

adapter = CrewAIAdapter(
    session_id="conversation-123",
    user_id="user-abc",
    tags=["production"],
)
adapter.instrument()
We recommend using UUIDs for session_id and user_id so traces can be grouped reliably across runs.

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Usage

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Research Analyst",
    goal="Find the latest AI trends",
    backstory="You are an expert AI researcher.",
)

task = Task(
    description="Research the latest developments in AI agents.",
    expected_output="A summary of key trends.",
    agent=researcher,
)

crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()

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What gets traced

CrewAI ComponentSpan KindDescription
Crew.kickoff / kickoff_for_eachCHAINRoot trace boundary, crew configuration as system message
Agent.execute_taskAGENTAgent backstory as system message, prior agent context
LLM completion callsLLMToken delta tracking (per-call usage), reasoning extraction
execute_tool_and_check_finalityTOOLTool name from AgentAction, input/output

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Multi-agent support

In multi-agent crews, PandaProbe tracks context propagation between agents. Each agent’s output is made available as context for subsequent agents, and this is reflected in the trace hierarchy.

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Token usage

CrewAI token usage is computed using delta tracking: the adapter snapshots the agent’s cumulative _token_usage before and after each LLM call to determine per-call token counts.
CrewAI FieldPandaProbe Field
prompt_tokensprompt_tokens
completion_tokenscompletion_tokens
total_tokenstotal_tokens
cached_prompt_tokenscache_read_tokens
reasoning_tokensreasoning_tokens

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Example with multiple agents

This example sets up a three-agent crew (researcher → analyst → writer). It traces the agents via CrewAIAdapter and wraps the run in a pandaprobe.session for trace grouping: 
import uuid

from crewai import Agent, Crew, LLM, Task

import pandaprobe
from pandaprobe.integrations.crewai import CrewAIAdapter

SESSION_ID = str(uuid.uuid4())
USER_ID = "user_1"


def main():
    adapter = CrewAIAdapter(
        user_id=USER_ID,
        tags=["multi-agent", "example"],
    )
    adapter.instrument()

    llm = LLM(model="gemini/gemini-3.1-flash-lite-preview", reasoning_effort="low")

    researcher = Agent(
        role="Senior Researcher",
        goal="Research and identify the most important trends in the given topic",
        backstory="You are an expert researcher who excels at finding key insights.",
        llm=llm,
    )

    analyst = Agent(
        role="Data Analyst",
        goal="Analyze research findings and identify actionable insights",
        backstory="You are a skilled analyst who turns raw research into clear conclusions.",
        llm=llm,
    )

    writer = Agent(
        role="Content Writer",
        goal="Write a compelling article based on research and analysis",
        backstory="You are a talented writer who creates engaging content from technical material.",
        llm=llm,
    )

    research_task = Task(
        description="Research the latest trends in renewable energy for 2026.",
        expected_output="A list of 5 key trends with brief descriptions.",
        agent=researcher,
    )

    analysis_task = Task(
        description="Analyze the research findings and identify the top 3 most impactful trends.",
        expected_output="A ranked list of the top 3 trends with impact analysis.",
        agent=analyst,
    )

    writing_task = Task(
        description="Write a short article summarizing the top renewable energy trends for 2026.",
        expected_output="A 200-word article suitable for a tech blog.",
        agent=writer,
    )

    crew = Crew(
        agents=[researcher, analyst, writer],
        tasks=[research_task, analysis_task, writing_task],
        verbose=True,
    )

    print(f"Session: {SESSION_ID}\n")

    with pandaprobe.session(SESSION_ID):
        result = crew.kickoff()
        print(f"\nResult:\n{result.raw}")

    pandaprobe.flush()
    pandaprobe.shutdown()
    print(f"\nTrace sent to PandaProbe backend (session={SESSION_ID}).")


if __name__ == "__main__":
    main()
Each agent run is captured as an AGENT span nested under the root CHAIN, with LLM calls and any tool use recorded beneath the corresponding agent.