Imagine having a team of AI assistants that work together automatically, each handling different parts of a complex task. That's exactly what Microsoft's AutoGen framework enables—and today, you'll learn how to connect it to a cost-effective API provider that can save you over 85% on your AI bills.

In this step-by-step tutorial, I'll walk you through everything from zero knowledge to having a working multi-agent system. No prior API experience required.

What You'll Build by the End

By following this guide, you'll create a simple multi-agent conversation where two AI assistants collaborate to answer user questions. One agent will be a "researcher" and another a "writer," working together seamlessly.

Why This Matters: Native API pricing from major providers often costs $7-15 per million tokens. With HolySheep AI, you get equivalent quality at just $0.42-$8 per million tokens—saving 85% or more while accessing the same underlying models with less than 50ms latency.

Prerequisites: What You Need Before Starting

[Screenshot hint: Show the Python version check in terminal by typing "python --version"]

Step 1: Get Your HolySheep AI API Key

First, you need an API key—a special password that lets your code talk to AI services. Follow these simple steps:

  1. Visit https://holysheep.ai/register
  2. Click the "Sign Up" button
  3. Enter your email and create a password
  4. Verify your email (check your inbox)
  5. Navigate to the Dashboard → API Keys section
  6. Click "Create New API Key" and copy it somewhere safe

[Screenshot hint: Show the API keys page with a generated key (partially masked for security)]

HolySheep Bonus: New users receive free credits upon registration—no payment required to start experimenting. They also support WeChat and Alipay for convenient payments, making it easy for users worldwide to access affordable AI.

Step 2: Install Required Software

Open your terminal (Command Prompt on Windows, Terminal on Mac/Linux) and run these commands:

# Upgrade pip first (this ensures you get the latest package installer)
pip install --upgrade pip


Install AutoGen and its dependencies

pip install autogen-agentchat pyautogen


Install OpenAI client library (required for the connection)

pip install openai

You should see download progress bars. If everything installs successfully, you'll see "Successfully installed" messages at the end.

[Screenshot hint: Show a successful pip installation output with green checkmarks]

Step 3: Set Up Your Environment File

For security, we store API keys in environment variables rather than directly in code. Create a new file called .env in your project folder:

# Create a .env file with your API credentials
HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY

Important: Replace YOUR_HOLYSHEEP_API_KEY with the actual key you copied in Step 1. Also, make sure to add .env to your .gitignore file if using version control.

Step 4: Configure AutoGen with Your Relay API

Now comes the main setup. Create a file called config.py and add this configuration:

import os
from autogen import ConversableAgent


Load environment variables from .env file

from dotenv import load_dotenv
load_dotenv()


Define the base URL for HolySheep AI relay service

base_url = "https://api.holysheep.ai/v1"


Get your API key from environment

api_key = os.getenv("HOLYSHEEP_API_KEY")


Create an LLM configuration for AutoGen

llm_config = {
    "config_list": [
        {
            "model": "gpt-4o",  # You can also use "claude-sonnet-4-5", "gemini-2.5-flash", "deepseek-v3.2"
            "api_type": "openai",  # AutoGen uses OpenAI-compatible interface
            "base_url": base_url,
            "api_key": api_key,
        }
    ],
    "temperature": 0.7,
}
Pricing Note: With HolySheep AI, you're accessing GPT-4.1 at $8/M tokens, Claude Sonnet 4.5 at $15/M tokens, Gemini 2.5 Flash at $2.50/M tokens, and DeepSeek V3.2 at just $0.42/M tokens—all through a unified, OpenAI-compatible interface.

Step 5: Create Your First Multi-Agent Conversation

Create a file called multi_agent.py with this complete example:

import os
from autogen import ConversableAgent
from dotenv import load_dotenv

load_dotenv()


Configure connection to HolySheep AI

base_url = "https://api.holysheep.ai/v1"
api_key = os.getenv("HOLYSHEEP_API_KEY")

llm_config = {
    "config_list": [{
        "model": "gpt-4o",
        "api_type": "openai",
        "base_url": base_url,
        "api_key": api_key,
    }],
    "temperature": 0.7,
}


Create the researcher agent

researcher = ConversableAgent(
    name="Researcher",
    system_message="""You are a helpful research assistant. 
    When asked a question, provide detailed, accurate information.
    Be thorough but concise in your responses.""",
    llm_config=llm_config,
    human_input_mode="NEVER",
)


Create the writer agent

writer = ConversableAgent(
    name="Writer",
    system_message="""You are a professional content writer.
    Take information from the researcher and transform it into 
    clear, engaging prose suitable for a general audience.""",
    llm_config=llm_config,
    human_input_mode="NEVER",
)


Start a conversation between the two agents

chat_result = writer.initiate_chat(
    recipient=researcher,
    message="Explain how photosynthesis works in simple terms.",
    max_turns=2,
)

print("\n=== CONVERSATION COMPLETE ===")
print(f"Total turns: {chat_result.chat_history}")

Run this with:

python multi_agent.py

[Screenshot hint: Show the terminal output with the conversation between Researcher and Writer agents]

You should see the Researcher agent respond first with facts about photosynthesis, then the Writer agent transforming that into engaging prose—all powered through HolySheep AI's relay infrastructure.

Understanding the Code: Key Concepts Explained

Advanced Example: User-Interactive Multi-Agent System

Let's create a more practical system where the user can ask questions and watch agents collaborate:

import os
from autogen import ConversableAgent, GroupChat, GroupChatManager
from dotenv import load_dotenv

load_dotenv()

base_url = "https://api.holysheep.ai/v1"
api_key = os.getenv("HOLYSHEEP_API_KEY")

llm_config = {
    "config_list": [{
        "model": "gpt-4o",
        "api_type": "openai",
        "base_url": base_url,
        "api_key": api_key,
    }],
    "temperature": 0.7,
}


Define three specialized agents

data_analyst = ConversableAgent(
    name="Data_Analyst",
    system_message="You analyze data and provide insights. Always include numbers and statistics when relevant.",
    llm_config=llm_config,
    human_input_mode="NEVER",
)

tech_expert = ConversableAgent(
    name="Tech_Expert",
    system_message="You explain technical concepts in accessible ways. Use analogies when helpful.",
    llm_config=llm_config,
    human_input_mode="NEVER",
)

creative_writer = ConversableAgent(
    name="Creative_Writer",
    system_message="You write engaging content that captures attention. Use storytelling techniques.",
    llm_config=llm_config,
    human_input_mode="NEVER",
)


Create a group chat with all three agents

group_chat = GroupChat(
    agents=[data_analyst, tech_expert, creative_writer],
    messages=[],
    max_round=5,
)


Create a manager to orchestrate the conversation

manager = GroupChatManager(
    groupchat=group_chat,
    llm_config=llm_config,
)


Initiate the group conversation with a complex query

result = data_analyst.initiate_chat(
    manager,
    message="""User wants to understand: How do neural networks learn?
    Please have all three agents contribute their perspective.""",
)

Common Errors and Fixes

Error 1: "Authentication Error" or "Invalid API Key"

Problem: Your code runs but you see authentication failures in the output.

Solutions:

Error 2: "Connection Refused" or "HTTPSConnection Error"

Problem: Your code fails immediately with a connection error.

Solutions:

Error 3: "Model Not Found" or "Unsupported Model"

Problem: The API responds but complains about the model name.

Solutions:

Error 4: "Rate Limit Exceeded"

Problem: You're making too many requests too quickly.

Solutions:

Error 5: "Import Error: No module named 'autogen'"

Problem: Python can't find the AutoGen library.

Solutions:

Best Practices for Production Use

Next Steps: Expanding Your Multi-Agent System

Now that you have a working foundation, consider exploring:

Conclusion

You've successfully connected Microsoft's AutoGen multi-agent framework to a cost-effective relay API! With this setup, you can build sophisticated AI agent systems while keeping your operational costs remarkably low—often less than $1 per million tokens compared to $7+ from direct providers.

The OpenAI-compatible interface makes switching between different AI providers seamless, while HolySheep AI's infrastructure delivers reliable sub-50ms latency and supports multiple payment methods including WeChat and Alipay for global accessibility.

Start experimenting today—the free credits you received upon registration are perfect for testing different agent configurations and finding what works best for your use case.

👉 Sign up for HolySheep AI — free credits on registration

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