Code beginner · 3 min read

How to use AutoGen in python

Q: How to use AutoGen in python

Use the autogen Python library to create AI agents by defining roles and tasks, then orchestrate their interaction with built-in agent classes and LLMs.

Direct answer

Use the autogen Python library to create AI agents by defining roles and tasks, then orchestrate their interaction with built-in agent classes and LLMs.

Setup

Install

bash

pip install autogen

Env vars

OPENAI_API_KEY

Imports

python

from autogen import AssistantAgent, UserAgent, MultiAgent, OpenAI
import os

Examples

inCreate a simple AutoGen agent that answers a user question.

outUserAgent asks a question, AssistantAgent responds with an answer using OpenAI LLM.

inSet up two agents to collaborate on a task using AutoGen MultiAgent.

outMultiAgent coordinates UserAgent and AssistantAgent to exchange messages and complete the task.

inUse AutoGen with OpenAI API key to generate a summary from user input.

outAssistantAgent uses OpenAI LLM to generate a summary and returns it to UserAgent.

Integration steps

Install the autogen Python package and set your OPENAI_API_KEY environment variable.
Import the necessary classes from autogen and initialize your LLM client.
Define UserAgent and AssistantAgent with roles and behaviors.
Create a MultiAgent instance to manage agent interactions.
Send messages from UserAgent and receive responses from AssistantAgent.
Run the MultiAgent loop to process the conversation and get the final output.

Full code

python

from autogen import AssistantAgent, UserAgent, MultiAgent, OpenAI
import os

# Initialize OpenAI LLM client with API key from environment
llm = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Define a user agent that sends a question
user = UserAgent(name="User", llm=llm)

# Define an assistant agent that answers
assistant = AssistantAgent(name="Assistant", llm=llm)

# Create a multi-agent system
multi_agent = MultiAgent(agents=[user, assistant])

# User sends a question
user.send_message("What is AutoGen in Python?")

# Run the multi-agent conversation loop
multi_agent.run()

# Print the assistant's response
print("Assistant reply:", assistant.last_message)

output

Assistant reply: AutoGen is a Python library that enables you to build AI agents which collaborate and automate tasks by orchestrating large language models.

API trace

Request

json

{"model": "gpt-4o", "messages": [{"role": "user", "content": "What is AutoGen in Python?"}]}

Response

json

{"choices": [{"message": {"content": "AutoGen is a Python library that enables..."}}], "usage": {"total_tokens": 50}}

Extractresponse.choices[0].message.content

Variants

Streaming MultiAgent Conversation ›

Use streaming mode to get partial responses in real-time for better user experience.

python

from autogen import AssistantAgent, UserAgent, MultiAgent, OpenAI
import os

llm = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
user = UserAgent(name="User", llm=llm)
assistant = AssistantAgent(name="Assistant", llm=llm)
multi_agent = MultiAgent(agents=[user, assistant], streaming=True)
user.send_message("Explain AutoGen streaming usage.")
multi_agent.run()
print("Assistant reply:", assistant.last_message)

Async AutoGen Agent Interaction ›

Use async mode for concurrent agent calls or integrating AutoGen in async applications.

python

import asyncio
from autogen import AssistantAgent, UserAgent, MultiAgent, OpenAI
import os

async def main():
    llm = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    user = UserAgent(name="User", llm=llm)
    assistant = AssistantAgent(name="Assistant", llm=llm)
    multi_agent = MultiAgent(agents=[user, assistant])
    user.send_message("How to use AutoGen asynchronously?")
    await multi_agent.run_async()
    print("Assistant reply:", assistant.last_message)

asyncio.run(main())

Using Claude 3.5 Sonnet Model with AutoGen ›

Use Anthropic Claude 3.5 Haiku for better coding and reasoning tasks with AutoGen.

python

from autogen import AssistantAgent, UserAgent, MultiAgent, Anthropic
import os

llm = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"], model="claude-3-5-haiku-20241022")
user = UserAgent(name="User", llm=llm)
assistant = AssistantAgent(name="Assistant", llm=llm)
multi_agent = MultiAgent(agents=[user, assistant])
user.send_message("What is AutoGen?")
multi_agent.run()
print("Assistant reply:", assistant.last_message)

Performance

Latency~800ms per agent call with gpt-4o non-streaming

Cost~$0.002 per 500 tokens exchanged

Rate limitsTier 1: 500 requests per minute / 30,000 tokens per minute

Keep prompts concise to reduce token usage.
Use streaming mode to start processing partial results early.
Cache repeated queries to avoid redundant calls.

Approach	Latency	Cost/call	Best for
Standard MultiAgent	~800ms	~$0.002	Simple synchronous agent workflows
Streaming MultiAgent	~600ms initial + streaming	~$0.002	Real-time user interaction
Async MultiAgent	~800ms concurrent	~$0.002	Concurrent or high-throughput apps

✓

Quick tip

Always set your API keys in environment variables and initialize your LLM client before creating agents in AutoGen.

⚠

Common mistake

Beginners often forget to run the MultiAgent loop, so agents never exchange messages or produce output.

Verified 2026-04 · gpt-4o, claude-3-5-haiku-20241022

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