> For the complete documentation index, see [llms.txt](https://docs.fastrouter.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fastrouter.ai/integrations/agents/autogen.md).
# AutoGen
### What is AutoGen?
[AutoGen](https://microsoft.github.io/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](https://fastrouter.ai))
* Python 3.10 or higher
***
#### Quick Start
**Step 1: Create a Project and Virtual Environment**
You'll only need to do this once:
```bash
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:
```bash
source .venv/bin/activate
```
On Windows:
```bash
.venv\Scripts\activate
```
**Step 2: Install AutoGen**
```bash
pip install -U "autogen-agentchat" "autogen-ext[openai]"
```
**Step 3: Get Your FastRouter API Key**
1. Sign up or log in at [fastrouter.ai](https://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:
```bash
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`:
```python
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**
```bash
python autogen_example.py
```
The agent responds, and the request appears in your [FastRouter Dashboard](https://dashboard.fastrouter.ai/) with token usage and cost.
***
#### 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:
```python
model="anthropic/claude-4.5-sonnet"
```
[Explore the full model catalog](https://fastrouter.ai/models)
**Automatic Model Selection**
Let FastRouter pick the best model for each request based on query complexity, domain, and cost:
```python
model="fastrouter/auto"
```
[Explore automatic model selection](https://docs.fastrouter.ai/automatic-model-selection)
***
#### 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.
***
#### Next Steps
* [Explore the full model catalog](https://fastrouter.ai/models)
* [Set up Fallback Models](https://docs.fastrouter.ai/fallback-models) for high availability
* [Configure Alerts](https://docs.fastrouter.ai/alerts) for spend and performance monitoring
* [Run a Free Audit](https://fastrouter.ai/audit) on your existing LLM traffic to identify savings
* [Join the Discord community](https://discord.gg/QfTgEtMyyU)
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.fastrouter.ai/integrations/agents/autogen.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.