CrewAI Multi-Agent Crew

Instrument a CrewAI crew with per-agent prompt templates and per-task user prompts.

This guide shows how to instrument a CrewAI crew where agents and tasks are defined in separate files.

CrewAI manages LLM calls internally, so you cannot wrap them with with neatlogs.trace(kind="LLM"):. Neatlogs provides two helpers instead:

neatlogs.bind_templates(llm, system_tpl): attach a system prompt template to an LLM before passing it to an agent
neatlogs.register_crewai_task(task, user_tpl): associate a user prompt template with a task after creating it

Setup

import os
import neatlogs

neatlogs.init(
    api_key=os.environ["NEATLOGS_API_KEY"],
    endpoint=os.environ.get("NEATLOGS_ENDPOINT"),
    workflow_name="support-crew",
    instrumentations=["crewai"],
)

# Import CrewAI AFTER init()
from crewai import Agent, Task, Crew, LLM

agents.py: Bind System Prompts

Create a PromptTemplate for each agent's system prompt, then pass it to neatlogs.bind_templates(llm, system_tpl). Use the bound LLM when constructing the agent.

# agents.py
import neatlogs
from crewai import Agent, LLM


def research_agent() -> Agent:
    system_tpl = neatlogs.PromptTemplate(
        "You are a research specialist. Find accurate, up-to-date information on the given topic."
    )
    return Agent(
        role="Researcher",
        goal="Research and summarize information accurately",
        backstory=str(system_tpl.template),
        llm=neatlogs.bind_templates(
            LLM(model="openai/gpt-4o"),
            system_tpl,
        ),
        allow_delegation=False,
    )


def writer_agent() -> Agent:
    system_tpl = neatlogs.PromptTemplate(
        "You are a professional writer. Produce clear, concise reports from research findings."
    )
    return Agent(
        role="Writer",
        goal="Write clear reports from research findings",
        backstory=str(system_tpl.template),
        llm=neatlogs.bind_templates(
            LLM(model="openai/gpt-4o"),
            system_tpl,
        ),
        allow_delegation=False,
    )

tasks.py: Register User Prompts

After creating each Task, call neatlogs.register_crewai_task(task, user_tpl) to associate a UserPromptTemplate with it.

# tasks.py
import neatlogs
from crewai import Task


def research_task(agent, topic: str) -> Task:
    description = f"Research the following topic thoroughly: {topic}"
    expected_output = "A detailed summary of findings with key facts and sources."
    task = Task(description=description, expected_output=expected_output, agent=agent)
    neatlogs.register_crewai_task(
        task,
        neatlogs.UserPromptTemplate(description + "\n\n" + expected_output),
    )
    return task


def write_report_task(agent, context: list) -> Task:
    description = "Using the research findings, write a concise 3-paragraph report."
    expected_output = "A polished report ready for publication."
    task = Task(description=description, expected_output=expected_output, agent=agent, context=context)
    neatlogs.register_crewai_task(
        task,
        neatlogs.UserPromptTemplate(description + "\n\n" + expected_output),
    )
    return task

Decorate the function that starts the workflow with @neatlogs.span(kind="WORKFLOW"). This can be the function that directly calls crew.kickoff(), or any function further up the call stack that represents the entry point:

# main.py
import os
import neatlogs

neatlogs.init(
    api_key=os.environ["NEATLOGS_API_KEY"],
    endpoint=os.environ.get("NEATLOGS_ENDPOINT"),
    workflow_name="support-crew",
    instrumentations=["crewai"],
)

from crewai import Crew
from agents import research_agent, writer_agent
from tasks import research_task, write_report_task


@neatlogs.span(kind="WORKFLOW", name="research_crew")
def run(topic: str) -> str:
    researcher = research_agent()
    writer = writer_agent()

    task1 = research_task(researcher, topic=topic)
    task2 = write_report_task(writer, context=[task1])

    crew = Crew(agents=[researcher, writer], tasks=[task1, task2])
    result = crew.kickoff()
    return result.raw if hasattr(result, "raw") else str(result)


print(run("Advances in vector database technology in 2024"))

neatlogs.flush()
neatlogs.shutdown()

What You'll See in the Dashboard

A WORKFLOW span for research_crew containing:

Agent spans for researcher and writer (captured automatically by the crewai instrumentation)
LLM spans with system prompts from bind_templates and user prompts from register_crewai_task

Summary

What	How
System prompt on an agent	`neatlogs.bind_templates(llm, system_tpl)`: pass bound LLM to `Agent(llm=...)`
User prompt on a task	`neatlogs.register_crewai_task(task, user_tpl)`: call after `Task(...)`
Workflow span	`@neatlogs.span(kind="WORKFLOW")` on the function that starts the workflow