AutoGen

Track usage, control costs, and add guardrails to your AutoGen multi-agent apps

What is AutoGen?

AutoGen is a framework from Microsoft for building multi-agent AI applications, where multiple conversational agents collaborate to solve tasks. It supports any OpenAI-compatible model through its client configuration.

By routing AutoGen through FastRouter, you get:

• 100+ models from OpenAI, Anthropic, Google, xAI, Meta, Groq, Mistral, and more through one endpoint—assign different models to different agents with one key

• Observability for every request: cost, tokens, latency, and model selection tracked in real time

• Reliability through automatic failover across providers, response caching, and intelligent routing

• Governance with per-key budgets, rate limits, model restrictions, role-based access, and project isolation

This guide covers connecting AutoGen (Python) to FastRouter using the OpenAI chat completion client.

Prerequisites

• A FastRouter.ai account (sign up)

• Python 3.10 or higher

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Quick Start

Step 1: Create a Project and Virtual Environment

You'll only need to do this once:

mkdir my_project
cd my_project
python -m venv .venv

Activate the virtual environment. Do this every time you start a new terminal session.

On macOS or Linux:

source .venv/bin/activate

On Windows:

.venv\Scripts\activate

Step 2: Install AutoGen

pip install -U "autogen-agentchat" "autogen-ext[openai]"

Step 3: Get Your FastRouter API Key

1. Sign up or log in at fastrouter.ai

2. Navigate to your project's Keys page

3. Click Create User Key

4. Copy the key immediately. FastRouter does not display the key again after creation.

Export it in your terminal:

export FASTROUTER_API_KEY=sk-add-your-key-here

Step 4: Configure the Model Client for FastRouter

AutoGen's OpenAIChatCompletionClient accepts a custom base URL. Save this as autogen_example.py:

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

model_client = OpenAIChatCompletionClient(
    model="openai/gpt-5.2",
    base_url="https://api.fastrouter.ai/api/v1",
    api_key=os.environ["FASTROUTER_API_KEY"],
    model_info={
        "vision": False,
        "function_calling": True,
        "json_output": True,
        "family": "unknown",
        "structured_output": True,
    },
)
import os
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(
    model="openai/gpt-5.2",
    base_url="https://api.fastrouter.ai/api/v1",
    api_key=os.environ["FASTROUTER_API_KEY"],
    model_info={
        "vision": False,
        "function_calling": True,
        "json_output": True,
        "family": "unknown",
        "structured_output": True,
    },
)

agent = AssistantAgent("assistant", model_client=model_client)


async def main():
    result = await agent.run(task="Explain what an LLM gateway does in one sentence.")
    print(result.messages[-1].content)


if __name__ == "__main__":
    asyncio.run(main())
agent = AssistantAgent("assistant", model_client=model_client)


async def main():
    result = await agent.run(task="Explain what an LLM gateway does in one sentence.")
    print(result.messages[-1].content)


if __name__ == "__main__":
    asyncio.run(main())

Note: For non-OpenAI model slugs, AutoGen requires the model_info block shown above so it knows the model's capabilities.

Step 5: Run the Agent

python autogen_example.py

The agent responds, and the request appears in your FastRouter Dashboard with token usage and cost.

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Use AutoGen with 100+ Models

FastRouter uses the provider/model-name format. Switch providers by changing the model slug. Give each agent in a multi-agent team its own client—a fast model for worker agents and a frontier model for the orchestrator:

model="anthropic/claude-4.5-sonnet"

Explore the full model catalog

Automatic Model Selection

Let FastRouter pick the best model for each request based on query complexity, domain, and cost:

model="fastrouter/auto"

Explore automatic model selection

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FAQs

Configuration & Setup

Can I use multiple models with the same API key?

Yes. The API key controls access and budget. Each agent can have its own model client, all sharing one key.

Why do I need the model_info block?

AutoGen looks up model capabilities by name. Because FastRouter slugs aren't in AutoGen's built-in table, you declare capabilities explicitly with model_info.

Can I restrict a key to only use specific models?

Yes. When creating or editing a key, use the Select Models setting to limit which models the key can access. FastRouter rejects requests to unauthorized models.

Costs & Budgeting

Multi-agent conversations generate many calls. How do I control cost?

Set budgets and rate limits on the key, and use Dynamic Tags to attribute spend per team or run. The Dashboard breaks down costs by project, key, model, and tag.

Performance & Reliability

Does FastRouter add latency?

FastRouter adds near-zero gateway overhead, negligible compared to model inference time.

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Next Steps

• Explore the full model catalog

• Set up Fallback Models for high availability

• Configure Alerts for spend and performance monitoring

• Run a Free Audit on your existing LLM traffic to identify savings

• Join the Discord community