CrewAI framework integration

AI Gateway

Route your LLM calls through the AI Gateway with a single base URL change. Zero vendor lock-in: always run on the best model at the lowest cost for your use case.

Observability

Instrument your code with OpenTelemetry to capture traces, logs, and metrics for every LLM call, agent step, and tool use.

AI Gateway

Overview

CrewAI is a framework for orchestrating multi-agent teams with role-based agents, hierarchical task management, and collaborative AI workflows. By connecting CrewAI to Orq.ai’s AI Gateway, you get access to 300+ models for your agent crews with a single configuration change.

Key Benefits

Orq.ai’s AI Gateway enhances your CrewAI applications with:

Complete Observability

Track every agent task, tool use, and crew interaction with detailed traces

Built-in Reliability

Automatic fallbacks, retries, and load balancing for production resilience

Cost Optimization

Real-time cost tracking and spend management across all your AI operations

Multi-Provider Access

Access 300+ LLMs and 20+ providers through a single, unified integration

Prerequisites

Before integrating CrewAI with Orq.ai, ensure you have:

An Orq.ai account and API Key
Python 3.10 to 3.12

To setup your API key, see API keys & Endpoints.

Python 3.13 and 3.14 are not yet supported. CrewAI’s chromadb dependency uses Pydantic v1, which breaks at import time on Python 3.13+. Use Python 3.10 to 3.12.

Installation

pip install crewai

Configuration

Configure CrewAI to use Orq.ai’s AI Gateway via the LLM class with a custom base_url:

Python

from crewai import LLM
import os

llm = LLM(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

base_url: https://api.orq.ai/v3/router

Basic Agent Example

Python

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

llm = LLM(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

researcher = Agent(
    role="Research Analyst",
    goal="Provide accurate and concise information on topics",
    backstory="Expert analyst with broad knowledge across many domains.",
    llm=llm,
)

task = Task(
    description="In two sentences, explain what machine learning is.",
    agent=researcher,
    expected_output="A concise two-sentence explanation of machine learning.",
)

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

Multi-Agent Crew

Orchestrate multiple agents with specialized roles:

Python

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

llm = LLM(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

researcher = Agent(
    role="Research Analyst",
    goal="Research topics and gather key facts",
    backstory="Expert at finding and summarizing information.",
    llm=llm,
)

writer = Agent(
    role="Content Writer",
    goal="Write clear, engaging content",
    backstory="Skilled at turning research into readable content.",
    llm=llm,
)

research_task = Task(
    description="Research the key benefits of renewable energy in 3 bullet points.",
    agent=researcher,
    expected_output="3 bullet points about renewable energy benefits.",
)

write_task = Task(
    description="Write a one-paragraph summary based on the research.",
    agent=writer,
    expected_output="A single paragraph summarizing renewable energy benefits.",
    context=[research_task],
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task], tracing=False)
result = crew.kickoff()
print(result)

Model Selection

With Orq.ai, you can use any supported model from 20+ providers:

Always prefix model IDs with openai/ when using CrewAI with the AI Gateway. Without it, CrewAI may route the request through a matching native provider client (notably its built-in Google client) that ignores base_url, producing misleading errors like “API key not valid”. The openai/ prefix forces the OpenAI-compatible code path, which respects base_url for every provider.

Python

from crewai import LLM
import os

# Use Claude
claude_llm = LLM(
    model="openai/anthropic/claude-sonnet-4-5-20250929",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

# Use Gemini
gemini_llm = LLM(
    model="openai/google/gemini-2.5-flash",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

# Use Groq
groq_llm = LLM(
    model="openai/groq/llama-3.3-70b-versatile",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

Observability

Getting Started

CrewAI enables powerful multi-agent coordination for complex AI workflows. Tracing CrewAI with Orq.ai provides comprehensive insights into agent interactions, task execution, tool usage, and crew performance to optimize your multi-agent systems.

Prerequisites

Before you begin, ensure you have:

An Orq.ai account and API Key
CrewAI installed in your project
Python 3.10 to 3.12
OpenAI API key (or other LLM provider credentials)

Python 3.13 and 3.14 are not yet supported. CrewAI’s chromadb dependency uses Pydantic v1, which breaks at import time on Python 3.13+. Use Python 3.10 to 3.12.

Install Dependencies

# OpenTelemetry, crewai, openinference
pip install crewai openinference-instrumentation-crewai openinference-instrumentation-openai
pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

# LLM providers
pip install openai anthropic

# Optional: Advanced tools and integrations
pip install crewai-tools

Configure Orq.ai

Set up your environment variables to connect to Orq.ai’s OpenTelemetry collector: Unix/Linux/macOS:

export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.orq.ai/v2/otel"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <ORQ_API_KEY>"
export OTEL_RESOURCE_ATTRIBUTES="service.name=crewai-app,service.version=1.0.0"
export OPENAI_API_KEY="<YOUR_OPENAI_API_KEY>"

Windows (PowerShell):

$env:OTEL_EXPORTER_OTLP_ENDPOINT = "https://api.orq.ai/v2/otel"
$env:OTEL_EXPORTER_OTLP_HEADERS = "Authorization=Bearer <ORQ_API_KEY>"
$env:OTEL_RESOURCE_ATTRIBUTES = "service.name=crewai-app,service.version=1.0.0"
$env:OPENAI_API_KEY = "<YOUR_OPENAI_API_KEY>"

Using .env file:

OTEL_EXPORTER_OTLP_ENDPOINT=https://api.orq.ai/v2/otel
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Bearer <ORQ_API_KEY>
OTEL_RESOURCE_ATTRIBUTES=service.name=crewai-app,service.version=1.0.0
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>

Integrations Example

We’ll be using OpenInference as TracerProvider with CrewAI

from openinference.instrumentation.crewai import CrewAIInstrumentor
from openinference.instrumentation.openai import OpenAIInstrumentor
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from crewai import Agent, Task, Crew

# Initialize OpenTelemetry
tracer_provider = trace_sdk.TracerProvider()
tracer_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": "Bearer <ORQ_API_KEY>"}
)))
trace.set_tracer_provider(tracer_provider)

# Instrument CrewAI
CrewAIInstrumentor().instrument(tracer_provider=tracer_provider)
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

# Your CrewAI code is automatically traced
researcher = Agent(
    role='Market Research Analyst',
    goal='Gather comprehensive market data and trends',
    backstory='Expert in analyzing market dynamics and consumer behavior'
)

task = Task(
    description='Research the latest trends in AI and machine learning',
    agent=researcher,
    expected_output='Comprehensive report on AI and ML trens with key insights and recommendations'
)

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

View Traces

Traces from your CrewAI execution will be visible within the Traces menu in your orq.ai studio.

Evaluations & Experiments

Once your agents are running, use Evaluatorq to score outputs across a dataset and Experiments to compare configurations side-by-side.

Run Evaluations with Evaluatorq

Run parallel evaluations across your agents and compare results.

Run Experiments via the API

Compare agent configurations and view results in the AI Studio.

AI Providers

Coding Assistants

Frameworks

Automations

AI Gateway

Observability

AI Gateway

Overview

Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

Prerequisites

Installation

Configuration

Basic Agent Example

Multi-Agent Crew

Model Selection

Observability

Getting Started

Prerequisites

Install Dependencies

Configure Orq.ai

Integrations Example

View Traces

Evaluations & Experiments

Run Evaluations with Evaluatorq

Run Experiments via the API

AI Gateway

Observability

​AI Gateway

​Overview

​Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

​Prerequisites

​Installation

​Configuration

​Basic Agent Example

​Multi-Agent Crew

​Model Selection

​Observability

​Getting Started

​Prerequisites

​Install Dependencies

​Configure Orq.ai

​Integrations Example

​View Traces

​Evaluations & Experiments

Run Evaluations with Evaluatorq

Run Experiments via the API

AI Gateway

Overview

Key Benefits

Prerequisites

Installation

Configuration

Basic Agent Example

Multi-Agent Crew

Model Selection

Observability

Getting Started

Prerequisites

Install Dependencies

Configure Orq.ai

Integrations Example

View Traces

Evaluations & Experiments