Working with CrewAI

Add end-to-end tracing to Agno agent workflows with LangDB—monitor model calls, tool usage, and step flows using a single init() call.

LangDB makes it effortless to trace CrewAI workflows end-to-end. With a single init() call, all agent interactions, task executions, and LLM calls are captured.

Checkout: https://app.langdb.ai/sharing/threads/3becbfed-a1be-ae84-ea3c-4942867a3e22

Installation

Install the LangDB client with LangChain feature flag:

pip install pylangdb[crewai]

Quick Start

Export Environment Variables

Set your LangDB credentials:

export LANGDB_API_KEY="<your_langdb_api_key>"
export LANGDB_PROJECT_ID="<your_langdb_project_id>"

Initialize Tracing

Import and run the initialize before configuring your CrewAI Code:

from pylangdb.crewai import init
# Initialise LangDB
init()

Configure your CrewAI code

import os
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, LLM

# Configure LLM with LangDB headers
llm = LLM(
    model="openai/gpt-4o",  # Use LiteLLM Like Model Names
    api_key=os.getenv("LANGDB_API_KEY"),
    base_url=os.getenv("LANGDB_API_BASE_URL"),
    extra_headers={"x-project-id": os.getenv("LANGDB_PROJECT_ID")}
)

# Define agents and tasks as usual
researcher = Agent(
    role="researcher",
    goal="Research topic thoroughly",
    backstory="You are an expert researcher",
    llm=llm,
    verbose=True
)
task = Task(description="Research the given topic", agent=researcher)
crew = Crew(agents=[researcher], tasks=[task])

# Kick off the workflow
result = crew.kickoff()
print(result)

All CrewAI calls—agent initialization, task execution, and model responses—are automatically linked.