Agno 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

Agno is a Python framework for building production-ready AI agents with structured workflows, tool integration, and multi-agent orchestration. By connecting Agno to Orq.ai’s AI Gateway, you get access to 300+ models for your agents with a single configuration change.

Key Benefits

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

Complete Observability

Track every agent interaction, tool use, and model call 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 Agno with Orq.ai, ensure you have:

An Orq.ai account and API Key
Python 3.10 or higher

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

Installation

pip install agno

Configuration

Configure Agno to use Orq.ai’s AI Gateway via OpenAILike with a custom base_url:

Python

import os
from agno.models.openai.like import OpenAILike

llm = OpenAILike(
    id="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

import os
from agno.agent import Agent
from agno.models.openai.like import OpenAILike

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

agent = Agent(
    model=llm,
    instructions="You are a helpful assistant. Keep responses concise.",
    markdown=True,
)

agent.print_response("Explain machine learning in two sentences.")

Agent with Tools

Add tools to your agent for real-world tasks:

Python

import os
from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.tools.yfinance import YFinanceTools

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

agent = Agent(
    model=llm,
    tools=[YFinanceTools(enable_stock_price=True)],
    instructions="Use tables to display data. Don't include any other text.",
    markdown=True,
)

agent.print_response("What is the stock price of Apple?", stream=True)

Agent with Custom Tools

Define your own Python functions as tools:

Python

import os
from agno.agent import Agent
from agno.models.openai.like import OpenAILike

def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"Weather in {city}: Sunny, 22°C."

def convert_currency(amount: float, from_currency: str, to_currency: str) -> str:
    """Convert an amount between currencies."""
    return f"{amount} {from_currency} = {amount * 1.1:.2f} {to_currency} (approximate)"

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

agent = Agent(
    model=llm,
    tools=[get_weather, convert_currency],
    instructions="Use the available tools to answer questions accurately.",
    markdown=True,
)

agent.print_response("What's the weather in Tokyo and convert 100 USD to EUR?")

Multi-Agent Team

Orchestrate multiple specialized agents:

Python

import os
from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.team import Team

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

researcher = Agent(
    name="Researcher",
    model=llm,
    instructions="You research topics and provide accurate, concise facts.",
    markdown=True,
)

writer = Agent(
    name="Writer",
    model=llm,
    instructions="You take research and turn it into clear, engaging content.",
    markdown=True,
)

team = Team(
    members=[researcher, writer],
    model=llm,
    instructions="Collaborate to produce well-researched, well-written content.",
    markdown=True,
)

team.print_response("Write a short paragraph about renewable energy benefits.")

Model Selection

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

Python

import os
from agno.models.openai.like import OpenAILike

# Use Claude
claude_llm = OpenAILike(
    id="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 = OpenAILike(
    id="google/gemini-2.5-flash",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

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

Observability

Installation

pip install opentelemetry-api \
    opentelemetry-sdk \
    "opentelemetry-exporter-otlp-proto-http" \
    openinference-instrumentation-agno

Configuring Orq.ai Observability

Set up the tracer provider and instrument Agno before creating any agents:

import os
from opentelemetry import trace as trace_api
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

exporter = OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
)
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
trace_api.set_tracer_provider(tracer_provider)

from openinference.instrumentation.agno import AgnoInstrumentor
AgnoInstrumentor().instrument()

The OTEL setup and AgnoInstrumentor().instrument() call must happen before any agent instantiation.

Basic Example

import os
from opentelemetry import trace as trace_api
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

exporter = OTLPSpanExporter(
    endpoint="https://api.orq.ai/v2/otel/v1/traces",
    headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
)
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
trace_api.set_tracer_provider(tracer_provider)

from openinference.instrumentation.agno import AgnoInstrumentor
AgnoInstrumentor().instrument()

from agno.agent import Agent
from agno.models.openai.like import OpenAILike

agent = Agent(
    name="orq-demo",
    model=OpenAILike(
        id="openai/gpt-4o-mini",
        api_key=os.getenv("ORQ_API_KEY"),
        base_url="https://api.orq.ai/v3/router",
    ),
    markdown=True,
)
agent.print_response("Summarize the latest AI news.")

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

Agent with Tools

Agent with Custom Tools

Multi-Agent Team

Model Selection

Observability

Installation

Configuring Orq.ai Observability

Basic Example

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

​Agent with Tools

​Agent with Custom Tools

​Multi-Agent Team

​Model Selection

​Observability

​Installation

​Configuring Orq.ai Observability

​Basic Example

​Evaluations & Experiments

Run Evaluations with Evaluatorq

Run Experiments via the API

AI Gateway

Overview

Key Benefits

Prerequisites

Installation

Configuration

Basic Agent Example

Agent with Tools

Agent with Custom Tools

Multi-Agent Team

Model Selection

Observability

Installation

Configuring Orq.ai Observability

Basic Example

Evaluations & Experiments