AutoGen multi-agent 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

Microsoft AutoGen is a framework for building multi-agent conversational AI systems with collaborative problem-solving through automated agent interactions. By connecting AutoGen to Orq.ai’s AI Gateway, you get access to 300+ models for your multi-agent workflows with a single configuration change.

Key Benefits

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

Complete Observability

Track every agent conversation, tool use, and multi-agent interaction

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 AutoGen 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 "autogen-agentchat" "autogen-ext[openai]"

Configuration

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

Python

import os
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(
    model="gpt-4o",
    base_url="https://api.orq.ai/v3/router",
    api_key=os.getenv("ORQ_API_KEY"),
    model_info={
        "vision": False,
        "function_calling": True,
        "json_output": True,
        "family": "unknown",
        "structured_output": True,
    },
)

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

The model_info dict is required when using a custom base_url so AutoGen knows the model’s capabilities.

Basic Agent Example

Python

import asyncio
import os
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main():
    model_client = OpenAIChatCompletionClient(
        model="gpt-4o",
        base_url="https://api.orq.ai/v3/router",
        api_key=os.getenv("ORQ_API_KEY"),
        model_info={
            "vision": False,
            "function_calling": True,
            "json_output": True,
            "family": "unknown",
            "structured_output": True,
        },
    )

    agent = AssistantAgent(
        name="assistant",
        model_client=model_client,
        system_message="You are a helpful assistant.",
    )

    result = await agent.run(task="What is quantum computing?")
    print(result.messages[-1].content)
    await model_client.close()

asyncio.run(main())

Multi-Agent Team

Orchestrate multiple specialized agents with RoundRobinGroupChat:

Python

import asyncio
import os
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main():
    model_client = OpenAIChatCompletionClient(
        model="gpt-4o",
        base_url="https://api.orq.ai/v3/router",
        api_key=os.getenv("ORQ_API_KEY"),
        model_info={
            "vision": False,
            "function_calling": True,
            "json_output": True,
            "family": "unknown",
            "structured_output": True,
        },
    )

    researcher = AssistantAgent(
        name="researcher",
        model_client=model_client,
        system_message="You research topics and provide key facts.",
    )

    writer = AssistantAgent(
        name="writer",
        model_client=model_client,
        system_message="You write clear summaries based on research.",
    )

    team = RoundRobinGroupChat(
        participants=[researcher, writer],
        termination_condition=MaxMessageTermination(max_messages=2),
    )

    result = await team.run(task="Research and summarize what LLMs are.")
    print(result.messages[-1].content)
    await model_client.close()

asyncio.run(main())

Model Selection

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

Python

import os
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_info = {
    "vision": False,
    "function_calling": True,
    "json_output": True,
    "family": "unknown",
    "structured_output": True,
}

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

# Use Gemini
gemini_client = OpenAIChatCompletionClient(
    model="gemini-2.5-flash",
    base_url="https://api.orq.ai/v3/router",
    api_key=os.getenv("ORQ_API_KEY"),
    model_info=model_info,
)

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

Observability

Getting Started

Microsoft AutoGen enables sophisticated multi-agent conversations and collaborative AI systems. Tracing AutoGen with Orq.ai provides deep insights into agent interactions, conversation flows, tool usage, and multi-agent coordination patterns to optimize your conversational AI applications.

Prerequisites

Before you begin, ensure you have:

An Orq.ai account and API Key
Python 3.8+
Microsoft AutoGen installed in your project
OpenAI API key (or other LLM provider credentials)

Install Dependencies

# Core AutoGen and OpenTelemetry packages
pip install pyautogen opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

# LLM providers
pip install openai

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=autogen-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=autogen-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=autogen-app,service.version=1.0.0
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>

Integration

AutoGen has built-in OpenTelemetry support. Configure the tracer provider and pass it to the AutoGen runtime.

Set up OpenTelemetry tracing in your application:

import os
import autogen
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# Configure tracer provider
tracer_provider = TracerProvider(
    resource=Resource({"service.name": "autogen-app"})
)

# Set up OTLP exporter
otlp_exporter = OTLPSpanExporter()

tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter))
trace.set_tracer_provider(tracer_provider)

# Instrument OpenAI calls for automatic tracing
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

config_list = [{"model": "gpt-4o", "api_key": os.getenv("OPENAI_API_KEY")}]

# Create agents
assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config={"config_list": config_list, "temperature": 0}
)

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=3,
    code_execution_config={"work_dir": "coding", "use_docker": False}
)

print("Starting AutoGen conversation (this will be traced)...")

# Start conversation (automatically traced)
user_proxy.initiate_chat(
    assistant,
    message="Write a Python function to calculate fibonacci numbers up to n=10"
)

All AutoGen agent conversations and interactions will be instrumented and exported to Orq.ai through the OTLP exporter. For more details, see Traces.

Advanced Examples

Multi-Agent Group Chat

import autogen
import os
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# Configure tracer provider
tracer_provider = TracerProvider(
  resource=Resource({"service.name": "autogen-app"})
)

# Set up OTLP exporter
otlp_exporter = OTLPSpanExporter()

tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter))
trace.set_tracer_provider(tracer_provider)

# Instrument OpenAI calls for automatic tracing
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

# Setup done as shown in Integration section above

config_list = [{"model": "gpt-4", "api_key": os.getenv("OPENAI_API_KEY")}]

# Create specialized agents
coder = autogen.AssistantAgent(
    name="coder",
    system_message="You are an expert Python developer.",
    llm_config={"config_list": config_list}
)

reviewer = autogen.AssistantAgent(
    name="code_reviewer",
    system_message="You review code for quality and best practices.",
    llm_config={"config_list": config_list}
)

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=5,
    code_execution_config={"work_dir": "workspace"}
)

# Create group chat
groupchat = autogen.GroupChat(
    agents=[user_proxy, coder, reviewer],
    messages=[],
    max_round=10
)

manager = autogen.GroupChatManager(
    groupchat=groupchat,
    llm_config={"config_list": config_list}
)

# Start group conversation (automatically traced)
user_proxy.initiate_chat(
    manager,
    message="Create a REST API for user management with FastAPI"
)

Agent with Custom Tools

import autogen
import os
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# Configure tracer provider
tracer_provider = TracerProvider(
  resource=Resource({"service.name": "autogen-app"})
)

# Set up OTLP exporter
otlp_exporter = OTLPSpanExporter()

tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter))
trace.set_tracer_provider(tracer_provider)

# Instrument OpenAI calls for automatic tracing
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

config_list = [{"model": "gpt-4", "api_key": os.getenv("OPENAI_API_KEY")}]

# Define custom tools/functions
def get_weather(location: str) -> str:
    """Get current weather for a location."""
    return f"Weather in {location}: Sunny, 75°F"

def calculate_distance(city1: str, city2: str) -> str:
    """Calculate distance between two cities."""
    return f"Distance between {city1} and {city2}: 500 km"

# Create agent with function calling
travel_planner = autogen.AssistantAgent(
    name="travel_planner",
    system_message="You help plan travel itineraries.",
    llm_config={
        "config_list": config_list,
        "functions": [
            {
                "name": "get_weather",
                "description": "Get current weather for a location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {"type": "string"}
                    },
                    "required": ["location"]
                }
            },
            {
                "name": "calculate_distance",
                "description": "Calculate distance between cities",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city1": {"type": "string"},
                        "city2": {"type": "string"}
                    },
                    "required": ["city1", "city2"]
                }
            }
        ]
    }
)

user_proxy = autogen.UserProxyAgent(
    name="user",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=5,
    function_map={
        "get_weather": get_weather,
        "calculate_distance": calculate_distance
    },
    code_execution_config=False
)

# Use agent with tools (automatically traced)
user_proxy.initiate_chat(
    travel_planner,
    message="Plan a 3-day trip from New York to London"
)

Autogen is also usable through our AI Gateway, to learn more, see AutoGen Gateway.

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 Team

Model Selection

Observability

Getting Started

Prerequisites

Install Dependencies

Configure Orq.ai

Integration

Advanced Examples

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 Team

​Model Selection

​Observability

​Getting Started

​Prerequisites

​Install Dependencies

​Configure Orq.ai

​Integration

​Advanced Examples

​Evaluations & Experiments

Run Evaluations with Evaluatorq

Run Experiments via the API

AI Gateway

Overview

Key Benefits

Prerequisites

Installation

Configuration

Basic Agent Example

Multi-Agent Team

Model Selection

Observability

Getting Started

Prerequisites

Install Dependencies

Configure Orq.ai

Integration

Advanced Examples

Evaluations & Experiments